Diagnosing and characterizing biliary strictures (BS) remains challenging. Artificial intelligence (AI) applied to digital single-operator cholangioscopy (D-SOC) holds promise for improving diagnostic accuracy in indeterminate BS. This multicenter study aimed to validate a convolutional neural network (CNN) model using a large dataset of D-SOC images to automatically detect and characterize malignant BS. D-SOC exams from three centers-Centro Hospitalar Universitário de São João, Porto, Portugal (n = 123), Hospital Universitario Puerta de Hierro Majadahonda, Madrid, Spain (n = 18), and New York University Langone Hospital, New York, USA (n = 23)-were included. Frames were categorized based on histopathology. The CNN's performance in detecting tumor vessels, papillary projections, nodules, and masses was assessed. The dataset was split into 90% training and 10% validation sets. Performance metrics included AUC, sensitivity, specificity, PPV, and NPV. Analysis of 96,020 images from 164 D-SOC exams (50,427 malignant strictures and 45,593 benign findings) showed the CNN achieved 92.9% accuracy, 91.7% sensitivity, 94.4% specificity, 95.1% PPV, 93.1% NPV, and an AUROC of 0.95. Accuracy rates for morphological features were 90.8% (papillary projections), 93.6% (nodules), 93.2% (masses), and 78.1% (tumor vessels). AI-driven CNN models hold promise for enhancing diagnostic accuracy in suspected biliary malignancies. This multicenter study contributes diverse datasets to ongoing research, supporting further AI applications in this patient population.

Accurately diagnosing biliary strictures is crucial for surgical decisions, and although peroral cholangioscopy (POCS) aids in visual diagnosis, diagnosing malignancies or determining lesion margins via this route remains challenging. Indigo carmine is commonly used to evaluate lesions during GI endoscopy. We aimed to establish the utility of virtual indigo carmine chromoendoscopy (VICI) converted from POCS images using artificial intelligence.

This single-center, retrospective study analyzed 40 patients with biliary strictures who underwent POCS using white-light imaging (WLI) and narrow-band imaging (NBI). A cycle-consistent adversarial network was used to convert the WLI into VICI of POCS images. Three experienced endoscopists evaluated WLI, NBI, and VICI via POCS in all patients. The primary outcome was the visualization quality of surface structures, surface microvessels, and lesion margins. The secondary outcome was diagnostic accuracy.

VICI showed superior visualization of the surface structures and lesion margins compared with WLI (P < .001) and NBI (P < .001). The diagnostic accuracies were 72.5%, 87.5%, and 90.0% in WLI alone, WLI and VICI simultaneously, and WLI and NBI simultaneously, respectively. WLI and VICI simultaneously tended to result in higher accuracy than WLI alone (P = .083), and the results were not significantly different from WLI and NBI simultaneously (P = .65).

VICI in POCS proved valuable for visualizing surface structures and lesion margins and contributed to higher diagnostic accuracy comparable to NBI. In addition to NBI, VICI may be a novel supportive modality for POCS.

Digital single-operator cholangioscopy (D-SOC) has enhanced the ability to diagnose indeterminate biliary strictures (BSs). Pilot studies using artificial intelligence (AI) models in D-SOC demonstrated promising results. Our group aimed to develop a convolutional neural network (CNN) for the identification and morphological characterization of malignant BSs in D-SOC. A total of 84,994 images from 129 D-SOC exams in two centers (Portugal and Spain) were used for developing the CNN. Each image was categorized as either a normal/benign finding or as malignant lesion (the latter dependent on histopathological results). Additionally, the CNN was evaluated for the detection of morphologic features, including tumor vessels and papillary projections. The complete dataset was divided into training and validation datasets. The model was evaluated through its sensitivity, specificity, positive and negative predictive values, accuracy and area under the receiver-operating characteristic and precision-recall curves (AUROC and AUPRC, respectively). The model achieved a 82.9% overall accuracy, 83.5% sensitivity and 82.4% specificity, with an AUROC and AUPRC of 0.92 and 0.93, respectively. The developed CNN successfully distinguished benign findings from malignant BSs. The development and application of AI tools to D-SOC has the potential to significantly augment the diagnostic yield of this exam for identifying malignant strictures.

Background and study aims  Indeterminate biliary strictures pose a significative clinical challenge. Dilated, irregular, and tortuous vessels, often described as tumor vessels, are frequently reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy (D-SOC). In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of tumor vessels (TVs) in D-SOC images. Patients and methods  A convolutional neural network (CNN) was developed. A total of 6475 images from 85 patients who underwent D-SOC (Spyglass, Boston Scientific, Marlborough, Massachusetts, United States) were included. Each frame was evaluated for the presence of TVs. The performance of the CNN was measured by calculating the area under the curve (AUC), sensitivity, specificity, positive and negative predictive values. Results  The sensitivity, specificity, positive predictive value, and negative predictive value were 99.3 %, 99.4 %, 99.6% and 98.7 %, respectively. The AUC was 1.00. Conclusions  Our CNN was able to detect TVs with high accuracy. Development of AI algorithms may enhance the detection of macroscopic characteristics associated with high probability of biliary malignancy, thus optimizing the diagnostic workup of patients with indeterminate biliary strictures.

The diagnosis and characterization of biliary strictures (BSs) is challenging. The introduction of digital single-operator cholangioscopy (DSOC) that allows direct visual inspection of the lesion and targeted biopsy sampling significantly improved the diagnostic yield in patients with indeterminate BSs. However, the diagnostic efficiency of DSOC remains suboptimal. Convolutional neural networks (CNNs) have shown great potential for the interpretation of medical images. We aimed to develop a CNN-based system for automatic detection of malignant BSs in DSOC images.

We developed, trained, and validated a CNN-based on DSOC images. Each frame was labeled as a normal/benign finding or as a malignant lesion if histopathologic evidence of biliary malignancy was available. The entire dataset was split for 5-fold cross-validation. In addition, the image dataset was split for constitution of training and validation datasets. The performance of the CNN was measured by calculating the area under the receiving operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values.

A total of 11,855 images from 85 patients were included (9695 malignant strictures and 2160 benign findings). The model had an overall accuracy of 94.9%, sensitivity of 94.7%, specificity of 92.1%, and AUC of .988 in cross-validation analysis. The image processing speed of the CNN was 7 ms per frame.

The developed deep learning algorithm accurately detected and differentiated malignant strictures from benign biliary conditions. The introduction of artificial intelligence algorithms to DSOC systems may significantly increase its diagnostic yield for malignant strictures.

Choledochoscopy, or cholangioscopy, is an endoscopic procedure for direct visualization within the biliary tract for diagnostic or therapeutic purposes. Since its conception in 1879, many variations and improvements are made to ensure relevance in diagnosing and managing a range of intrahepatic and extrahepatic biliary pathologies. This ranges from improved visual impression and optical guided biopsies of indeterminate biliary strictures and clinically indistinguishable pathologies to therapeutic uses in stone fragmentation and other ablative therapies. Furthermore, with the evolving understanding of biliary disorders, there are significant innovative ideas and techniques to fill this void, such as nuanced instances of biliary stenting and retrieving migrated ductal stents. With this in mind, we present a review of the current advancements in choledo-choscopy with new supporting evidence that further delineates the role of choledochoscopy in various diagnostic and therapeutic interventions, complications, limitations and put forth areas for further study.

Characterization of biliary strictures is challenging. Papillary projections (PP) are often reported in biliary strictures with high malignancy potential during digital single-operator cholangioscopy. In recent years, the development of artificial intelligence (AI) algorithms for application to endoscopic practice has been intensely studied. We aimed to develop an AI algorithm for automatic detection of PP in digital single-operator cholangioscopy images.

A convolutional neural network (CNN) was developed. Each frame was evaluated for the presence of PP. The CNN's performance was measured by the area under the curve, sensitivity, specificity, and positive and negative predictive values.

A total of 3,920 images from 85 patients were included. Our model had a sensitivity and specificity 99.7% and 97.1%, respectively. The area under the curve was 1.00.

Our CNN was able to detect PP with high accuracy. Future development of AI tools may optimize the macroscopic characterization of biliary strictures.

The prognosis of patients with advanced or unresectable extrahepatic cholangiocarcinoma is poor. More than 50% of patients with jaundice are inoperable at the time of first diagnosis. Endoscopic treatment in patients with obstructive jaundice ensures bile duct drainage in preoperative or palliative settings. Relief of symptoms (pain, pruritus, jaundice) and improvement in quality of life are the aims of palliative therapy. Stent implantation by endoscopic retrograde cholangiopancreatography is generally preferred for long-term palliation. There is a vast variety of plastic and metal stents, covered or uncovered. The stent choice depends on the expected length of survival, quality of life, costs and physician expertise. This review will provide the framework for the endoscopic minimally invasive therapy in extrahepatic cholangiocarcinoma. Moreover, additional therapies, such as brachytherapy, photodynamic therapy, radiofrequency ablation, chemotherapy, molecular-targeted therapy and/or immunotherapy by the endoscopic approach, are the nonsurgical methods associated with survival improvement rate and/or local symptom palliation.