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Semenov S, Ismail MS, Sihag S, Manoharan T, Reilly P, Boran G, Ryan B, Breslin N, O’Connor A, O’Donnell S, McNamara D. Colon capsule endoscopy is an effective filter test for colonic polyp surveillance. World J Gastrointest Endosc 2025; 17:101322. [DOI: 10.4253/wjge.v17.i5.101322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/10/2024] [Revised: 02/26/2025] [Accepted: 04/11/2025] [Indexed: 05/12/2025] Open
Abstract
BACKGROUND Surveillance colonoscopies are predominantly normal, identifying patients for potential polypectomy is advantageous.
AIM To assess colon capsule endoscopy (CCE) and/or faecal immunochemical test (FIT) as filters in surveillance.
METHODS Patients aged ≥ 18 due for polyp surveillance were invited for CCE and FIT. Identifying polyps or colorectal cancer resulted in a positive CCE. Significant lesions (≥ 3 polyps or ≥ 6 mm polyps), incomplete studies and positive FITs (≥ 225 ng/mL) were referred for endoscopy. CCE and endoscopy results, FIT accuracy and patient preference were assessed.
RESULTS From a total of 126 CCEs [mean age 64 (31-80), 67 (53.2%) males), 70.6% (89/126) were excreted, 86.5% (109/126) had adequate image quality. CCE positivity was 70.6% (89/126), 42.9% (54/126) having significant polyps with 63.5% (80/126) referred for endoscopy (19 sigmoidoscopies, 61 colonoscopies). CCE reduced endoscopy need by 36.5% (46/126) and 51.6% (65/126) were spared a colonoscopy. CCE positive predictive value was 88.2% (45/51). Significant extracolonic findings were reported in 3.2% (4/126). Patients with positive CCEs were older > 65 [odds ratio (OR) = 2.5, 95% confidence interval (CI): 1.1517-5.5787, P = 0.0159], with personal history of polyps (OR = 2.3, 95%CI: 0.9734-5.4066, P = 0.045), with high/intermediate polyp surveillance risk (OR = 5.4, 95%CI: 1.1979-24.3824, P = 0.0156). Overall, 5/114 (4.4%) FITs were positive (range: 0-1394 ng/mL, mean: 54 ng/mL). Sensitivity (9.6%) and negative predictive values (20.3%) were inadequate. Receiver operating curve analysis gave a sensitivity and specificity of 26.9% and 91.7%, for FIT of 43 ng/mL. Patients preferred CCE 63.3% (76/120), with less impact on daily activities (21.7% vs 93.2%) and time off work (average days 0.9 vs 1.2, P = 0.0201).
CONCLUSION CCE appears effective in low-risk polyp surveillance. FIT does not appear to be of benefit in surveillance.
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Affiliation(s)
- Serhiy Semenov
- Trinity Academic Gastroenterology Group, School of Medicine - Trinity College Dublin, Dublin D2, Ireland
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
| | - Mohd Syafiq Ismail
- Trinity Academic Gastroenterology Group, School of Medicine - Trinity College Dublin, Dublin D2, Ireland
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
| | - Sandeep Sihag
- Trinity Academic Gastroenterology Group, School of Medicine - Trinity College Dublin, Dublin D2, Ireland
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
| | - Thilagaraj Manoharan
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
| | - Phyllis Reilly
- Department of Clinical Chemistry, Tallaght University Hospital, Dublin D24, Ireland
| | - Gerard Boran
- Department of Clinical Chemistry, Tallaght University Hospital, Dublin D24, Ireland
| | - Barbara Ryan
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
| | - Niall Breslin
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
| | - Anthony O’Connor
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
| | - Sarah O’Donnell
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
| | - Deirdre McNamara
- Trinity Academic Gastroenterology Group, School of Medicine - Trinity College Dublin, Dublin D2, Ireland
- Department of Gastroenterology, Tallaght University Hospital, Dublin D24, Ireland
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Mota J, Almeida MJ, Martins M, Mendes F, Cardoso P, Afonso J, Ribeiro T, Ferreira J, Fonseca F, Limbert M, Lopes S, Macedo G, Castro Poças F, Mascarenhas M. Artificial Intelligence in Coloproctology: A Review of Emerging Technologies and Clinical Applications. J Clin Med 2024; 13:5842. [PMID: 39407902 PMCID: PMC11477032 DOI: 10.3390/jcm13195842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2024] [Revised: 09/21/2024] [Accepted: 09/22/2024] [Indexed: 10/20/2024] Open
Abstract
Artificial intelligence (AI) has emerged as a transformative tool across several specialties, namely gastroenterology, where it has the potential to optimize both diagnosis and treatment as well as enhance patient care. Coloproctology, due to its highly prevalent pathologies and tremendous potential to cause significant mortality and morbidity, has drawn a lot of attention regarding AI applications. In fact, its application has yielded impressive outcomes in various domains, colonoscopy being one prominent example, where it aids in the detection of polyps and early signs of colorectal cancer with high accuracy and efficiency. With a less explored path but equivalent promise, AI-powered capsule endoscopy ensures accurate and time-efficient video readings, already detecting a wide spectrum of anomalies. High-resolution anoscopy is an area that has been growing in interest in recent years, with efforts being made to integrate AI. There are other areas, such as functional studies, that are currently in the early stages, but evidence is expected to emerge soon. According to the current state of research, AI is anticipated to empower gastroenterologists in the decision-making process, paving the way for a more precise approach to diagnosing and treating patients. This review aims to provide the state-of-the-art use of AI in coloproctology while also reflecting on future directions and perspectives.
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Affiliation(s)
- Joana Mota
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Maria João Almeida
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Miguel Martins
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Francisco Mendes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Pedro Cardoso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Afonso
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - Tiago Ribeiro
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
| | - João Ferreira
- Department of Mechanical Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal;
- DigestAID—Digestive Artificial Intelligence Development, Rua Alfredo Allen n.° 455/461, 4200-135 Porto, Portugal
| | - Filipa Fonseca
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
| | - Manuel Limbert
- Instituto Português de Oncologia de Lisboa Francisco Gentil (IPO Lisboa), 1099-023 Lisboa, Portugal; (F.F.); (M.L.)
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
| | - Susana Lopes
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Guilherme Macedo
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
| | - Fernando Castro Poças
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Department of Gastroenterology, Santo António University Hospital, 4099-001 Porto, Portugal
- Abel Salazar Biomedical Sciences Institute (ICBAS), 4050-313 Porto, Portugal
| | - Miguel Mascarenhas
- Precision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal; (J.M.); (M.J.A.); (M.M.); (F.M.); (P.C.); (J.A.); (T.R.); (S.L.); (G.M.)
- WGO Gastroenterology and Hepatology Training Center, 4200-047 Porto, Portugal
- Artificial Intelligence Group of the Portuguese Society of Coloproctology, 1050-117 Lisboa, Portugal;
- Faculty of Medicine, University of Porto, 4200-047 Porto, Portugal
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Sharif K, de Santiago ER, David P, Afek A, Gralnek IM, Ben-Horin S, Lahat A. Ecogastroenterology: cultivating sustainable clinical excellence in an environmentally conscious landscape. Lancet Gastroenterol Hepatol 2024; 9:550-563. [PMID: 38554732 DOI: 10.1016/s2468-1253(23)00414-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Revised: 11/24/2023] [Accepted: 11/24/2023] [Indexed: 04/02/2024]
Abstract
Gastrointestinal practices, especially endoscopy, have a substantial environmental impact, marked by notable greenhouse gas emissions and waste generation. As the world struggles with climate change, there emerges a pressing need to re-evaluate and reform the environmental footprint within gastrointestinal medicine. The challenge lies in finding a harmonious balance between ensuring clinical effectiveness and upholding environmental responsibility. This task involves recognising that the most significant reduction in the carbon footprint of endoscopy is achieved by avoiding unnecessary procedures; addressing the use of single-use endoscopes and accessories; and extending beyond the procedural suites to include clinics, virtual care, and conferences, among other aspects of gastrointestinal practice. The emerging digital realm in health care is crucial, given the potential environmental advantages of virtual gastroenterological care. Through an in-depth analysis, this review presents a path towards sustainable gastrointestinal practices, emphasising integrated strategies that prioritise both patient care and environmental stewardship.
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Affiliation(s)
- Kassem Sharif
- Department of Gastroenterology, Sheba Medical Centre, Ramat Gan, Israel; Department of Internal Medicine B, Sheba Medical Centre, Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
| | - Enrique Rodriguez de Santiago
- Gastroenterology and Hepatology, Hospital Universitario Ramón y Cajal, Universidad de Alcalá, IRYCIS, CIBERehd, ISCIII, Madrid, Spain
| | - Paula David
- Department of Internal Medicine B, Sheba Medical Centre, Ramat Gan, Israel
| | - Arnon Afek
- Department of Gastroenterology, Sheba Medical Centre, Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Ian M Gralnek
- Ellen and Pinchas Mamber Institute of Gastroenterology and Hepatology, Emek Medical Centre, Afula, Israel; Rappaport Faculty of Medicine Technion Israel Institute of Technology, Haifa, Israel
| | - Shomron Ben-Horin
- Department of Gastroenterology, Sheba Medical Centre, Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Adi Lahat
- Department of Gastroenterology, Sheba Medical Centre, Ramat Gan, Israel; Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
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De Lange G, Prouvost V, Rahmi G, Vanbiervliet G, Le Berre C, Mack S, Koessler T, Coron E. Artificial intelligence for characterization of colorectal polyps: Prospective multicenter study. Endosc Int Open 2024; 12:E413-E418. [PMID: 38504743 PMCID: PMC10948273 DOI: 10.1055/a-2261-2711] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Accepted: 02/01/2024] [Indexed: 03/21/2024] Open
Abstract
Background and study aims Optical diagnosis poses challenges to implementation of "resect and discard" strategies. This study aimed to assess the feasibility and performance of a new commercially available system for colorectal polyps. Patients and methods Nine expert endoscopists in three centers performed colonoscopies using artificial intelligence-equipped colonoscopes (CAD EYE, Fujifilm). Histology and predictions were compared, with hyperplastic polyps and sessile serrated lesions grouped for analysis. Results Overall, 253 polyps in 119 patients were documented (n=152 adenomas, n=78 hyperplastic polyps, n=23 sessile serrated lesions). CAD EYE detected polyps before endoscopists in 81 of 253 cases (32%). The mean polyp size was 5.5 mm (SD 0.6 mm). Polyp morphology was Paris Ip (4 %), Is (28 %), IIa (60 %), and IIb (8 %). CAD EYE achieved a sensitivity of 80%, specificity of 83%, positive predictive value (PPV) of 96%, and negative predictive value (NPV) of 72%. Expert endoscopists had a sensitivity of 88%, specificity of 83%, PPV of 96%, and NPV of 72%. Diagnostic accuracy was similar between CAD EYE (81%) and endoscopists (86%). However, sensitivity was greater with endoscopists as compared with CAD EYE ( P <0.05). CAD EYE classified sessile serrated lesions as hyperplasia in 22 of 23 cases, and endoscopists correctly classified 16 of 23 cases. Conclusions The CAD EYE system shows promise for detecting and characterizing colorectal polyps. Larger studies are needed, however, to confirm these findings.
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Affiliation(s)
- Glenn De Lange
- Faculty of Medicine, University of Geneva, Geneve, Switzerland
| | - Victor Prouvost
- IMAD, Centre Hospitalier Universitaire de Nantes, Nantes, France
| | - Gabriel Rahmi
- Hôpital Européen Georges Pompidou Hépato-gastro-entérologie et oncologie disgestive, Paris, France
| | | | | | - Sahar Mack
- Service de Gastroentérologie et d'hépatologie, Hôpitaux Universitaires Genève, Geneve, Switzerland
| | - Thibaud Koessler
- Service d'oncologie, Hôpitaux Universitaires Genève, Geneve, Switzerland
| | - Emmanuel Coron
- IMAD, Centre Hospitalier Universitaire de Nantes, Nantes, France
- Service de Gastroentérologie et d'hépatologie, Hôpitaux Universitaires Genève, Geneve, Switzerland
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Kang YW, Lee JH, Lee JY. The Utility of Narrow-Band Imaging International Colorectal Endoscopic Classification in Predicting the Histologies of Diminutive Colorectal Polyps Using I-Scan Optical Enhancement: A Prospective Study. Diagnostics (Basel) 2023; 13:2720. [PMID: 37627979 PMCID: PMC10453535 DOI: 10.3390/diagnostics13162720] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2023] [Revised: 08/13/2023] [Accepted: 08/19/2023] [Indexed: 08/27/2023] Open
Abstract
(1) Background: This study aimed to evaluate the accuracy of predicting the histology of diminutive colonic polyps (DCPs) (≤5 mm) using i-scan optical enhancement (OE) based on the narrow-band imaging international colorectal endoscopic (NICE) classification. The study compared the diagnostic accuracy between experts who were already familiar with the NICE classification and trainees who were not, both before and after receiving brief training on the NICE classification. (2) Method: This prospective, single-center clinical trial was conducted at the Dong-A University Hospital from March 2020 to August 2020 and involved two groups of participants. The first group comprised two experienced endoscopists who were proficient in using i-scan OE and had received formal training in optical diagnosis and dye-less chromoendoscopy (DLC) techniques. The second group consisted of three endoscopists in the process of training in internal medicine at the Dong-A University Hospital. Each endoscopist examined the polyps and evaluated them using the NICE classification through i-scan OE. The results were not among the participants. Trained endoscopists were divided into pre- and post-training groups. (3) Results: During the study, a total of 259 DCPs were assessed using i-scan OE by the two expert endoscopists. They made real-time histological predictions according to the NICE classification criteria. For the trainee group, before training, the area under the receiver operating characteristic curves (AUROCs) for predicting histopathological results using i-scan OE were 0.791, 0.775, and 0.818. However, after receiving training, the AUROCs improved to 0.935, 0.949, and 0.963, which were not significantly different from the results achieved by the expert endoscopists. (4) Conclusions: This study highlights the potential of i-scan OE, along with the NICE classification, in predicting the histopathological results of DCPs during colonoscopy. In addition, this study suggests that even an endoscopist without experience in DLC can effectively use i-scan OE to improve diagnostic performance with only brief training.
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Affiliation(s)
| | | | - Jong Yoon Lee
- Division of Gastroenterology, Department of Internal Medicine, Dong-A University College of Medicine, Busan 49201, Republic of Korea; (Y.W.K.); (J.H.L.)
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Li JW, Wu CCH, Lee JWJ, Liang R, Soon GST, Wang LM, Koh XH, Koh CJ, Chew WD, Lin KW, Thian MY, Matthew R, Kim G, Khor CJL, Fock KM, Ang TL, So JBY. Real-World Validation of a Computer-Aided Diagnosis System for Prediction of Polyp Histology in Colonoscopy: A Prospective Multicenter Study. Am J Gastroenterol 2023; 118:1353-1364. [PMID: 37040553 DOI: 10.14309/ajg.0000000000002282] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 03/28/2023] [Indexed: 04/13/2023]
Abstract
INTRODUCTION Computer-aided diagnosis (CADx) of polyp histology could support endoscopists in clinical decision-making. However, this has not been validated in a real-world setting. METHODS We performed a prospective, multicenter study comparing CADx and endoscopist predictions of polyp histology in real-time colonoscopy. Optical diagnosis based on visual inspection of polyps was made by experienced endoscopists. After this, the automated output from the CADx support tool was recorded. All imaged polyps were resected for histological assessment. Primary outcome was difference in diagnostic performance between CADx and endoscopist prediction of polyp histology. Subgroup analysis was performed for polyp size, bowel preparation, difficulty of location of the polyps, and endoscopist experience. RESULTS A total of 661 eligible polyps were resected in 320 patients aged ≥40 years between March 2021 and July 2022. CADx had an overall accuracy of 71.6% (95% confidence interval [CI] 68.0-75.0), compared with 75.2% (95% CI 71.7-78.4) for endoscopists ( P = 0.023). The sensitivity of CADx for neoplastic polyps was 61.8% (95% CI 56.9-66.5), compared with 70.3% (95% CI 65.7-74.7) for endoscopists ( P < 0.001). The interobserver agreement between CADx and endoscopist predictions of polyp histology was moderate (83.1% agreement, κ 0.661). When there was concordance between CADx and endoscopist predictions, the accuracy increased to 78.1%. DISCUSSION The overall diagnostic accuracy and sensitivity for neoplastic polyps was higher in experienced endoscopists compared with CADx predictions, with moderate interobserver agreement. Concordance in predictions increased this diagnostic accuracy. Further research is required to improve the performance of CADx and to establish its role in clinical practice.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Clement Chun Ho Wu
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore Health Services, Singapore
| | - Jonathan Wei Jie Lee
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, National University Health System, Singapore
- Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Institute of Health Innovation and Technology (iHealthtech), National University of Singapore, Singapore
| | - Raymond Liang
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Gwyneth Shook Ting Soon
- Department of Pathology, National University Hospital, National University Health System, Singapore
| | - Lai Mun Wang
- Department of Laboratory Medicine, Changi General Hospital, Singapore Health Services, Singapore
| | - Xuan Han Koh
- Department of Health Sciences Research, Changi General Hospital, Singapore
| | - Calvin Jianyi Koh
- Division of Gastroenterology and Hepatology, Department of Medicine, National University Hospital, National University Health System, Singapore
| | - Wei Da Chew
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Kenneth Weicong Lin
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Mann Yie Thian
- Department of Gastroenterology and Hepatology, Tan Tock Seng Hospital, National Healthcare Group, Singapore
| | - Ronnie Matthew
- Department of Colorectal Surgery, Singapore General Hospital, Singapore Health Services, Singapore
| | - Guowei Kim
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- University Surgical Cluster, National University Hospital, Singapore
| | - Christopher Jen Lock Khor
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
- Department of Gastroenterology and Hepatology, Singapore General Hospital, Singapore Health Services, Singapore
| | - Kwong Ming Fock
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore Health Services, Singapore
- Duke-NUS Academic Medicine Centre, Singapore Health Services, Singapore
| | - Jimmy Bok Yan So
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- University Surgical Cluster, National University Hospital, Singapore
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Yong KK, He Y, Cheung HCA, Sriskandarajah R, Jenkins W, Goldin R, Beg S. Rationalising the use of specimen pots following colorectal polypectomy: a small step towards greener endoscopy. Frontline Gastroenterol 2022; 14:295-299. [PMID: 37409340 PMCID: PMC11138171 DOI: 10.1136/flgastro-2022-102231] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2022] [Accepted: 10/21/2022] [Indexed: 11/09/2022] Open
Abstract
Aims In this study, we aim to determine whether combining multiple small colorectal polyps within a single specimen pot can reduce carbon footprint, without an associated deleterious clinical impact. Methods This was a retrospective observational study of colorectal polyps resected during 2019, within the Imperial College Healthcare Trust. The numbers of pots for polypectomy specimens were calculated and corresponding histology results were extracted. We modelled the potential reduction in carbon footprint if all less than 10 mm polyps were sent together and the number of advanced lesions we would not be able to locate if we adopted this strategy. Carbon footprint was estimated based on previous study using a life-cycle assessment, at 0.28 kgCO2e per pot. Results A total of 11 781 lower gastrointestinal endoscopies were performed. There were 5125 polyps removed and 4192 pots used, equating to a carbon footprint of 1174 kgCO2e. There were 4563 (89%) polyps measuring 0-10 mm. 6 (0.1%) of these polyps were cancers, while 12 (0.2%) demonstrated high-grade dysplasia. If we combined all small polyps in a single pot, total pot usage could be reduced by one-third (n=2779). Conclusion A change in practice by placing small polyps collectively in one pot would have resulted in reduction in carbon footprint equivalent to 396 kgCO2e (emissions from 982 miles driven by an average passenger car). The reduction in carbon footprint from judicious use of specimen pots would be amplified with a change in practice on a national level.
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Affiliation(s)
- Karl King Yong
- Gastroenterology, Imperial College Healthcare NHS Trust, London, UK
| | - Yun He
- School of Medicine, Imperial College School of Medicine, London, UK
| | | | | | - William Jenkins
- School of Medicine, Imperial College School of Medicine, London, UK
| | - Robert Goldin
- Division of Digestive Diseases, Imperial College School of Medicine, London, UK
| | - Sabina Beg
- Gastroenterology, Imperial College Healthcare NHS Trust, London, UK
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Gong EJ, Bang CS, Lee JJ, Yang YJ, Baik GH. Impact of the Volume and Distribution of Training Datasets in the Development of Deep-Learning Models for the Diagnosis of Colorectal Polyps in Endoscopy Images. J Pers Med 2022; 12:1361. [PMID: 36143146 PMCID: PMC9505038 DOI: 10.3390/jpm12091361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2022] [Revised: 08/13/2022] [Accepted: 08/19/2022] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Establishment of an artificial intelligence model in gastrointestinal endoscopy has no standardized dataset. The optimal volume or class distribution of training datasets has not been evaluated. An artificial intelligence model was previously created by the authors to classify endoscopic images of colorectal polyps into four categories, including advanced colorectal cancer, early cancers/high-grade dysplasia, tubular adenoma, and nonneoplasm. The aim of this study was to evaluate the impact of the volume and distribution of training dataset classes in the development of deep-learning models for colorectal polyp histopathology prediction from endoscopic images. METHODS The same 3828 endoscopic images that were used to create earlier models were used. An additional 6838 images were used to find the optimal volume and class distribution for a deep-learning model. Various amounts of data volume and class distributions were tried to establish deep-learning models. The training of deep-learning models uniformly used no-code platform Neuro-T. Accuracy was the primary outcome on four-class prediction. RESULTS The highest internal-test classification accuracy in the original dataset, doubled dataset, and tripled dataset was commonly shown by doubling the proportion of data for fewer categories (2:2:1:1 for advanced colorectal cancer: early cancers/high-grade dysplasia: tubular adenoma: non-neoplasm). Doubling the proportion of data for fewer categories in the original dataset showed the highest accuracy (86.4%, 95% confidence interval: 85.0-97.8%) compared to that of the doubled or tripled dataset. The total required number of images in this performance was only 2418 images. Gradient-weighted class activation mapping confirmed that the part that the deep-learning model pays attention to coincides with the part that the endoscopist pays attention to. CONCLUSION As a result of a data-volume-dependent performance plateau in the classification model of colonoscopy, a dataset that has been doubled or tripled is not always beneficial to training. Deep-learning models would be more accurate if the proportion of fewer category lesions was increased.
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Affiliation(s)
- Eun Jeong Gong
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Korea
| | - Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Korea
- Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea
| | - Young Joo Yang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea
| | - Gwang Ho Baik
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea
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9
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Gong EJ, Bang CS, Lee JJ, Seo SI, Yang YJ, Baik GH, Kim JW. No-Code Platform-Based Deep-Learning Models for Prediction of Colorectal Polyp Histology from White-Light Endoscopy Images: Development and Performance Verification. J Pers Med 2022; 12:963. [PMID: 35743748 PMCID: PMC9225479 DOI: 10.3390/jpm12060963] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 05/27/2022] [Accepted: 06/10/2022] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND The authors previously developed deep-learning models for the prediction of colorectal polyp histology (advanced colorectal cancer, early cancer/high-grade dysplasia, tubular adenoma with or without low-grade dysplasia, or non-neoplasm) from endoscopic images. While the model achieved 67.3% internal-test accuracy and 79.2% external-test accuracy, model development was labour-intensive and required specialised programming expertise. Moreover, the 240-image external-test dataset included only three advanced and eight early cancers, so it was difficult to generalise model performance. These limitations may be mitigated by deep-learning models developed using no-code platforms. OBJECTIVE To establish no-code platform-based deep-learning models for the prediction of colorectal polyp histology from white-light endoscopy images and compare their diagnostic performance with traditional models. METHODS The same 3828 endoscopic images used to establish previous models were used to establish new models based on no-code platforms Neuro-T, VLAD, and Create ML-Image Classifier. A prospective multicentre validation study was then conducted using 3818 novel images. The primary outcome was the accuracy of four-category prediction. RESULTS The model established using Neuro-T achieved the highest internal-test accuracy (75.3%, 95% confidence interval: 71.0-79.6%) and external-test accuracy (80.2%, 76.9-83.5%) but required the longest training time. In contrast, the model established using Create ML-Image Classifier required only 3 min for training and still achieved 72.7% (70.8-74.6%) external-test accuracy. Attention map analysis revealed that the imaging features used by the no-code deep-learning models were similar to those used by endoscopists during visual inspection. CONCLUSION No-code deep-learning tools allow for the rapid development of models with high accuracy for predicting colorectal polyp histology.
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Affiliation(s)
- Eun Jeong Gong
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea; (E.J.G.); (S.I.S.); (Y.J.Y.); (G.H.B.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Korea
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Korea;
| | - Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea; (E.J.G.); (S.I.S.); (Y.J.Y.); (G.H.B.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Korea
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Korea;
- Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon 24253, Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon 24253, Korea;
- Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon 24253, Korea
- Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea
| | - Seung In Seo
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea; (E.J.G.); (S.I.S.); (Y.J.Y.); (G.H.B.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Korea
| | - Young Joo Yang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea; (E.J.G.); (S.I.S.); (Y.J.Y.); (G.H.B.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Korea
| | - Gwang Ho Baik
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon 24253, Korea; (E.J.G.); (S.I.S.); (Y.J.Y.); (G.H.B.)
- Institute for Liver and Digestive Diseases, Hallym University, Chuncheon 24253, Korea
| | - Jong Wook Kim
- Department of Internal Medicine, Inje University Ilsan Paik Hospital, Goyang 10556, Korea;
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10
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Taghiakbari M, Hammar C, Frenn M, Djinbachian R, Pohl H, Deslandres E, Bouchard S, Bouin M, von Renteln D. Non-optical polyp-based resect and discard strategy: A prospective clinical study. World J Gastroenterol 2022; 28:2137-2147. [PMID: 35664039 PMCID: PMC9134134 DOI: 10.3748/wjg.v28.i19.2137] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/02/2022] [Revised: 03/21/2022] [Accepted: 04/09/2022] [Indexed: 02/06/2023] Open
Abstract
BACKGROUND Post-polypectomy surveillance intervals are currently determined based on pathology results. AIM To evaluate a polyp-based resect and discard model that assigns surveillance intervals based solely on polyp number and size. METHODS Patients undergoing elective colonoscopies at the Montreal University Medical Center were enrolled prospectively. The polyp-based strategy was used to assign the next surveillance interval using polyp size and number. Surveillance intervals were also assigned using optical diagnosis for small polyps (< 10 mm). The primary outcome was surveillance interval agreement between the polyp-based model, optical diagnosis, and the pathology-based reference standard using the 2020 United States Multi-Society Task Force guidelines. Secondary outcomes included the proportion of reduction in required histopathology evaluations and proportion of immediate post-colonoscopy recommendations provided to patients. RESULTS Of 944 patients (mean age 62.6 years, 49.3% male, 933 polyps) were enrolled. The surveillance interval agreement for the polyp-based strategy was 98.0% [95% confidence interval (CI): 0.97-0.99] compared with pathology-based assignment. Optical diagnosis-based intervals achieved 95.8% (95%CI: 0.94-0.97) agreement with pathology. When using the polyp-based strategy and optical diagnosis, the need for pathology assessment was reduced by 87.8% and 70.6%, respectively. The polyp-based strategy provided 93.7% of patients with immediate surveillance interval recommendations vs 76.1% for optical diagnosis. CONCLUSION The polyp-based strategy achieved almost perfect surveillance interval agreement compared with pathology-based assignments, significantly reduced the number of required pathology evaluations, and provided most patients with immediate surveillance interval recommendations.
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Affiliation(s)
- Mahsa Taghiakbari
- Department of Gastroenterology, Montreal University Hospital Research Center (CRCHUM), Montréal H2X 0A9, Quebec, Canada
| | - Celia Hammar
- Department of Gastroenterology, Montreal University Hospital Research Center (CRCHUM), Montréal H2X 0A9, Quebec, Canada
- Department of Gastroenterology, University of Montreal, Faculty of Medicine, Montreal H2X 0A9, Quebce, Canada
| | - Mira Frenn
- Department of Gastroenterology, Montreal University Hospital Research Center (CRCHUM), Montréal H2X 0A9, Quebec, Canada
- Department of Gastroenterology, University of Montreal, Faculty of Medicine, Montreal H2X 0A9, Quebce, Canada
| | - Roupen Djinbachian
- Department of Gastroenterology, Montreal University Hospital Research Center (CRCHUM), Montréal H2X 0A9, Quebec, Canada
- Department of Internal Medicine, University of Montreal Hospital Center (CHUM), Montreal H2X 0A9, Quebec, Canada
| | - Heiko Pohl
- Department of Medicine, Veterans Affairs Medical Center, White River Junction, VT 05009, United States
- Department of Gastroenterology, Dartmouth Geisel School of Medicine and The Dartmouth Institute, Hanover, NH 03755, United States
| | - Erik Deslandres
- Department of Gastroenterology, Montreal University Hospital Research Center (CRCHUM), Montréal H2X 0A9, Quebec, Canada
| | - Simon Bouchard
- Department of Gastroenterology, Montreal University Hospital Research Center (CRCHUM), Montréal H2X 0A9, Quebec, Canada
| | - Mickael Bouin
- Department of Gastroenterology, Montreal University Hospital Research Center (CRCHUM), Montréal H2X 0A9, Quebec, Canada
| | - Daniel von Renteln
- Department of Gastroenterology, Montreal University Hospital Research Center (CRCHUM), Montréal H2X 0A9, Quebec, Canada
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11
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Li JW, Wang LM, Ang TL. Artificial intelligence-assisted colonoscopy: a narrative review of current data and clinical applications. Singapore Med J 2022; 63:118-124. [PMID: 35509251 PMCID: PMC9251247 DOI: 10.11622/smedj.2022044] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/22/2023]
Abstract
Colonoscopy is the reference standard procedure for the prevention and diagnosis of colorectal cancer, which is a leading cause of cancer-related deaths in Singapore. Artificial intelligence systems are automated, objective and reproducible. Artificial intelligence-assisted colonoscopy has recently been introduced into clinical practice as a clinical decision support tool. This review article provides a summary of the current published data and discusses ongoing research and current clinical applications of artificial intelligence-assisted colonoscopy.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore
| | - Lai Mun Wang
- Pathology Section, Department of Laboratory Medicine, Changi General Hospital, Singapore
- SingHealth Duke-NUS Pathology Academic Clinical Programme, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- SingHealth Duke-NUS Medicine Academic Clinical Programme, Singapore
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12
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Kandel P, Wallace MB. Advanced Imaging Techniques and In vivo Histology: Current Status and Future Perspectives (Lower G.I.). GASTROINTESTINAL AND PANCREATICO-BILIARY DISEASES: ADVANCED DIAGNOSTIC AND THERAPEUTIC ENDOSCOPY 2022:291-310. [DOI: 10.1007/978-3-030-56993-8_110] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/03/2025]
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13
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Koehn C, Rex DK, Allen J, Bhatti U, Bhavsar-Burke I, Thoguluva Chandrasekar V, Challa A, Duvvuri A, Dakhoul L, Ha J, Hamade N, Hicks SB, Jansson-Knodell C, Krajicek E, Das Kundumadam S, Nutalapati V, Phatharacharukul PP, Razmdjou S, Saito A, Sarkis F, Sutton R, Wehbeh A, Sharma P, Desai M. Optical diagnosis of colorectal polyps using novel blue light imaging classification among trainee endoscopists. Dig Endosc 2022; 34:191-197. [PMID: 34053136 DOI: 10.1111/den.14050] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 05/22/2021] [Accepted: 05/26/2021] [Indexed: 12/23/2022]
Abstract
BACKGROUND Blue light imaging (BLI) has been shown to improve the characterization of colorectal polyps among the endoscopy experts. We aimed to determine if this technology could be taught to endoscopy trainees while maintaining high accuracy and interobserver agreement. METHODS Twenty-one gastroenterology trainees (fellows) from two academic institutions participated in this prospective study. Each trainee completed a web-based learning comprising four modules: pre-test, didactic videos explaining the BLI Adenoma Serrated International Classification (BASIC), interactive examples, and post-test assessment. The pre- and post-test modules consisted of reviewing video images of colon polyps in high definition white light imaging and BLI and then applying the BASIC classification to determine if the polyps were likely to be adenomatous. Confidence in adenoma identification (rated '1' to '5'), accuracy in polyp (adenoma vs. non-adenoma) identification, and agreement in characterization per BASIC criteria were derived. RESULTS Trainee accuracy in the adenoma diagnosis improved from 74.7% (pre-test) to 85.4% (post-test) (P < 0.01). There was a trend towards higher accuracy in polyp characterization with subsequent years of training (1st year fellows 77.4%, 2nd year 88.5%, and final year 94.0%) with consistent improvements after the e-learning across years of trainees. Overall, trainees were able to identify adenoma with a high sensitivity of 86.9%, specificity 83.9%, positive predictive value of 84.4%, and negative predictive value of 86.5%. However, their interobserver agreement in adenoma diagnosis was moderate (k = 0.52). CONCLUSION The novel BLI classification can be easily taught to gastroenterology trainees using an online module and accuracy improves with years of training reaching >90% for colorectal polyp characterization.
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Affiliation(s)
- Christopher Koehn
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Douglas K Rex
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Jimmy Allen
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Umer Bhatti
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Indira Bhavsar-Burke
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | | | - Abhishek Challa
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Abhiram Duvvuri
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Lara Dakhoul
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - John Ha
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Nour Hamade
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - S Bradley Hicks
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Claire Jansson-Knodell
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Edward Krajicek
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Shanker Das Kundumadam
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Venkat Nutalapati
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA
| | | | - Shadi Razmdjou
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Akira Saito
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Fayez Sarkis
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Richard Sutton
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA
| | - Antonios Wehbeh
- Division of Gastroenterology and Hepatology, Indiana University Medical Center, Indianapolis, IN, USA
| | - Prateek Sharma
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA.,Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, MO, USA
| | - Madhav Desai
- Division of Gastroenterology, Hepatology and Motility, University of Kansas School of Medicine, Kansas City, KS, USA.,Department of Gastroenterology, Kansas City VA Medical Center, Kansas City, MO, USA
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14
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Rutter CM, Nascimento de Lima P, Lee JK, Ozik J. Too Good to Be True? Evaluation of Colonoscopy Sensitivity Assumptions Used in Policy Models. Cancer Epidemiol Biomarkers Prev 2021; 31:775-782. [PMID: 34906968 DOI: 10.1158/1055-9965.epi-21-1001] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2021] [Revised: 10/13/2021] [Accepted: 11/23/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Models can help guide colorectal cancer (CRC) screening policy. While models are carefully calibrated and validated, there is less scrutiny of assumptions about test performance. METHODS We examined the validity of the CRC-SPIN model and colonoscopy sensitivity assumptions. Standard sensitivity assumptions, consistent with published decision analyses, assume sensitivity equal to 0.75 for diminutive adenomas (<6mm), 0.85 for small adenomas (6-10mm), 0.95 for large adenomas ( {greater than or equal to} 10mm), and 0.95 for preclinical cancer. We also selected adenoma sensitivity that resulted in more accurate predictions. Targets were drawn from the Wheat Bran Fiber study. We examined how well the model predicted outcomes measured over a three-year follow-up period, including: the number of adenomas detected, the size of the largest adenoma detected, and incident CRC. RESULTS Using standard sensitivity assumptions, the model predicted adenoma prevalence that was too low (42.5% versus 48.9% observed, with 95% confidence interval 45.3%-50.7%) and detection of too few large adenomas (5.1% versus 14.% observed, with 95% confidence interval 11.8%-17.4%). Predictions were close to targets when we set sensitivities to 0.20 for diminutive adenomas, 0.60 for small adenomas, 0.80 for 10-20mm adenomas, and 0.98 for adenomas 20mm and larger. CONCLUSIONS Colonoscopy may be less accurate than currently assumed, especially for diminutive adenomas. Alternatively, the CRC-SPIN model may not accurately simulate onset and progression of adenomas in higher-risk populations. IMPACT Misspecification of either colonoscopy sensitivity or disease progression in high-risk populations may impact the predicted effectiveness of CRC screening. When possible, decision analyses used to inform policy should address these uncertainties.
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Affiliation(s)
| | | | - Jeffrey K Lee
- Division of Research, Kaiser Permanente Northern California
| | - Jonathan Ozik
- Decision and Infrastructure Sciences Division, Argonne National Laboratory
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15
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Fonollà R, van der Zander QEW, Schreuder RM, Subramaniam S, Bhandari P, Masclee AAM, Schoon EJ, van der Sommen F, de With PHN. Automatic image and text-based description for colorectal polyps using BASIC classification. Artif Intell Med 2021; 121:102178. [PMID: 34763800 DOI: 10.1016/j.artmed.2021.102178] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 09/01/2021] [Accepted: 09/21/2021] [Indexed: 12/18/2022]
Abstract
Colorectal polyps (CRP) are precursor lesions of colorectal cancer (CRC). Correct identification of CRPs during in-vivo colonoscopy is supported by the endoscopist's expertise and medical classification models. A recent developed classification model is the Blue light imaging Adenoma Serrated International Classification (BASIC) which describes the differences between non-neoplastic and neoplastic lesions acquired with blue light imaging (BLI). Computer-aided detection (CADe) and diagnosis (CADx) systems are efficient at visually assisting with medical decisions but fall short at translating decisions into relevant clinical information. The communication between machine and medical expert is of crucial importance to improve diagnosis of CRP during in-vivo procedures. In this work, the combination of a polyp image classification model and a language model is proposed to develop a CADx system that automatically generates text comparable to the human language employed by endoscopists. The developed system generates equivalent sentences as the human-reference and describes CRP images acquired with white light (WL), blue light imaging (BLI) and linked color imaging (LCI). An image feature encoder and a BERT module are employed to build the AI model and an external test set is used to evaluate the results and compute the linguistic metrics. The experimental results show the construction of complete sentences with an established metric scores of BLEU-1 = 0.67, ROUGE-L = 0.83 and METEOR = 0.50. The developed CADx system for automatic CRP image captioning facilitates future advances towards automatic reporting and may help reduce time-consuming histology assessment.
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Affiliation(s)
- Roger Fonollà
- Department of Electrical Engineering, Video Coding and Architectures (VCA), Eindhoven University of Technology, Eindhoven, Noord-Brabant, the Netherlands.
| | - Quirine E W van der Zander
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, the Netherlands; GROW, School for Oncology and Developmental Biology, Maastricht University, Maastricht, the Netherlands
| | - Ramon M Schreuder
- Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, Noord-Brabant, the Netherlands
| | - Sharmila Subramaniam
- Department of Gastroenterology, Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Pradeep Bhandari
- Department of Gastroenterology, Portsmouth Hospitals University NHS Trust, Portsmouth, United Kingdom
| | - Ad A M Masclee
- Division of Gastroenterology and Hepatology, Maastricht University Medical Center, Maastricht, the Netherlands; NUTRIM, School of Nutrition & Translational Research in Metabolism, Maastricht University, Maastricht, the Netherlands
| | - Erik J Schoon
- Department of Gastroenterology and Hepatology, Catharina Hospital, Eindhoven, Noord-Brabant, the Netherlands
| | - Fons van der Sommen
- Department of Electrical Engineering, Video Coding and Architectures (VCA), Eindhoven University of Technology, Eindhoven, Noord-Brabant, the Netherlands
| | - Peter H N de With
- Department of Electrical Engineering, Video Coding and Architectures (VCA), Eindhoven University of Technology, Eindhoven, Noord-Brabant, the Netherlands
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16
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Kim GH, Kwon KA, Park DH, Han J. Editors' Choice of Noteworthy Clinical Endoscopy Publications in the First Decade. Clin Endosc 2021; 54:633-640. [PMID: 34510862 PMCID: PMC8505185 DOI: 10.5946/ce.2021.216] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Revised: 08/26/2021] [Accepted: 08/30/2021] [Indexed: 11/17/2022] Open
Abstract
This is a special review to celebrate the 10th anniversary of Clinical Endoscopy. Each deputy editor has selected articles from one's subspecialty that are significant in terms of the number of downloads, citations, and clinical importance. The articles included original articles, review articles, systematic reviews, and meta-analyses.
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Affiliation(s)
- Gwang Ha Kim
- Department of Internal Medicine, Pusan National University School of Medicine and Biomedical Research Institute, Pusan National University Hospital, Busan, Korea
| | - Kwang An Kwon
- Department of Gastroenterology, Gachon University Gil Hospital, Incheon, Korea
| | - Do Hyun Park
- Division of Gastroenterology, Department of Internal Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
| | - Jimin Han
- Division of Gastroenterology, Department of Internal Medicine, Daegu Catholic University School of Medicine, Daegu, Korea
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17
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Bang CS, Lee JJ, Baik GH. Computer-Aided Diagnosis of Diminutive Colorectal Polyps in Endoscopic Images: Systematic Review and Meta-analysis of Diagnostic Test Accuracy. J Med Internet Res 2021; 23:e29682. [PMID: 34432643 PMCID: PMC8427459 DOI: 10.2196/29682] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 07/04/2021] [Accepted: 07/27/2021] [Indexed: 12/19/2022] Open
Abstract
Background Most colorectal polyps are diminutive and benign, especially those in the rectosigmoid colon, and the resection of these polyps is not cost-effective. Advancements in image-enhanced endoscopy have improved the optical prediction of colorectal polyp histology. However, subjective interpretability and inter- and intraobserver variability prohibits widespread implementation. The number of studies on computer-aided diagnosis (CAD) is increasing; however, their small sample sizes limit statistical significance. Objective This review aims to evaluate the diagnostic test accuracy of CAD models in predicting the histology of diminutive colorectal polyps by using endoscopic images. Methods Core databases were searched for studies that were based on endoscopic imaging, used CAD models for the histologic diagnosis of diminutive colorectal polyps, and presented data on diagnostic performance. A systematic review and diagnostic test accuracy meta-analysis were performed. Results Overall, 13 studies were included. The pooled area under the curve, sensitivity, specificity, and diagnostic odds ratio of CAD models for the diagnosis of diminutive colorectal polyps (adenomatous or neoplastic vs nonadenomatous or nonneoplastic) were 0.96 (95% CI 0.93-0.97), 0.93 (95% CI 0.91-0.95), 0.87 (95% CI 0.76-0.93), and 87 (95% CI 38-201), respectively. The meta-regression analysis showed no heterogeneity, and no publication bias was detected. Subgroup analyses showed robust results. The negative predictive value of CAD models for the diagnosis of adenomatous polyps in the rectosigmoid colon was 0.96 (95% CI 0.95-0.97), and this value exceeded the threshold of the diagnosis and leave strategy. Conclusions CAD models show potential for the optical histological diagnosis of diminutive colorectal polyps via the use of endoscopic images. Trial Registration PROSPERO CRD42021232189; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=232189
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Affiliation(s)
- Chang Seok Bang
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea
| | - Jae Jun Lee
- Institute of New Frontier Research, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Division of Big Data and Artificial Intelligence, Chuncheon Sacred Heart Hospital, Chuncheon, Republic of Korea.,Department of Anesthesiology and Pain Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea
| | - Gwang Ho Baik
- Department of Internal Medicine, Hallym University College of Medicine, Chuncheon, Republic of Korea.,Institute for Liver and Digestive Diseases, Hallym University College of Medicine, Chuncheon, Republic of Korea
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18
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Kim N, Gim JA, Lee BJ, Choi BI, Park SB, Yoon HS, Kang SH, Kim SH, Joo MK, Park JJ, Kim C, Kim HK. RNA-sequencing identification and validation of genes differentially expressed in high-risk adenoma, advanced colorectal cancer, and normal controls. Funct Integr Genomics 2021; 21:513-521. [PMID: 34273035 DOI: 10.1007/s10142-021-00795-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Revised: 06/14/2021] [Accepted: 06/22/2021] [Indexed: 12/18/2022]
Abstract
Distinct gene expression patterns that occur during the adenoma-carcinoma sequence need to be determined to analyze the underlying mechanism in each step of colorectal cancer progression. Elucidation of biomarkers for colorectal polyps that harbor malignancy potential is important for prevention of colorectal cancer. Here, we use RNA sequencing to determine gene expression profile in patients with high-risk adenoma treated with endoscopic submucosal dissection by comparing with gene expression in patients with advanced colorectal cancer and normal controls. We collected 70 samples, which consisted of 27 colorectal polyps, 24 cancer tissues, and 19 normal colorectal mucosa. RNA sequencing was performed on an Illumina platform to select differentially expressed genes (DEGs) between colorectal polyps and cancer, polyps and controls, and cancer and normal controls. The Kyoto Gene and Genome Encyclopedia (KEGG) and gene ontology (GO) analysis, gene-concept network, GSEA, and a decision tree were used to evaluate the DEGs. We selected the most highly expressed genes in high-risk polyps and validated their expression using real-time PCR and immunohistochemistry. Compared to patients with colorectal cancer, 82 upregulated and 24 downregulated genes were detected in high-risk adenoma. In comparison with normal controls, 33 upregulated and 79 downregulated genes were found in high-risk adenoma. In total, six genes were retrieved as the highest and second highest expressed in advanced polyps and cancers among the three groups. Among the six genes, ANAX3 and CD44 expression in real-time PCR for validation was in good accordance with RNA sequencing. We identified differential expression of mRNAs among high-risk adenoma, advanced colorectal cancer, and normal controls, including that of CD44 and ANXA3, suggesting that this cluster of genes as a marker of high-risk colorectal adenoma.
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Affiliation(s)
- Namjoo Kim
- Department of Gastroenterology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Jeong-An Gim
- Medical Science Research Center, College of Medicine, Korea University Guro Hospital Seoul, Seoul, Republic of Korea
| | - Beom Jae Lee
- Department of Gastroenterology, Korea University Guro Hospital, Seoul, Republic of Korea.
| | - Byung Il Choi
- Department of Gastroenterology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Seung Bin Park
- Department of Gastroenterology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Hee Sook Yoon
- Department of Gastroenterology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Sang Hee Kang
- Department of Surgery, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Seung Han Kim
- Department of Gastroenterology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Moon Kyung Joo
- Department of Gastroenterology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Jong-Jae Park
- Department of Gastroenterology, Korea University Guro Hospital, Seoul, Republic of Korea
| | - Chungyeul Kim
- Department of Pathology, College of Medicine, Korea University, Seoul, Republic of Korea
| | - Han-Kyeom Kim
- Department of Pathology, College of Medicine, Korea University, Seoul, Republic of Korea
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19
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Kaalby L, Deding U, Kobaek-Larsen M, Havshoi ALV, Zimmermann-Nielsen E, Thygesen MK, Kroeijer R, Bjørsum-Meyer T, Baatrup G. Colon capsule endoscopy in colorectal cancer screening: a randomised controlled trial. BMJ Open Gastroenterol 2021; 7:bmjgast-2020-000411. [PMID: 32601101 PMCID: PMC7326244 DOI: 10.1136/bmjgast-2020-000411] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Revised: 05/07/2020] [Accepted: 05/31/2020] [Indexed: 02/06/2023] Open
Abstract
Introduction The use of capsule endoscopy has become an approved method in small bowel diagnostics, but the same level of integration is not seen in large bowel diagnostics. We will use colon capsule endoscopy (CCE) as a filter test in colorectal cancer (CRC) screening between the faecal immunochemical test (FIT) and colonoscopy. We aim to investigate the clinical performance, population acceptability, and economic implications of the procedure in a large-scale clinical trial. Methods and analysis We will randomly allocate 124 214 Danish citizens eligible for participation in the national CRC screening programme within the Region of Southern Denmark to either an intervention group or a control group. Prior to submitting a FIT, citizens randomised to the intervention group will be informed about their opportunity to undergo CCE, instead of colonoscopy, if the FIT is positive. Suspected cancers; >3 adenomas <10 mm in size, 1 adenoma >10 mm in size or >4 adenomas regardless of size, detected during CCE will generate an invitation to colonoscopy as per regular screening guidelines, whereas citizens with suspected low risk polyps will re-enter the biennial screening programme. Citizens with no CCE findings will be excluded from screening for 8 years. In the control group, citizens will follow standard screening procedures. Ethics and dissemination All participants must consent prior to capsule ingestion. All collected data will be handled and stored in accordance with current data protection legislation. Approvals from the regional ethics committee (ref. S-20190100) and the Danish data protection agency have been obtained (ref. 19/29858). Trial registration details The study has been registered with ClinicalTrials.gov under: NCT04049357.
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Affiliation(s)
- Lasse Kaalby
- Department of Surgery, Odense University Hospital, Svendborg, Denmark .,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ulrik Deding
- Department of Surgery, Odense University Hospital, Svendborg, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Morten Kobaek-Larsen
- Department of Surgery, Odense University Hospital, Svendborg, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | - Erik Zimmermann-Nielsen
- Department of Surgery, Odense University Hospital, Svendborg, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Marianne Kirstine Thygesen
- Department of Surgery, Odense University Hospital, Svendborg, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Rasmus Kroeijer
- Department of Surgery, Southwest Jutland Hospital Esbjerg, Esbjerg, Denmark
| | | | - Gunnar Baatrup
- Department of Surgery, Odense University Hospital, Svendborg, Denmark.,Department of Clinical Research, University of Southern Denmark, Odense, Denmark
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20
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Alaoui AA, Oumedjbeur K, Djinbachian R, Marchand É, Marques PN, Bouin M, Bouchard S, von Renteln D. Clinical validation of the SIMPLE classification for optical diagnosis of colorectal polyps. Endosc Int Open 2021; 9:E684-E692. [PMID: 33937508 PMCID: PMC8062223 DOI: 10.1055/a-1388-6694] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/01/2020] [Accepted: 01/20/2021] [Indexed: 11/05/2022] Open
Abstract
Background and study aims A novel endoscopic optical diagnosis classification system (SIMPLE) has recently been developed. This study aimed to evaluate the SIMPLE classification in a clinical cohort. Patients and methods All diminutive and small colorectal polyps found in a cohort of individuals undergoing screening, diagnostic, or surveillance colonoscopies underwent optical diagnosis using image-enhanced endoscopy (IEE) and the SIMPLE classification. The primary outcome was the agreement of surveillance intervals determined by optical diagnosis compared with pathology-based results for diminutive polyps. Secondary outcomes included the negative predictive value (NPV) for rectosigmoid adenomas, the percentage of pathology exams avoided, and the percentage of immediate surveillance interval recommendations. Analysis of optical diagnosis for polyps ≤ 10 mm was also performed. Results 399 patients (median age 62.6 years; 55.6 % female) were enrolled. For patients with at least one polyp ≤ 5 mm undergoing optical diagnosis, agreement with pathology-based surveillance intervals was 93.5 % (95 % confidence interval [CI] 91.4-95.6). The NPV for rectosigmoid adenomas was 86.7 % (95 %CI 77.5-93.2). When using optical diagnosis, pathology analysis could be avoided in 61.5 % (95 %CI 56.9-66.2) of diminutive polyps, and post-colonoscopy surveillance intervals could be given immediately to 70.9 % (95 %CI 66.5-75.4) of patients. For patients with at least one ≤ 10 mm polyp, agreement with pathology-based surveillance intervals was 92.7 % (95 %CI 89.7-95.1). NPV for rectosigmoid adenomas ≤ 10 mm was 85.1 % (95 %CI CI 76.3-91.6). Conclusions IEE with the SIMPLE classification achieved the quality benchmark for the resect and discard strategy; however, the NPV for rectosigmoid polyps requires improvement.
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Affiliation(s)
- Ahmed Amine Alaoui
- University of Montreal, Faculty of Medicine, Montreal, QC, Canada,University of Montreal Hospital Centre Research Center, Gastroenterology, Montreal, QC, Canada
| | - Kussil Oumedjbeur
- University of Montreal, Faculty of Medicine, Montreal, QC, Canada,University of Montreal Hospital Centre Research Center, Gastroenterology, Montreal, QC, Canada
| | - Roupen Djinbachian
- University of Montreal Hospital Centre Research Center, Gastroenterology, Montreal, QC, Canada,University of Montreal Hospital Center, Division of Internal Medicine, Montreal, QC, Canada
| | - Étienne Marchand
- University of Montreal, Faculty of Medicine, Montreal, QC, Canada,University of Montreal Hospital Center, Division of Internal Medicine, Montreal, QC, Canada
| | - Paola N. Marques
- University of Montreal Hospital Centre Research Center, Gastroenterology, Montreal, QC, Canada,Bahia State University, Faculty of Medicine, Salvador, Brazil
| | - Mickael Bouin
- University of Montreal Hospital Centre Research Center, Gastroenterology, Montreal, QC, Canada,University of Montreal Hospital Center, Division of Gastroenterology, Montreal, QC, Canada
| | - Simon Bouchard
- University of Montreal Hospital Centre Research Center, Gastroenterology, Montreal, QC, Canada,University of Montreal Hospital Center, Division of Gastroenterology, Montreal, QC, Canada
| | - Daniel von Renteln
- University of Montreal Hospital Centre Research Center, Gastroenterology, Montreal, QC, Canada,University of Montreal Hospital Center, Division of Gastroenterology, Montreal, QC, Canada
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21
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Li JW, Ang TL. Colonoscopy and artificial intelligence: Bridging the gap or a gap needing to be bridged? Artif Intell Gastrointest Endosc 2021; 2:36-49. [DOI: 10.37126/aige.v2.i2.36] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/19/2021] [Revised: 03/27/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Research in artificial intelligence (AI) in gastroenterology has increased over the last decade. Colonoscopy represents the most widely published field with regards to its use in gastroenterology. Most studies to date center on polyp detection and characterization, as well as real-time evaluation of adequacy of mucosal exposure for inspection. This review article discusses how advances in AI has bridged certain gaps in colonoscopy. In addition, the gaps formed with the development of AI that currently prevent its routine use in colonoscopy will be explored.
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Affiliation(s)
- James Weiquan Li
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
| | - Tiing Leong Ang
- Department of Gastroenterology and Hepatology, Changi General Hospital, Singapore 529889, Singapore
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22
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A CNN CADx System for Multimodal Classification of Colorectal Polyps Combining WL, BLI, and LCI Modalities. APPLIED SCIENCES-BASEL 2020. [DOI: 10.3390/app10155040] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Colorectal polyps are critical indicators of colorectal cancer (CRC). Blue Laser Imaging and Linked Color Imaging are two modalities that allow improved visualization of the colon. In conjunction with the Blue Laser Imaging (BLI) Adenoma Serrated International Classification (BASIC) classification, endoscopists are capable of distinguishing benign and pre-malignant polyps. Despite these advancements, this classification still prevails a high misclassification rate for pre-malignant colorectal polyps. This work proposes a computer aided diagnosis (CADx) system that exploits the additional information contained in two novel imaging modalities, enabling more informative decision-making during colonoscopy. We train and benchmark six commonly used CNN architectures and compare the results with 19 endoscopists that employed the standard clinical classification model (BASIC). The proposed CADx system for classifying colorectal polyps achieves an area under the curve (AUC) of 0.97. Furthermore, we incorporate visual explanatory information together with a probability score, jointly computed from White Light, Blue Laser Imaging, and Linked Color Imaging. Our CADx system for automatic polyp malignancy classification facilitates future advances towards patient safety and may reduce time-consuming and costly histology assessment.
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23
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