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

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.

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.

[A look into the neighboring discipline: eHealth in oncology]

Digitalization is dramatically changing the entire healthcare system. Keywords such as artificial intelligence, electronic patient files (ePA), electronic prescriptions (eRp), telemedicine, wearables, augmented reality and digital health applications (DiGA) represent the digital transformation that is already taking place. Digital becomes real! This article outlines the state of research and development, current plans and ongoing uses of digital tools in oncology in the first half of 2024. The possibilities for using artificial intelligence and the use of DiGAs in oncology are presented in more detail in this overview according to their stage of development as they already show a noticeable benefit in oncology.

Deep Learning for Automatic Diagnosis and Morphologic Characterization of Malignant Biliary Strictures Using Digital Cholangioscopy: A Multicentric Study

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.

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.

Usefulness of artificial intelligence-assisted digital single-operator cholangioscopy as a second-opinion consultation tool during interhospital assessment of an indeterminate biliary stricture: a case report

Video 1Application of a novel digital single-operator cholangioscopy-based convolutional neuronal network to detect neoplastic lesions as a second-opinion consultation tool between 2 hospitals and biopsy sampling.