Artificial intelligence for automatic diagnosis of biliary stricture malignancy status in single-operator cholangioscopy: a pilot study

Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases

Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobiliary and hepatic conditions. A key focus is differentiating between benign and malignant lesions, which is crucial for treatment decisions. AI improves diagnostic accuracy through high sensitivity and specificity, while CNN algorithms enhance image analysis and reduce variability. These advancements aim to match the accuracy of pathologists in cancer detection. In addition, AI aids in identifying diagnostic markers, as early detection is essential. This article reviews the applications of machine learning and deep learning in the diagnosis of hepatic and pancreaticobiliary diseases. Although the use of AI in these specialized areas of gastroenterology is primarily confined to experimental trials, current models demonstrate significant potential for enhancing the detection, evaluation, and treatment planning of hepatic and pancreaticobiliary conditions.

Diagnosis of Cholangiocarcinoma: The New Biological and Technological Horizons

The diagnosis of cholangiocarcinoma (CCA) remains challenging. Although new technologies have been developed and validated, their routine use in clinical practice is needed. Conventional cytology obtained during endoscopic retrograde cholangiopancreatography-guided brushings is the first-line technique for the diagnosis of CCA, but it has shown limited sensitivity when combined with endoscopic ultrasound-guided biopsy. Other diagnostic tools have been proposed for the diagnosis of CCA, with their respective advantages and limitations. Cholangioscopy with biopsy or cytology combined with FISH analysis, intraductal biliary ultrasound and confocal laser microscopy have made significant advances in the last decade. More recently, developments in the analytical "omics" sciences have allowed the mapping of the microbiota of patients with CCA, and liquid biopsy with proteomic and extracellular vesicle analysis has allowed the identification of new biomarkers that can be incorporated into the predictive diagnostics. Furthermore, in the preoperative setting, radiomics, radiogenomics and the integrated use of artificial intelligence may provide new useful foundations for integrated diagnosis and personalized therapy for hepatobiliary diseases. This review aims to evaluate the current diagnostic approaches and innovative translational research that can be integrated for the diagnosis of CCA.

Endoscopic treatment of unresectable perihilar cholangiocarcinoma: beyond biliary drainage

Perihilar cholangiocarcinoma (PHCC) is an aggressive biliary malignancy originating from the epithelial cells of the bile duct, typically located in the extrahepatic biliary tree, proximal to the cystic duct. PHCC often presents with a rapid onset of jaundice. While radical surgical resection remains the only curative treatment, only a minority of patients are eligible due to early metastasis and challenges associated with preoperative evaluations. Comprehensive treatments, including chemotherapy, radiotherapy, targeted therapy, and immunotherapy, are crucial for managing PHCC. However, in advanced stages, complications such as cholestatic liver injury, malnutrition, and biliary infections pose significant obstacles to these treatments. Therefore, biliary drainage (BD) is essential in the management of PHCC. In addition to external drainage methods like percutaneous transhepatic biliary drainage (PTBD), endoscopic biliary drainage (EBD), particularly endoscopic retrograde cholangiopancreatography (ERCP), offer an effective option for internal drainage, which is more physiologically compatible and better tolerated. Furthermore, the integration of various endoscopic techniques has expanded the management of PHCC beyond mere drainage. Techniques such as radiofrequency ablation (RFA), photodynamic therapy (PDT), and endoscopic ultrasound (EUS) based methods present new therapeutic avenues, albeit with variable results. This review aims to summarize current advancements and ongoing debates in the field of endoscopic treatment for unresectable PHCC.

The Role of Artificial Intelligence for Advanced Endoscopy

Artificial intelligence (AI) application in gastroenterology has grown in the last decade and continues to evolve very rapidly. Early promising results have opened the door to explore its potential application to advanced endoscopy (AE). The aim of this review is to discuss the current state of the art and future directions of AI in AE. Current evidence suggests that AI-assisted endoscopic ultrasound models can be used in clinical practice to distinguish between benign and malignant pancreatic diseases with excellent results. AI-assisted endoscopic retrograde cholangiopancreatography models could also be useful in identifying the papilla, predicting difficult cannulation, and differentiating between benign and malignant strictures.

Multimodal convolutional neural network-based algorithm for real-time detection and differentiation of malignant and inflammatory biliary strictures in cholangioscopy: a proof-of-concept study (with video)

BACKGROUND AND AIMS

Deep learning algorithms gained attention for detection (computer-aided detection [CADe]) of biliary tract cancer in digital single-operator cholangioscopy (dSOC). We developed a multimodal convolutional neural network (CNN) for detection (CADe), characterization and discriminating (computer-aided diagnosis [CADx]) between malignant, inflammatory, and normal biliary tissue in raw dSOC videos. In addition, clinical metadata were included in the CNN algorithm to overcome limitations of image-only models.

METHODS

Based on dSOC videos and images of 111 patients (total of 15,158 still frames), a real-time CNN-based algorithm for CADe and CADx was developed and validated. We established an image-only model and metadata injection approach. In addition, frame-wise and case-based predictions on complete dSOC video sequences were validated. Model embeddings were visualized, and class activation maps highlighted relevant image regions.

RESULTS

The concatenation-based CADx approach achieved a per-frame area under the receiver-operating characteristic curve of .871, sensitivity of .809 (95% CI, .784-.832), specificity of .773 (95% CI, .761-.785), positive predictive value of .450 (95% CI, .423-.467), and negative predictive value of .946 (95% CI, .940-.954) with respect to malignancy on 5715 test frames from complete videos of 20 patients. For case-based diagnosis using average prediction scores, 6 of 8 malignant cases and all 12 benign cases were identified correctly.

CONCLUSIONS

Our algorithm distinguishes malignant and inflammatory bile duct lesions in dSOC videos, indicating the potential of CNN-based diagnostic support systems for both CADe and CADx. The integration of non-image data can improve CNN-based support systems, targeting current challenges in the assessment of biliary strictures.

Unlocking the Potential of AI in EUS and ERCP: A Narrative Review for Pancreaticobiliary Disease

Artificial Intelligence (AI) is transforming pancreaticobiliary endoscopy by enhancing diagnostic accuracy, procedural efficiency, and clinical outcomes. This narrative review explores AI's applications in endoscopic ultrasound (EUS) and endoscopic retrograde cholangiopancreatography (ERCP), emphasizing its potential to address diagnostic and therapeutic challenges in pancreaticobiliary diseases. In EUS, AI improves pancreatic mass differentiation, malignancy prediction, and landmark recognition, demonstrating high diagnostic accuracy and outperforming traditional guidelines. In ERCP, AI facilitates precise biliary stricture identification, optimizes procedural techniques, and supports decision-making through real-time data integration, improving ampulla recognition and predicting cannulation difficulty. Additionally, predictive analytics help mitigate complications like post-ERCP pancreatitis. The future of AI in pancreaticobiliary endoscopy lies in multimodal data fusion, integrating imaging, genomic, and molecular data to enable personalized medicine. However, challenges such as data quality, external validation, clinician training, and ethical concerns-like data privacy and algorithmic bias-must be addressed to ensure safe implementation. By overcoming these challenges, AI has the potential to redefine pancreaticobiliary healthcare, improving diagnostic accuracy, therapeutic outcomes, and personalized care.

Artificial intelligence for automatic diagnosis and pleomorphic morphological characterization of malignant biliary strictures using digital cholangioscopy

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.

Diagnostic Approach to Biliary Strictures

Biliary strictures represent a narrowing of the bile ducts, leading to obstruction that may result from benign or malignant etiologies. Accurate diagnosis is crucial but challenging due to overlapping features between benign and malignant strictures. This review presents a comprehensive diagnostic approach that integrates biochemical markers, imaging modalities, and advanced endoscopic techniques to distinguish between these causes. Imaging tools such as ultrasound, MRI/MRCP, and CECT are commonly used, each with distinct advantages and limitations. Furthermore, endoscopic procedures such as ERCP and EUS are key in tissue acquisition, enhancing diagnostic accuracy, especially for indeterminate or complex strictures. Recent innovations, including artificial intelligence and new endoscopic techniques, hold promise in enhancing precision and reducing diagnostic challenges. This review emphasizes a multidisciplinary strategy to improve diagnostic pathways, ensuring timely management for patients with biliary strictures.

Impact of Artificial Intelligence on Pancreaticobiliary Endoscopy

Pancreaticobiliary diseases can lead to significant morbidity and their diagnoses rely on imaging and endoscopy which are dependent on operator expertise. Artificial intelligence (AI) has seen a rapid uptake in the field of luminal endoscopy, such as polyp detection during colonoscopy. However, its use for pancreaticobiliary endoscopic modalities such as endoscopic ultrasound (EUS) and cholangioscopy remains scarce, with only few studies available. In this review, we delve into the current evidence, benefits, limitations, and future scope of AI technologies in pancreaticobiliary endoscopy.

Evaluating ChatGPT-4 for the Interpretation of Images from Several Diagnostic Techniques in Gastroenterology

Background: Several artificial intelligence systems based on large language models (LLMs) have been commercially developed, with recent interest in integrating them for clinical questions. Recent versions now include image analysis capacity, but their performance in gastroenterology remains untested. This study assesses ChatGPT-4's performance in interpreting gastroenterology images. Methods: A total of 740 images from five procedures-capsule endoscopy (CE), device-assisted enteroscopy (DAE), endoscopic ultrasound (EUS), digital single-operator cholangioscopy (DSOC), and high-resolution anoscopy (HRA)-were included and analyzed by ChatGPT-4 using a predefined prompt for each. ChatGPT-4 predictions were compared to gold standard diagnoses. Statistical analyses included accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the curve (AUC). Results: For CE, ChatGPT-4 demonstrated accuracies ranging from 50.0% to 90.0%, with AUCs of 0.50-0.90. For DAE, the model demonstrated an accuracy of 67.0% (AUC 0.670). For EUS, the system showed AUCs of 0.488 and 0.550 for the differentiation between pancreatic cystic and solid lesions, respectively. The LLM differentiated benign from malignant biliary strictures with an AUC of 0.550. For HRA, ChatGPT-4 showed an overall accuracy between 47.5% and 67.5%. Conclusions: ChatGPT-4 demonstrated suboptimal diagnostic accuracies for image interpretation across several gastroenterology techniques, highlighting the need for continuous improvement before clinical adoption.

Deep Learning in Endoscopic Ultrasound: A Breakthrough in Detecting Distal Cholangiocarcinoma

INTRODUCTION

Cholangiocarcinoma (CCA) is a highly lethal malignancy originating in the bile ducts, often diagnosed late with poor prognosis. Differentiating benign from malignant biliary tumors remains challenging, necessitating advanced diagnostic techniques.

OBJECTIVE

This study aims to enhance the diagnostic accuracy of endoscopic ultrasound (EUS) for distal cholangiocarcinoma (dCCA) using advanced convolutional neural networks (CCNs) for the classification and segmentation of EUS images, specifically targeting dCCAs, the pancreas, and the bile duct.

MATERIALS AND METHODS

In this retrospective study, EUS images from patients diagnosed with dCCA via biopsy and an EUS-identified bile duct tumor were evaluated. A custom CNN was developed for classification, trained on 156 EUS images. To enhance the model's robustness, image augmentation techniques were applied, generating a total of 1248 images. For tumor and organ segmentation, the DeepLabv3+ network with ResNet50 architecture was utilized, employing Tversky loss to manage unbalanced classes. Performance evaluation included metrics such as accuracy, sensitivity, specificity, and Intersection over Union (IoU). These methods were implemented in collaboration with the ADAPTED Research Group at the Technical University of Cluj-Napoca.

RESULTS

The classification model achieved a high accuracy of 97.82%, with precision and specificity both at 100% and sensitivity at 94.44%. The segmentation models for the pancreas and bile duct demonstrated global accuracies of 84% and 90%, respectively, with robust IoU scores indicating good overlap between predicted and actual contours. The application performed better than the UNet model, particularly in generalization and boundary delineation.

CONCLUSIONS

This study demonstrates the significant potential of AI in EUS imaging for dCCA, presenting a robust tool that enhances diagnostic accuracy and efficiency. The developed MATLAB application serves as a valuable aid for medical professionals, facilitating informed decision-making and improving patient outcomes in the diagnosis of cholangiocarcinoma and related pathologies.

Advancements in biliopancreatic endoscopy - A comprehensive review of artificial intelligence in EUS and ERCP

The development and implementation of artificial intelligence (AI), particularly deep learning (DL) models, has generated significant interest across various fields of gastroenterology. While research in luminal endoscopy has seen rapid translation to clinical practice with approved AI devices, its potential extends far beyond, offering promising benefits for biliopancreatic endoscopy like optical characterization of strictures during cholangioscopy or detection and classification of pancreatic lesions during diagnostic endoscopic ultrasound (EUS). This narrative review provides an up-to-date of the latest literature and available studies in this field. Serving as a comprehensive guide to the current landscape of AI in biliopancreatic endoscopy, emphasizing technological advancements, main applications, ethical considerations, and future directions for research and clinical implementation.

Current and Emerging Applications of Artificial Intelligence (AI) in the Management of Pancreatobiliary (PB) disorders

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.

Virtual indigo carmine chromoendoscopy images: a novel modality for peroral cholangioscopy using artificial intelligence technology (with video)

BACKGROUND AND AIMS

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.

METHODS

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.

RESULTS

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).

CONCLUSIONS

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.

Cholangioscopy-based convoluted neuronal network vs. confocal laser endomicroscopy in identification of neoplastic biliary strictures

Background and study aims Artificial intelligence (AI) models have demonstrated high diagnostic performance identifying neoplasia during digital single-operator cholangioscopy (DSOC). To date, there are no studies directly comparing AI vs. DSOC-guided probe-base confocal laser endomicroscopy (DSOC-pCLE). Thus, we aimed to compare the diagnostic accuracy of a DSOC-based AI model with DSOC-pCLE for identifying neoplasia in patients with indeterminate biliary strictures. Patients and methods This retrospective cohort-based diagnostic accuracy study included patients ≥ 18 years old who underwent DSOC and DSOC-pCLE (June 2014 to May 2022). Four methods were used to diagnose each patient's biliary structure, including DSOC direct visualization, DSOC-pCLE, an offline DSOC-based AI model analysis performed in DSOC recordings, and DSOC/pCLE-guided biopsies. The reference standard for neoplasia was a diagnosis based on further clinical evolution, imaging, or surgical specimen findings during a 12-month follow-up period. Results A total of 90 patients were included in the study. Eighty-six of 90 (95.5%) had neoplastic lesions including cholangiocarcinoma (98.8%) and tubulopapillary adenoma (1.2%). Four cases were inflammatory including two cases with chronic inflammation and two cases of primary sclerosing cholangitis. Compared with DSOC-AI, which obtained an area under the receiver operator curve (AUC) of 0.79, DSOC direct visualization had an AUC of 0.74 ( P = 0.763), DSOC-pCLE had an AUC of 0.72 ( P = 0.634), and DSOC- and pCLE-guided biopsy had an AUC of 0.83 ( P = 0.809). Conclusions The DSOC-AI model demonstrated an offline diagnostic performance similar to that of DSOC-pCLE, DSOC alone, and DSOC/pCLE-guided biopsies. Larger multicenter, prospective, head-to-head trials with a proportional sample among neoplastic and nonneoplastic cases are advisable to confirm the obtained results.

Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions

Pancreaticobiliary endoscopy is an essential tool for diagnosing and treating pancreaticobiliary diseases. However, it does not fully meet clinical needs, which presents challenges such as significant difficulty in operation and risks of missed diagnosis or misdiagnosis. In recent years, artificial intelligence (AI) has enhanced the diagnostic and treatment efficiency and quality of pancreaticobiliary endoscopy. Diagnosis and differential diagnosis based on endoscopic ultrasound (EUS) images, pathology of EUS-guided fine-needle aspiration or biopsy, need for endoscopic retrograde cholangiopancreatography (ERCP) and assessment of operational difficulty, postoperative complications and prediction of patient prognosis, and real-time procedure guidance. This review provides an overview of AI applications in pancreaticobiliary endoscopy and proposes future development directions in aspects such as data quality and algorithmic interpretability, aiming to provide new insights for the integration of AI technology with pancreaticobiliary endoscopy.

Applications of artificial intelligence in biliary tract cancers

Biliary tract cancers are malignant neoplasms arising from bile duct epithelial cells. They include cholangiocarcinomas and gallbladder cancer. Gallbladder cancer has a marked geographical preference and is one of the most common cancers in women in northern India. Biliary tract cancers are usually diagnosed at an advanced, unresectable stage. Hence, the prognosis is extremely dismal. The five-year survival rate in advanced gallbladder cancer is < 5%. Hence, early detection and radical surgery are critical to improving biliary tract cancer prognoses. Radiological imaging plays an essential role in diagnosing and managing biliary tract cancers. However, the diagnosis is challenging because the biliary tract is affected by many diseases that may have radiological appearances similar to cancer. Artificial intelligence (AI) can improve radiologists' performance in various tasks. Deep learning (DL)-based approaches are increasingly incorporated into medical imaging to improve diagnostic performance. This paper reviews the AI-based strategies in biliary tract cancers to improve the diagnosis and prognosis.

Endoscopic evaluation of indeterminate biliary strictures: Cholangioscopy, endoscopic ultrasound, or both?

Accurate and timely diagnosis of biliary strictures can be challenging. Because the diagnostic sensitivity and accuracy of standard endoscopic retrograde cholangiopancreatography-based tissue sampling for malignancy are suboptimal, additional endoscopic evaluation by cholangioscopy and/or endoscopic ultrasound (EUS) is often necessary to differentiate between malignant and benign biliary strictures to guide clinical management. While direct visualization by cholangioscopy and/or high-resolution imaging by EUS are often the first step in the evaluation of an indeterminate biliary stricture (IDBS), tissue diagnosis by cholangioscopy-guided biopsy and/or EUS-guided fine-needle tissue acquisition is the preferred modality to establish a diagnosis of malignancy. Because each modality has its own strengths and limitations, selection of cholangioscopy and EUS is best guided by the biliary stricture location and local expertise. Artificial intelligence-assisted diagnosis, biopsy forceps with improved design, contrast-enhanced EUS, and dedicated fine-needle biopsy devices are recent technological advances that may further improve the diagnostic performance of cholangioscopy and EUS in patients with IDBS.

Deep Learning and High-Resolution Anoscopy: Development of an Interoperable Algorithm for the Detection and Differentiation of Anal Squamous Cell Carcinoma Precursors-A Multicentric Study

High-resolution anoscopy (HRA) plays a central role in the detection and treatment of precursors of anal squamous cell carcinoma (ASCC). Artificial intelligence (AI) algorithms have shown high levels of efficiency in detecting and differentiating HSIL from low-grade squamous intraepithelial lesions (LSIL) in HRA images. Our aim was to develop a deep learning system for the automatic detection and differentiation of HSIL versus LSIL using HRA images from both conventional and digital proctoscopes. A convolutional neural network (CNN) was developed based on 151 HRA exams performed at two volume centers using conventional and digital HRA systems. A total of 57,822 images were included, 28,874 images containing HSIL and 28,948 LSIL. Partial subanalyses were performed to evaluate the performance of the CNN in the subset of images acetic acid and lugol iodine staining and after treatment of the anal canal. The overall accuracy of the CNN in distinguishing HSIL from LSIL during the testing stage was 94.6%. The algorithm had an overall sensitivity and specificity of 93.6% and 95.7%, respectively (AUC 0.97). For staining with acetic acid, HSIL was differentiated from LSIL with an overall accuracy of 96.4%, while for lugol and after therapeutic manipulation, these values were 96.6% and 99.3%, respectively. The introduction of AI algorithms to HRA may enhance the early diagnosis of ASCC precursors, and this system was shown to perform adequately across conventional and digital HRA interfaces.

Diagnostic accuracy and interobserver agreement of cholangioscopy for indeterminate biliary strictures: A single-center experience

BACKGROUND AND STUDY AIMS

Characterization of indeterminate biliary strictures (IDBS) still represents a major challenge. Digital single-operator cholangioscopy (DSOC) could potentially overcome limits of conventional biopsy and brush sampling. The aim of this study was to compare diagnostic accuracy of visual evaluation and DSOC-guided biopsies to conventional trans-papillary sampling techniques and to evaluate the inter-observer agreement (IOA) on visual diagnosis.

PATIENTS AND METHODS

All consecutive patients undergoing DSOC-guided biopsy after conventional sampling techniques for IDBS during a six-year period were retrospectively evaluated. Final diagnosis was based on histological evaluation of the surgical specimen if available or a clinical follow-up of at least 6 months. For IOA, 20-second DSOC clips were retrospectively reviewed by 6 experts and 6 trainees and classified according to the Monaco Classification.

RESULTS

Thirty-five patients underwent DSOC for IDBS in the study period; 14 patients (F = 9) with a median age of 64 years (range 53-76) met the study aim. After DSOC, strictures location was changed in three patients (additional yield of 21.4 %). Intraductal DSOC-guided biopsy were technically successful in all cases, with an adequacy of 92.8 %. No adverse events were recorded. Final diagnosis was benign disease in five cases and cholangiocarcinoma in the others. For IOA, 29 videos were evaluated with almost perfect agreement for final diagnosis (kappa 0.871; agreement 93.1, p <0.001), although overall accuracy of DSOC visual finding was 73.6 % and 64.4 % for experts and trainees, respectively.

CONCLUSIONS

DSOC could improve diagnostic accuracy for IDBS, since it showed high sensitivity for visual finding and high specificity for DSOC guided-biopsy. Visual diagnosis seems reliable for diagnosis using the Monaco Classification.

Smart Endoscopy Is Greener Endoscopy: Leveraging Artificial Intelligence and Blockchain Technologies to Drive Sustainability in Digestive Health Care

The surge in the implementation of artificial intelligence (AI) in recent years has permeated many aspects of our life, and health care is no exception. Whereas this technology can offer clear benefits, some of the problems associated with its use have also been recognised and brought into question, for example, its environmental impact. In a similar fashion, health care also has a significant environmental impact, and it requires a considerable source of greenhouse gases. Whereas efforts are being made to reduce the footprint of AI tools, here, we were specifically interested in how employing AI tools in gastroenterology departments, and in particular in conjunction with capsule endoscopy, can reduce the carbon footprint associated with digestive health care while offering improvements, particularly in terms of diagnostic accuracy. We address the different ways that leveraging AI applications can reduce the carbon footprint associated with all types of capsule endoscopy examinations. Moreover, we contemplate how the incorporation of other technologies, such as blockchain technology, into digestive health care can help ensure the sustainability of this clinical speciality and by extension, health care in general.

Role of artificial intelligence in the characterization of indeterminate pancreatic head mass and its usefulness in preoperative diagnosis

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.

The 2022 top 10 list of endoscopy topics in medical publishing: an annual review by the American Society for Gastrointestinal Endoscopy Editorial Board

Using a systematic literature search of original articles published during 2022 in Gastrointestinal Endoscopy and other high-impact medical and gastroenterology journals, the 10-member Editorial Board of the American Society for Gastrointestinal Endoscopy composed a list of the 10 most significant topic areas in GI endoscopy during the study year. Each Editorial Board member was directed to consider 3 criteria in generating candidate lists-significance, novelty, and global impact on clinical practice-and subject matter consensus was facilitated by the Chair through electronic voting. The 10 identified areas collectively represent advances in the following endoscopic spheres: artificial intelligence, endoscopic submucosal dissection, Barrett's esophagus, interventional EUS, endoscopic resection techniques, pancreaticobiliary endoscopy, management of acute pancreatitis, endoscopic environmental sustainability, the NordICC trial, and spiral enteroscopy. Each board member was assigned a consensus topic area around which to summarize relevant important articles, thereby generating this précis of the "top 10" endoscopic advances of 2022.

The Role of Cholangioscopy in Biliary Diseases

Endoscopy plays a central role in diagnostic and therapeutic approaches to biliary disease in both benign and malignant conditions. A cholangioscope is an endoscopic instrument that allows for the direct exploration of the biliary tree. Over the years, technology has improved endoscopic image quality and allowed for the development of an operative procedure that can be performed during cholangioscopy. Different types of instruments are available in this context, and they can be used in different anatomical access points according to the most appropriate clinical indication. The direct visualization of biliary mucosa is essential in the presence of biliary strictures of unknown significance, allowing for the appropriate allocation of patients to surgery or conservative treatments. Cholangioscopy has demonstrated excellent performance in discriminating malignant conditions (such as colangiocarcinoma) from benign inflammatory strictures, and more recent advances (e.g., artificial intelligence and confocal laser endomicroscopy) could further increase its diagnostic accuracy. Cholangioscopy also plays a primary role in the treatment of benign conditions such as difficult bile stones (DBSs). In this case, it may not be possible to achieve complete biliary drainage using standard ERCP. Therapeutic cholangioscopy-guided lithotripsy allows for stone fragmentation and complete biliary drainage. Indeed, other complex clinical situations, such as patients with intra-hepatic lithiasis and patients with an altered anatomy, could benefit from the therapeutic role of cholangioscopy. The aim of the present review is to explore the most recent diagnostic and therapeutic advances in the roles of cholangioscopy in the management of biliary diseases.

Artificial intelligence in endoscopy: Overview, applications, and future directions

Since the emergence of artificial intelligence (AI) in medicine, endoscopy applications in gastroenterology have been at the forefront of innovations. The ever-increasing number of studies necessitates the need to organize and classify applications in a useful way. Separating AI capabilities by computer aided detection (CADe), diagnosis (CADx), and quality assessment (CADq) allows for a systematic evaluation of each application. CADe studies have shown promise in accurate detection of esophageal, gastric and colonic neoplasia as well as identifying sources of bleeding and Crohn's disease in the small bowel. While more advanced CADx applications employ optical biopsies to give further information to characterize neoplasia and grade inflammatory disease, diverse CADq applications ensure quality and increase the efficiency of procedures. Future applications show promise in advanced therapeutic modalities and integrated systems that provide multimodal capabilities. AI is set to revolutionize clinical decision making and performance of endoscopy.

A real-time interpretable artificial intelligence model for the cholangioscopic diagnosis of malignant biliary stricture (with videos)

BACKGROUND AND AIMS

It is crucial to accurately determine malignant biliary strictures (MBSs) for early curative treatment. This study aimed to develop a real-time interpretable artificial intelligence (AI) system to predict MBSs under digital single-operator cholangioscopy (DSOC).

METHODS

A novel interpretable AI system called MBSDeiT was developed consisting of 2 models to identify qualified images and then predict MBSs in real time. The overall efficiency of MBSDeiT was validated at the image level on internal, external, and prospective testing data sets and subgroup analyses, and at the video level on the prospective data sets; these findings were compared with those of the endoscopists. The association between AI predictions and endoscopic features was evaluated to increase the interpretability.

RESULTS

MBSDeiT can first automatically select qualified DSOC images with an area under the curve (AUC) of .963 and .968 to .973 on the internal testing data set and the external testing data sets, and then identify MBSs with an AUC of .971 on the internal testing data set, an AUC of .978 to .999 on the external testing data sets, and an AUC of .976 on the prospective testing data set, respectively. MBSDeiT accurately identified 92.3% of MBSs in prospective testing videos. Subgroup analyses confirmed the stability and robustness of MBSDeiT. The AI system achieved superior performance to that of expert and novice endoscopists. The AI predictions were significantly associated with 4 endoscopic features (nodular mass, friability, raised intraductal lesion, and abnormal vessels; P < .05) under DSOC, which is consistent with the endoscopists' predictions.

CONCLUSIONS

The study findings suggest that MBSDeiT could be a promising approach for the accurate diagnosis of MBSs under DSOC.

Artificial intelligence for diagnosing neoplasia on digital cholangioscopy: development and multicenter validation of a convolutional neural network model

BACKGROUND

We aimed to develop a convolutional neural network (CNN) model for detecting neoplastic lesions during real-time digital single-operator cholangioscopy (DSOC) and to clinically validate the model through comparisons with DSOC expert and nonexpert endoscopists.

METHODS

In this two-stage study, we first developed and validated CNN1. Then, we performed a multicenter diagnostic trial to compare four DSOC experts and nonexperts against an improved model (CNN2). Lesions were classified into neoplastic and non-neoplastic in accordance with Carlos Robles-Medranda (CRM) and Mendoza disaggregated criteria. The final diagnosis of neoplasia was based on histopathology and 12-month follow-up outcomes.

RESULTS

In stage I, CNN2 achieved a mean average precision of 0.88, an intersection over the union value of 83.24 %, and a total loss of 0.0975. For clinical validation, a total of 170 videos from newly included patients were analyzed with the CNN2. Half of cases (50 %) had neoplastic lesions. This model achieved significant accuracy values for neoplastic diagnosis, with a 90.5 % sensitivity, 68.2 % specificity, and 74.0 % and 87.8 % positive and negative predictive values, respectively. The CNN2 model outperformed nonexpert #2 (area under the receiver operating characteristic curve [AUC]-CRM 0.657 vs. AUC-CNN2 0.794, P < 0.05; AUC-Mendoza 0.582 vs. AUC-CNN2 0.794, P < 0.05), nonexpert #4 (AUC-CRM 0.683 vs. AUC-CNN2 0.791, P < 0.05), and expert #4 (AUC-CRM 0.755 vs. AUC-CNN2 0.848, P < 0.05; AUC-Mendoza 0.753 vs. AUC-CNN2 0.848, P < 0.05).

CONCLUSIONS

The proposed CNN model distinguished neoplastic bile duct lesions with good accuracy and outperformed two nonexpert and one expert endoscopist.

Cholangioscopy for biliary diseases

PURPOSE OF REVIEW

Cholangioscopy is a mini-invasive endoscopic procedure, which consists in a direct intraductal visualization of the biliary tract. The purpose of this review is to summarize the technique, the clinical applications, as well as future perspectives of cholangioscopy.

RECENT FINDINGS

Numerous technologic advances during the last decades have allowed for an improved utility and functionality, leading to a broader use of this procedure, for diagnostic or therapeutic purposes, in the setting of biliary diseases. Novel tools and emerging indications have been developed and more are yet to come.

SUMMARY

Cholangioscopy can be performed by peroral, percutaneous transhepatic or intra-operative transcystic or transcholedochal access. Clinical applications of cholangioscopy are multiple, ranging from visual impression and optical guided biopsies of indeterminate biliary strictures to the management of difficult stones , guidance before biliary stenting and retrieval of migrated ductal stents. Multiple devices such as lithotripsy probes, biopsy forceps, snares and baskets have been developed to help achieve these procedures successfully.Cholangioscopy has improved the way biliary diseases can be visualized and treated. New technology, accessories, and applications are expected in the future.

Identification of patients with malignant biliary strictures using a cholangioscopy-based deep learning artificial intelligence (with video)

BACKGROUND AND AIMS

Accurately diagnosing malignant biliary strictures (MBSs) as benign or malignant remains challenging. It has been suggested that direct visualization and interpretation of cholangioscopy images provide greater accuracy for stricture classification than current sampling techniques (ie, brush cytology and forceps biopsy sampling) using ERCP. We aimed to develop a convolutional neural network (CNN) model capable of accurate stricture classification and real-time evaluation based solely on cholangioscopy image analysis.

METHODS

Consecutive patients with cholangioscopy examinations from 2012 to 2021 were reviewed. A CNN was developed and tested using cholangioscopy images with direct expert annotations. The CNN was then applied to a multicenter, reserved test set of cholangioscopy videos. CNN performance was then directly compared with that of ERCP sampling techniques. Occlusion block heatmap analyses were used to evaluate and rank cholangioscopy features associated with MBSs.

RESULTS

One hundred fifty-four patients with available cholangioscopy examinations were included in the study. The final image database comprised 2,388,439 still images. The CNN demonstrated good performance when tasked with mimicking expert annotations of high-quality malignant images (area under the receiver-operating characteristic curve, .941). Overall accuracy of CNN-based video analysis (.906) was significantly greater than that of brush cytology (.625, P = .04) or forceps biopsy sampling (.609, P = .03). Occlusion block heatmap analysis demonstrated that the most frequent image feature for an MBS was the presence of frond-like mucosa/papillary projections.

CONCLUSIONS

This study demonstrates that a CNN developed using cholangioscopy data alone has greater accuracy for biliary stricture classification than traditional ERCP-based sampling techniques.

Current endoscopic approaches to biliary strictures

PURPOSE OF REVIEW

To summarize the current status and future perspectives of the endoscopic management of biliary strictures.

RECENT FINDINGS

In addition to conventional diagnostic modalities, such as cross-sectional imaging and endoscopic ultrasonography (EUS), per-oral cholangioscopy is helpful for indeterminate biliary strictures. It allows direct visualization of the biliary tract and targeted biopsy. For distal malignant biliary obstruction (MBO), a self-expandable metal stent (SEMS) via endoscopic retrograde cholangiopancreatography (ERCP) is a standard of care. EUS-guided biliary drainage (EUS-BD) is an emerging alternative to percutaneous transhepatic biliary drainage in cases with failed ERCP. EUS-BD is also an effective salvage option for perihilar MBO, which can not be managed via ERCP or percutaneous transhepatic biliary drainage. Preoperative drainage is necessary for most jaundiced patients as neoadjuvant chemotherapy is widely administered for resectable and borderline resectable pancreatic cancer, and a SEMS is preferred in this setting, too. For benign biliary strictures, a covered SEMS can improve stricture resolution and reduce the number of endoscopic sessions as compared to plastic stents.

SUMMARY

ERCP and EUS play a central role in the diagnosis and drainage for both malignant and benign biliary strictures.

Multidisciplinary Approach to Indeterminate Biliary Strictures

Biliary strictures that remain unclassified after cross-sectional imaging and endoscopic retrograde cholangiopancreatography-based tissue sampling are defined as indeterminate biliary strictures (IDBS). A substantial proportion of biliary strictures fall into this category due to low sensitivity of brush cytology and intraductal biopsy. Over last few decades, several modalities have emerged for the evaluation of IDBS. Of these, cholangioscopy and endosonography are the frontrunners and have cemented their place for the evaluation of IDBS. Both of these modalities are widely available, and therefore, biliary strictures that remain uncharacterized after their utilization represent IDBS in the current era.

Artificial intelligence in the endoscopic approach of biliary tract diseases: A current review

In recent years there have been major developments in the field of artificial intelligence. The different areas of medicine have taken advantage of this tool to make various diagnostic and therapeutic methods more effective, safe, and user-friendly. In this way, artificial intelligence has been an increasingly present reality in medicine. In the field of Gastroenterology, the main application has been in the detection and characterization of colonic polyps, but an increasing number of studies have been published on the application of deep learning systems in other pathologies of the gastrointestinal tract. Evidence of the application of artificial intelligence in the assessment of biliary tract is still scarce. Some studies support the usefulness of these systems in the investigation and treatment of choledocholithiasis, demonstrating that they have the potential to be integrated into clinical practice and endoscopic procedures, such as endoscopic retrograde cholangiopancreatography. Its application in cholangioscopy for the investigation of undetermined biliary strictures also seems to be promising. Assessing the bile duct through endoscopic ultrasound can be challenging, especially for less experienced operators, thus becoming an area of potential interest for artificial intelligence. In this review, we summarize the state of the art of artificial intelligence in the endoscopic diagnosis and treatment of biliary diseases.

Cholangioscopy: Has It Changed Management?

The single operator per oral cholangioscope is a catheter-based system that allows for direct visualization of the bile duct and pancreatic duct. The instrument with its improved imaging technique and larger accessory channel allows for high-quality image acquisition and performance of therapeutic and diagnostic procedures within the bile duct and pancreatic duct. There has been an increase in the range of indications for the use of the cholangioscope. The current indications include management of difficult biliary stones, pancreatic calculi, assessment of indeterminate biliary stricture, pancreatic stricture, intra-ductal papillary mucinous neoplasms, and extractions of proximally migrated stents. The use of laser lithotripsy and electro-hydraulic lithotripsy has improved the management of difficult bile duct stones. Direct visualization of biliary and pancreatic duct strictures is helpful in the diagnosis of indeterminate strictures. In this review, we explore how cholangioscopy has changed management.

Deep Learning and Device-Assisted Enteroscopy: Automatic Detection of Gastrointestinal Angioectasia

Background and Objectives: Device-assisted enteroscopy (DAE) allows deep exploration of the small bowel and combines diagnostic and therapeutic capacities. Suspected mid-gastrointestinal bleeding is the most frequent indication for DAE, and vascular lesions, particularly angioectasia, are the most common etiology. Nevertheless, the diagnostic yield of DAE for the detection of these lesions is suboptimal. Deep learning algorithms have shown great potential for automatic detection of lesions in endoscopy. We aimed to develop an artificial intelligence (AI) model for the automatic detection of angioectasia DAE images. Materials and Methods: A convolutional neural network (CNN) was developed using DAE images. Each frame was labeled as normal/mucosa or angioectasia. The image dataset was split for the constitution of training and validation datasets. The latter was used for assessing the performance of the CNN. Results: A total of 72 DAE exams were included, and 6740 images were extracted (5345 of normal mucosa and 1395 of angioectasia). The model had a sensitivity of 88.5%, a specificity of 97.1% and an AUC of 0.988. The image processing speed was 6.4 ms/frame. Conclusions: The application of AI to DAE may have a significant impact on the management of patients with suspected mid-gastrointestinal bleeding.