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Zhang YH, Fang AQ, Zhu HY, Du YQ. Facing the challenges of autoimmune pancreatitis diagnosis: The answer from artificial intelligence. World J Gastroenterol 2025; 31:102950. [PMID: 40182594 PMCID: PMC11962844 DOI: 10.3748/wjg.v31.i12.102950] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2024] [Revised: 02/19/2025] [Accepted: 02/26/2025] [Indexed: 03/26/2025] Open
Abstract
Current diagnosis of autoimmune pancreatitis (AIP) is challenging and often requires combining multiple dimensions. There is a need to explore new methods for diagnosing AIP. The development of artificial intelligence (AI) is evident, and it is believed to have potential in the clinical diagnosis of AIP. This article aims to list the current diagnostic difficulties of AIP, describe existing AI applications, and suggest directions for future AI usages in AIP diagnosis.
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Affiliation(s)
- You-Han Zhang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai 200433, China
| | - Ai-Qiao Fang
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai 200433, China
| | - Hui-Yun Zhu
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai 200433, China
| | - Yi-Qi Du
- Department of Gastroenterology, Changhai Hospital, Second Military Medical University (Naval Medical University), Shanghai 200433, China
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2
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Bush N, Khashab M, Akshintala VS. Current and Emerging Applications of Artificial Intelligence (AI) in the Management of Pancreatobiliary (PB) disorders. Curr Gastroenterol Rep 2024; 26:304-309. [PMID: 39134866 DOI: 10.1007/s11894-024-00942-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/30/2024] [Indexed: 09/11/2024]
Abstract
PURPOSE OF REVIEW: In this review, we aim to summarize the existing literature and future directions on the use of artificial intelligence (AI) for the diagnosis and treatment of PB (pancreaticobiliary) disorders. RECENT FINDINGS: AI models have been developed to aid in the diagnosis and management of PB disorders such as pancreatic adenocarcinoma (PDAC), pancreatic neuroendocrine tumors (pNETs), acute pancreatitis, chronic pancreatitis, autoimmune pancreatitis, choledocholithiasis, indeterminate biliary strictures, cholangiocarcinoma and endoscopic procedures such as ERCP, EUS, and cholangioscopy. Recent studies have integrated radiological, endoscopic and pathological data to develop models to aid in better detection and prognostication of these disorders. AI is an indispensable proponent in the future practice of medicine. It has been extensively studied and approved for use in the detection of colonic polyps. AI models based on clinical, laboratory, and radiomics have been developed to aid in the diagnosis and management of various PB disorders and its application is ever expanding. Despite promising results, these AI-based models need further external validation to be clinically applicable.
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Affiliation(s)
- Nikhil Bush
- Department of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Mouen Khashab
- Department of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Venkata S Akshintala
- Department of Gastroenterology and Hepatology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
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3
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Rawlani P, Ghosh NK, Kumar A. Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis. Artif Intell Gastroenterol 2023; 4:48-63. [DOI: 10.35712/aig.v4.i3.48] [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: 07/27/2023] [Revised: 09/11/2023] [Accepted: 10/08/2023] [Indexed: 12/07/2023] Open
Abstract
Artificial intelligence (AI) has been used in various fields of day-to-day life and its role in medicine is immense. Understanding of oncology has been improved with the introduction of AI which helps in diagnosis, treatment planning, management, prognosis, and follow-up. It also helps to identify high-risk groups who can be subjected to timely screening for early detection of malignant conditions. It is more important in pancreatic cancer as it is one of the major causes of cancer-related deaths worldwide and there are no specific early features (clinical and radiological) for diagnosis. With improvement in imaging modalities (computed tomography, magnetic resonance imaging, endoscopic ultrasound), most often clinicians were being challenged with lesions that were difficult to diagnose with human competence. AI has been used in various other branches of medicine to differentiate such indeterminate lesions including the thyroid gland, breast, lungs, liver, adrenal gland, kidney, etc. In the case of pancreatic cancer, the role of AI has been explored and is still ongoing. This review article will focus on how AI can be used to diagnose pancreatic cancer early or differentiate it from benign pancreatic lesions, therefore, management can be planned at an earlier stage.
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Affiliation(s)
- Palash Rawlani
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Nalini Kanta Ghosh
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
| | - Ashok Kumar
- Department of Surgical Gastroenterology, Sanjay Gandhi Post Graduate Institute of Medical Sciences, Lucknow 226014, Uttar Pradesh, India
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Artificial Intelligence-The Rising Star in the Field of Gastroenterology and Hepatology. Diagnostics (Basel) 2023; 13:diagnostics13040662. [PMID: 36832150 PMCID: PMC9955763 DOI: 10.3390/diagnostics13040662] [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: 12/05/2022] [Revised: 01/31/2023] [Accepted: 02/07/2023] [Indexed: 02/12/2023] Open
Abstract
Artificial intelligence (AI) is a term that covers a multitude of techniques that are used in a manner that tries to reproduce human intelligence. AI is helpful in various medical specialties that use imaging for diagnostic purposes, and gastroenterology is no exception. In this field, AI has several applications, such as detecting and classifying polyps, detecting the malignancy in polyps, diagnosing Helicobacter pylori infection, gastritis, inflammatory bowel disease, gastric cancer, esophageal neoplasia, and pancreatic and hepatic lesions. The aim of this mini-review is to analyze the currently available studies regarding AI in the field of gastroenterology and hepatology and to discuss its main applications as well as its main limitations.
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Usmani UA, Happonen A, Watada J, Khakurel J. Artificial Intelligence Applications in Healthcare. LECTURE NOTES IN NETWORKS AND SYSTEMS 2023:1085-1104. [DOI: 10.1007/978-981-99-3091-3_89] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/05/2025]
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A systematic review of radiomics in pancreatitis: applying the evidence level rating tool for promoting clinical transferability. Insights Imaging 2022; 13:139. [PMID: 35986798 PMCID: PMC9391628 DOI: 10.1186/s13244-022-01279-4] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Accepted: 07/26/2022] [Indexed: 12/16/2022] Open
Abstract
Background Multiple tools have been applied to radiomics evaluation, while evidence rating tools for this field are still lacking. This study aims to assess the quality of pancreatitis radiomics research and test the feasibility of the evidence level rating tool. Results Thirty studies were included after a systematic search of pancreatitis radiomics studies until February 28, 2022, via five databases. Twenty-four studies employed radiomics for diagnostic purposes. The mean ± standard deviation of the adherence rate was 38.3 ± 13.3%, 61.3 ± 11.9%, and 37.1 ± 27.2% for the Radiomics Quality Score (RQS), the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist, and the Image Biomarker Standardization Initiative (IBSI) guideline for preprocessing steps, respectively. The median (range) of RQS was 7.0 (− 3.0 to 18.0). The risk of bias and application concerns were mainly related to the index test according to the modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool. The meta-analysis on differential diagnosis of autoimmune pancreatitis versus pancreatic cancer by CT and mass-forming pancreatitis versus pancreatic cancer by MRI showed diagnostic odds ratios (95% confidence intervals) of, respectively, 189.63 (79.65–451.48) and 135.70 (36.17–509.13), both rated as weak evidence mainly due to the insufficient sample size. Conclusions More research on prognosis of acute pancreatitis is encouraged. The current pancreatitis radiomics studies have insufficient quality and share common scientific disadvantages. The evidence level rating is feasible and necessary for bringing the field of radiomics from preclinical research area to clinical stage. Supplementary Information The online version contains supplementary material available at 10.1186/s13244-022-01279-4.
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Rangwani S, Ardeshna DR, Rodgers B, Melnychuk J, Turner R, Culp S, Chao WL, Krishna SG. Application of Artificial Intelligence in the Management of Pancreatic Cystic Lesions. Biomimetics (Basel) 2022; 7:79. [PMID: 35735595 PMCID: PMC9221027 DOI: 10.3390/biomimetics7020079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Revised: 06/07/2022] [Accepted: 06/07/2022] [Indexed: 12/10/2022] Open
Abstract
The rate of incidentally detected pancreatic cystic lesions (PCLs) has increased over the past decade and was recently reported at 8%. These lesions pose a unique challenge, as each subtype of PCL carries a different risk of malignant transformation, ranging from 0% (pancreatic pseudocyst) to 34-68% (main duct intraductal papillary mucinous neoplasm). It is imperative to correctly risk-stratify the malignant potential of these lesions in order to provide the correct care course for the patient, ranging from monitoring to surgical intervention. Even with the multiplicity of guidelines (i.e., the American Gastroenterology Association guidelines and Fukuoka/International Consensus guidelines) and multitude of diagnostic information, risk stratification of PCLs falls short. Studies have reported that 25-64% of patients undergoing PCL resection have pancreatic cysts with no malignant potential, and up to 78% of mucin-producing cysts resected harbor no malignant potential on pathological evaluation. Clinicians are now incorporating artificial intelligence technology to aid in the management of these difficult lesions. This review article focuses on advancements in artificial intelligence within digital pathomics, radiomics, and genomics as they apply to the diagnosis and risk stratification of PCLs.
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Affiliation(s)
- Shiva Rangwani
- Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA; (S.R.); (D.R.A.)
| | - Devarshi R. Ardeshna
- Department of Internal Medicine, Ohio State University Wexner Medical Center, Columbus, OH 43210, USA; (S.R.); (D.R.A.)
| | - Brandon Rodgers
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA; (B.R.); (J.M.); (R.T.)
| | - Jared Melnychuk
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA; (B.R.); (J.M.); (R.T.)
| | - Ronald Turner
- College of Medicine, The Ohio State University, Columbus, OH 43210, USA; (B.R.); (J.M.); (R.T.)
| | - Stacey Culp
- Department of Biomedical Informatics, The Ohio State University College of Medicine, Columbus, OH 43210, USA;
| | - Wei-Lun Chao
- Department of Computer Science and Engineering, The Ohio State University, Columbus, OH 43210, USA;
| | - Somashekar G. Krishna
- Department of Gastroenterology, Hepatology, and Nutrition, The Ohio State University Wexner Medical Center, Columbus, OH 43210, USA
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Simsek C, Lee LS. Machine learning in endoscopic ultrasonography and the pancreas: The new frontier? Artif Intell Gastroenterol 2022; 3:54-65. [DOI: 10.35712/aig.v3.i2.54] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/01/2022] [Revised: 03/28/2022] [Accepted: 04/19/2022] [Indexed: 02/06/2023] Open
Abstract
Pancreatic diseases have a substantial burden on society which is predicted to increase further over the next decades. Endoscopic ultrasonography (EUS) remains the best available diagnostic method to assess the pancreas, however, there remains room for improvement. Artificial intelligence (AI) approaches have been adopted to assess pancreatic diseases for over a decade, but this methodology has recently reached a new era with the innovative machine learning algorithms which can process, recognize, and label endosonographic images. Our review provides a targeted summary of AI in EUS for pancreatic diseases. Included studies cover a wide spectrum of pancreatic diseases from pancreatic cystic lesions to pancreatic masses and diagnosis of pancreatic cancer, chronic pancreatitis, and autoimmune pancreatitis. For these, AI models seemed highly successful, although the results should be evaluated carefully as the tasks, datasets and models were greatly heterogenous. In addition to use in diagnostics, AI was also tested as a procedural real-time assistant for EUS-guided biopsy as well as recognition of standard pancreatic stations and labeling anatomical landmarks during routine examination. Studies thus far have suggested that the adoption of AI in pancreatic EUS is highly promising and further opportunities should be explored in the field.
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Affiliation(s)
- Cem Simsek
- Department of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, United States
| | - Linda S Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, United States
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Kröner PT, Engels MML, Glicksberg BS, Johnson KW, Mzaik O, van Hooft JE, Wallace MB, El-Serag HB, Krittanawong C. Artificial intelligence in gastroenterology: A state-of-the-art review. World J Gastroenterol 2021; 27:6794-6824. [PMID: 34790008 PMCID: PMC8567482 DOI: 10.3748/wjg.v27.i40.6794] [Citation(s) in RCA: 74] [Impact Index Per Article: 18.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/11/2021] [Revised: 06/15/2021] [Accepted: 09/16/2021] [Indexed: 02/06/2023] Open
Abstract
The development of artificial intelligence (AI) has increased dramatically in the last 20 years, with clinical applications progressively being explored for most of the medical specialties. The field of gastroenterology and hepatology, substantially reliant on vast amounts of imaging studies, is not an exception. The clinical applications of AI systems in this field include the identification of premalignant or malignant lesions (e.g., identification of dysplasia or esophageal adenocarcinoma in Barrett’s esophagus, pancreatic malignancies), detection of lesions (e.g., polyp identification and classification, small-bowel bleeding lesion on capsule endoscopy, pancreatic cystic lesions), development of objective scoring systems for risk stratification, predicting disease prognosis or treatment response [e.g., determining survival in patients post-resection of hepatocellular carcinoma), determining which patients with inflammatory bowel disease (IBD) will benefit from biologic therapy], or evaluation of metrics such as bowel preparation score or quality of endoscopic examination. The objective of this comprehensive review is to analyze the available AI-related studies pertaining to the entirety of the gastrointestinal tract, including the upper, middle and lower tracts; IBD; the hepatobiliary system; and the pancreas, discussing the findings and clinical applications, as well as outlining the current limitations and future directions in this field.
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Affiliation(s)
- Paul T Kröner
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Megan ML Engels
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Cancer Center Amsterdam, Department of Gastroenterology and Hepatology, Amsterdam UMC, Location AMC, Amsterdam 1105, The Netherlands
| | - Benjamin S Glicksberg
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Kipp W Johnson
- The Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, United States
| | - Obaie Mzaik
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
| | - Jeanin E van Hooft
- Department of Gastroenterology and Hepatology, Leiden University Medical Center, Amsterdam 2300, The Netherlands
| | - Michael B Wallace
- Division of Gastroenterology and Hepatology, Mayo Clinic, Jacksonville, FL 32224, United States
- Division of Gastroenterology and Hepatology, Sheikh Shakhbout Medical City, Abu Dhabi 11001, United Arab Emirates
| | - Hashem B El-Serag
- Section of Gastroenterology and Hepatology, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
| | - Chayakrit Krittanawong
- Section of Health Services Research, Michael E. DeBakey VA Medical Center and Baylor College of Medicine, Houston, TX 77030, United States
- Section of Cardiology, Michael E. DeBakey VA Medical Center, Houston, TX 77030, United States
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Mankoo R, Ali AH, Hammoud GM. Use of artificial intelligence in endoscopic ultrasound evaluation of pancreatic pathologies. Artif Intell Gastrointest Endosc 2021; 2:89-94. [DOI: 10.37126/aige.v2.i3.89] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 06/20/2021] [Accepted: 06/28/2021] [Indexed: 02/06/2023] Open
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Mankoo R, Ali AH, Hammoud GM. Use of artificial intelligence in endoscopic ultrasound evaluation of pancreatic pathologies. Artif Intell Gastrointest Endosc 2021; 2:88-93. [DOI: 10.37126/aige.v2.i3.88] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
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Gorris M, Hoogenboom SA, Wallace MB, van Hooft JE. Artificial intelligence for the management of pancreatic diseases. Dig Endosc 2021; 33:231-241. [PMID: 33065754 PMCID: PMC7898901 DOI: 10.1111/den.13875] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 09/29/2020] [Accepted: 10/11/2020] [Indexed: 12/16/2022]
Abstract
Novel artificial intelligence techniques are emerging in all fields of healthcare, including gastroenterology. The aim of this review is to give an overview of artificial intelligence applications in the management of pancreatic diseases. We performed a systematic literature search in PubMed and Medline up to May 2020 to identify relevant articles. Our results showed that the development of machine-learning based applications is rapidly evolving in the management of pancreatic diseases, guiding precision medicine in clinical, endoscopic and radiologic settings. Before implementation into clinical practice, further research should focus on the external validation of novel techniques, clarifying the accuracy and robustness of these models.
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Affiliation(s)
- Myrte Gorris
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
| | - Sanne A. Hoogenboom
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
| | - Michael B. Wallace
- Department of Gastroenterology and HepatologyMayo Clinic JacksonvilleJacksonvilleUSA
| | - Jeanin E. van Hooft
- Department of Gastroenterology and HepatologyAmsterdam Gastroenterology Endocrinology MetabolismAmsterdam University Medical CentersUniversity of AmsterdamAmsterdamThe Netherlands
- Department of Gastroenterology and HepatologyLeiden University Medical CenterLeidenThe Netherlands
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Pannala R, Krishnan K, Melson J, Parsi MA, Schulman AR, Sullivan S, Trikudanathan G, Trindade AJ, Watson RR, Maple JT, Lichtenstein DR. Artificial intelligence in gastrointestinal endoscopy. VIDEOGIE : AN OFFICIAL VIDEO JOURNAL OF THE AMERICAN SOCIETY FOR GASTROINTESTINAL ENDOSCOPY 2020; 5:598-613. [PMID: 33319126 PMCID: PMC7732722 DOI: 10.1016/j.vgie.2020.08.013] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND AIMS Artificial intelligence (AI)-based applications have transformed several industries and are widely used in various consumer products and services. In medicine, AI is primarily being used for image classification and natural language processing and has great potential to affect image-based specialties such as radiology, pathology, and gastroenterology (GE). This document reviews the reported applications of AI in GE, focusing on endoscopic image analysis. METHODS The MEDLINE database was searched through May 2020 for relevant articles by using key words such as machine learning, deep learning, artificial intelligence, computer-aided diagnosis, convolutional neural networks, GI endoscopy, and endoscopic image analysis. References and citations of the retrieved articles were also evaluated to identify pertinent studies. The manuscript was drafted by 2 authors and reviewed in person by members of the American Society for Gastrointestinal Endoscopy Technology Committee and subsequently by the American Society for Gastrointestinal Endoscopy Governing Board. RESULTS Deep learning techniques such as convolutional neural networks have been used in several areas of GI endoscopy, including colorectal polyp detection and classification, analysis of endoscopic images for diagnosis of Helicobacter pylori infection, detection and depth assessment of early gastric cancer, dysplasia in Barrett's esophagus, and detection of various abnormalities in wireless capsule endoscopy images. CONCLUSIONS The implementation of AI technologies across multiple GI endoscopic applications has the potential to transform clinical practice favorably and improve the efficiency and accuracy of current diagnostic methods.
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Key Words
- ADR, adenoma detection rate
- AI, artificial intelligence
- AMR, adenoma miss rate
- ANN, artificial neural network
- BE, Barrett’s esophagus
- CAD, computer-aided diagnosis
- CADe, CAD studies for colon polyp detection
- CADx, CAD studies for colon polyp classification
- CI, confidence interval
- CNN, convolutional neural network
- CRC, colorectal cancer
- DL, deep learning
- GI, gastroenterology
- HD-WLE, high-definition white light endoscopy
- HDWL, high-definition white light
- ML, machine learning
- NBI, narrow-band imaging
- NPV, negative predictive value
- PIVI, preservation and Incorporation of Valuable Endoscopic Innovations
- SVM, support vector machine
- VLE, volumetric laser endomicroscopy
- WCE, wireless capsule endoscopy
- WL, white light
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Affiliation(s)
- Rahul Pannala
- Department of Gastroenterology and Hepatology, Mayo Clinic, Scottsdale, Arizona
| | - Kumar Krishnan
- Division of Gastroenterology, Department of Internal Medicine, Harvard Medical School and Massachusetts General Hospital, Boston, Massachusetts
| | - Joshua Melson
- Division of Digestive Diseases, Department of Internal Medicine, Rush University Medical Center, Chicago, Illinois
| | - Mansour A Parsi
- Section for Gastroenterology and Hepatology, Tulane University Health Sciences Center, New Orleans, Louisiana
| | - Allison R Schulman
- Department of Gastroenterology, Michigan Medicine, University of Michigan, Ann Arbor, Michigan
| | - Shelby Sullivan
- Division of Gastroenterology and Hepatology, University of Colorado School of Medicine, Aurora, Colorado
| | - Guru Trikudanathan
- Department of Gastroenterology, Hepatology and Nutrition, University of Minnesota, Minneapolis, Minnesota
| | - Arvind J Trindade
- Department of Gastroenterology, Zucker School of Medicine at Hofstra/Northwell, Long Island Jewish Medical Center, New Hyde Park, New York
| | - Rabindra R Watson
- Department of Gastroenterology, Interventional Endoscopy Services, California Pacific Medical Center, San Francisco, California
| | - John T Maple
- Division of Digestive Diseases and Nutrition, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - David R Lichtenstein
- Division of Gastroenterology, Boston Medical Center, Boston University School of Medicine, Boston, Massachusetts
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14
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Hellmann AR, Paiella S, Kostro J, Marek I, Adrych K, Śledziński Z, Hać S, Bassi C. Surgical decompression of Wirsung duct reduces serum concentration of SPINK1 in patients with chronic pancreatitis. Pancreatology 2018; 18:275-279. [PMID: 29525377 DOI: 10.1016/j.pan.2018.03.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Revised: 01/21/2018] [Accepted: 03/02/2018] [Indexed: 12/11/2022]
Abstract
OBJECTIVES The primary aim of this study was to determine the blood levels of SPINK1 in patients with chronic pancreatitis (CP) submitted to surgical or endoscopic decompression of pancreatic duct (PD). Additionally, we measured trypsin activity levels. METHODS Two groups were identified, surgical (group A) and endoscopic (group B). Levels of SPINK1 and trypsin activity were measured at baseline and 6 months after pancreatic duct decompression and then compared within the groups. SPINK1 levels were determined with Human ELISA Kit. RESULTS Group A and B were made up of 30 and 28 patients, respectively. Baseline features of the groups were similar. A decrease in SPINK1 levels was significant only in group A 46.88 to 16.10 ng/mL (p = 0.001). On the contrary, trypsin activity changed significantly in group B 40.01 to 34.92 mU/mL (p = 0.01). Patients of group A showed a significant increase in BMI, before and after treatment. The pain score pre- and post-treatment reduced significantly in both groups (p < 0.001). CONCLUSIONS We demonstrate for the first time a significant decrease of SPINK1 levels after surgical decompression of PD and a reduction of trypsin activity analysis after endoscopic decompression. The meaning of this phenomena is yet to be explained and it should be further explored.
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Affiliation(s)
- Andrzej Rafal Hellmann
- Department of General, Endocrine and Transplant Surgery, Medical University of Gdańsk, Poland.
| | - Salvatore Paiella
- Department of General and Pancreatic Surgery, Pancreas Institute, University and Hospital Trust of Verona, 37134 Verona, Italy
| | - Justyna Kostro
- Department of General, Endocrine and Transplant Surgery, Medical University of Gdańsk, Poland
| | - Iwona Marek
- Department of Gastroenterology & Hepatology, Medical University of Gdańsk, Poland
| | - Krystian Adrych
- Department of Gastroenterology & Hepatology, Medical University of Gdańsk, Poland
| | - Zbigniew Śledziński
- Department of General, Endocrine and Transplant Surgery, Medical University of Gdańsk, Poland
| | - Stanisław Hać
- Department of General, Endocrine and Transplant Surgery, Medical University of Gdańsk, Poland
| | - Claudio Bassi
- Department of General and Pancreatic Surgery, Pancreas Institute, University and Hospital Trust of Verona, 37134 Verona, Italy
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15
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Lee LS, Andersen DK, Ashida R, Brugge WR, Canto MI, Chang KJ, Chari ST, DeWitt J, Hwang JH, Khashab MA, Kim K, Levy MJ, McGrath K, Park WG, Singhi A, Stevens T, Thompson CC, Topazian MD, Wallace MB, Wani S, Waxman I, Yadav D, Singh VK. EUS and related technologies for the diagnosis and treatment of pancreatic disease: research gaps and opportunities-Summary of a National Institute of Diabetes and Digestive and Kidney Diseases workshop. Gastrointest Endosc 2017; 86:768-778. [PMID: 28941651 PMCID: PMC6698378 DOI: 10.1016/j.gie.2017.08.006] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2017] [Accepted: 08/07/2017] [Indexed: 12/11/2022]
Abstract
A workshop was sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases to address the research gaps and opportunities in pancreatic EUS. The event occurred on July 26, 2017 in 4 sessions: (1) benign pancreatic diseases, (2) high-risk pancreatic diseases, (3) diagnostic and therapeutics, and (4) new technologies. The current state of knowledge was reviewed, with identification of numerous gaps in knowledge and research needs. Common themes included the need for large multicenter consortia of various pancreatic diseases to facilitate meaningful research of these entities; to standardize EUS features of different pancreatic disorders, the technique of sampling pancreatic lesions, and the performance of various therapeutic EUS procedures; and to identify high-risk disease early at the cellular level before macroscopic disease develops. The need for specialized tools and accessories to enable the safe and effective performance of therapeutic EUS procedures also was discussed.
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Affiliation(s)
- Linda S Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Dana K Andersen
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, Maryland, USA
| | - Reiko Ashida
- Departments of Cancer Survey and Gastrointestinal Oncology, Osaka Prefectural Hospital Organization, Osaka International Cancer Institute, Osaka, Japan
| | - William R Brugge
- Department of Gastroenterology, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Mimi I Canto
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kenneth J Chang
- Comprehensive Digestive Disease Center, Department of Gastroenterology and Hepatology, University of California at Irvine Health, Orange, California, USA
| | - Suresh T Chari
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - John DeWitt
- Division of Gastroenterology, Indiana University Health Medical Center, Indianapolis, Indiana, USA
| | - Joo Ha Hwang
- Department of Medicine, University of Washington, Seattle, Washington, USA
| | - Mouen A Khashab
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Kang Kim
- Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Michael J Levy
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Kevin McGrath
- Division of Gastroenterology, Hepatology, and Nutrition, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | - Walter G Park
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Aatur Singhi
- Department of Pathology, University of Pittsburgh Medical Center, Sewickley, Pennsylvania, USA
| | - Tyler Stevens
- Department of Gastroenterology, Cleveland Clinic, Cleveland, Ohio, USA
| | - Christopher C Thompson
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Mark D Topazian
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, Minnesota, USA
| | - Michael B Wallace
- Department of Gastroenterology and Hepatology, Mayo Clinic Florida, Jacksonville, Florida, USA
| | - Sachin Wani
- Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Campus, Aurora, Colorado, USA
| | - Irving Waxman
- Department of Medicine, The University of Chicago Comprehensive Cancer Center, University of Chicago School of Medicine, Chicago, Illinois, USA
| | - Dhiraj Yadav
- Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Vikesh K Singh
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
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16
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A Panel of CA19-9, Ca125, and Ca15-3 as the Enhanced Test for the Differential Diagnosis of the Pancreatic Lesion. DISEASE MARKERS 2017; 2017:8629712. [PMID: 28356610 PMCID: PMC5357521 DOI: 10.1155/2017/8629712] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/16/2016] [Revised: 01/29/2017] [Accepted: 02/01/2017] [Indexed: 12/16/2022]
Abstract
Background. Proper diagnosis of pancreatic lesion etiology is a challenging clinical dilemma. Studies suggest that surgery for suspected pancreatic ductal adenocarcinoma (PDAC) reveals a benign lesion in 5% to 13% of cases. The aim of our study was to assess whether routinely used biomarkers such as CA19-9, Ca125, Ca15-3, and CEA, when combined, can potentially yield an accurate test predicting pancreatic lesion etiology. Methods. We retrospectively analyzed data of 326 patients who underwent a diagnostic process due to pancreatic lesions of unknown etiology. Results. We found statistically significant differences in mean levels of all biomarkers. In logistic regression model, we applied levels CA19-9, Ca125, and Ca15-3 as variables. Two validation methods were used, namely, random data split into training and validation groups and bootstrapping. Afterward, we built ROC curve using the model that we had created, reaching AUC = 0,801. With an optimal cut-off point, it achieved specificity of 81,2% and sensitivity of 63,10%. Our proposed model has superior diagnostic accuracy to both CA19-9 (p = 0,0194) and CA125 (p = 0,0026). Conclusion. We propose a test that is superior to CA19-9 in a differential diagnosis of pancreatic lesion etiology. Although our test fails to reach exceptionally high accuracy, its feasibility and cost-effectiveness make it clinically useful.
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17
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Lee LS, Andersen DK, Ashida R, Brugge WR, Canto MI, Chang KJ, Chari ST, DeWitt J, Hwang JH, Khashab MA, Kim K, Levy MJ, McGrath K, Park WG, Singhi A, Stevens T, Thompson CC, Topazian MD, Wallace MB, Wani S, Waxman I, Yadav D, Singh VK. Endoscopic Ultrasound and Related Technologies for the Diagnosis and Treatment of Pancreatic Disease - Research Gaps and Opportunities: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop. Pancreas 2017; 46:1242-1250. [PMID: 28926412 PMCID: PMC5645254 DOI: 10.1097/mpa.0000000000000936] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
A workshop was sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases to address the research gaps and opportunities in pancreatic endoscopic ultrasound (EUS). The event occurred on July 26, 2017 in 4 sessions: (1) benign pancreatic diseases, (2) high-risk pancreatic diseases, (3) diagnostic and therapeutics, and (4) new technologies. The current state of knowledge was reviewed, with identification of numerous gaps in knowledge and research needs. Common themes included the need for large multicenter consortia of various pancreatic diseases to facilitate meaningful research of these entities; to standardize EUS features of different pancreatic disorders, the technique of sampling pancreatic lesions, and the performance of various therapeutic EUS procedures; and to identify high-risk disease early at the cellular level before macroscopic disease develops. The need for specialized tools and accessories to enable the safe and effective performance of therapeutic EUS procedures also was discussed.
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Affiliation(s)
- Linda S. Lee
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Dana K. Andersen
- Division of Digestive Diseases and Nutrition, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD
| | - Reiko Ashida
- Departments of Cancer Survey and Gastrointestinal Oncology, Osaka Prefectural Hospital Organization, Osaka International Cancer Institute, Osaka, Japan
| | - William R. Brugge
- Department of Gastroenterology, Massachusetts General Hospital, Boston, MA
| | - Mimi I. Canto
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kenneth J. Chang
- Comprehensive Digestive Disease Center, Department of Gastroenterology and Hepatology, University of California at Irvine Health, Orange, CA
| | - Suresh T. Chari
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| | - John DeWitt
- Division of Gastroenterology, Indiana University Health Medical Center, Indianapolis, IN
| | - Joo Ha Hwang
- Department of Medicine, University of Washington, Seattle, WA
| | - Mouen A. Khashab
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Kang Kim
- Department of Medicine, University of Pittsburgh, Pittsburgh, PA
| | - Michael J. Levy
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| | - Kevin McGrath
- Division of Gastroenterology, Hepatology, and Nutrition, University of Pittsburgh Medical Center, Pittsburgh, PA
| | - Walter G. Park
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Aatur Singhi
- Department of Pathology, University of Pittsburgh Medical Center, Sewickley, PA
| | - Tyler Stevens
- Department of Gastroenterology, Cleveland Clinic, Cleveland, OH
| | - Christopher C. Thompson
- Division of Gastroenterology, Hepatology and Endoscopy, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA
| | - Mark D. Topazian
- Department of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN
| | - Michael B. Wallace
- Department of Gastroenterology and Hepatology, Mayo Clinic Florida, Jacksonville, FL
| | - Sachin Wani
- Division of Gastroenterology and Hepatology, University of Colorado Anschutz Medical Campus, Aurora, CO
| | - Irving Waxman
- Department of Medicine, The University of Chicago Comprehensive Cancer Center, University of Chicago School of Medicine, Chicago, IL
| | - Dhiraj Yadav
- Department of Medicine, Stanford University School of Medicine, Stanford, CA
| | - Vikesh K. Singh
- Division of Gastroenterology, Johns Hopkins University School of Medicine, Baltimore, MD
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