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DOI 10.14309/ctg.0000000000001015

Медицинская библиотека · Clinical and translational gastroenterology · 2026

Clinical Validation of AI-Assisted Evaluation of Indeterminate Biliary Strictures in Digital-Single Operator Cholangioscopy: A Transcontinental Multicentric Study.

Mascarenhas Miguel, Widmer Jessica, Mendes Francisco, Ribeiro Tiago, Pinto da Costa Antonio, Agudo Belén, Martins Miguel, Afonso João, Mota Joana, Almeida Maria João, Cardoso Pedro, Frias Joana, Araújo Catarina, Cardoso Hélder, Plaza González Maria, Pérez-González Ana, Lera Dos Santos Marcos Eduardo, Moris Maria, Garcia de Paredes Ana Garcia, Foruny José, Bicudo de Oliveira Luiza, Ferreira de Carvalho Matheus, Maluf-Filho Fauze, Ferreira Maria Clara, Prince Tomazo, Velasquez Andrea, González Ivan Enrique, Ferreira João, Kim Grace E, Siddiqui Uzma D, Omrani Laleh R, Alrossais Naif, Keegan Mathew, Aslam Perveen, Aggarwal Vipul, Vilas-Boas Filipe, Pereira Pedro, Sabbagh Luis Carlos, Almuhaidb Aymen, Guimarães Hourneaux De Moura Eduardo, Macedo Guilherme, González-Haba Mariano

СтатьяМногоцентровое исследованиеВалидационное исследованиеПеревод пока не подготовлен

Аннотация

INTRODUCTION

Biliary strictures (BS) are a significant challenge, with malignant strictures frequently diagnosed at advanced stages, limiting curative options. Digital single-operator cholangioscopy (D-SOC) enables high-resolution, direct visualization of the bile duct, yet with suboptimal accuracy. Artificial intelligence (AI) has shown promise for detection and differentiation of BS in frame-level analysis and small clinical series. This study aimed to validate a deep learning model for AI-assisted D-SOC image analysis.

METHODS

This multicenter study included 135 D-SOC examinations from 129 patients (61 with malignant BS) across 14 centers in the United States, Brazil, Spain, Colombia, Australia, and Saudi Arabia. For each examination, up to 25 clinically relevant frames were selected and uploaded to a web-based platform for AI analysis. The model performed both detection and differentiation of BS: Detection was assessed by comparing AI-generated bounding boxes with expert-defined annotations using intersection-over-union (IoU), while differentiation was benchmarked against histopathology. Performance metrics included accuracy, sensitivity, specificity, and positive and negative predictive values.

RESULTS

At the patient level, malignant BS were identified with 86.0% accuracy, 84.1% sensitivity, and 85.7% specificity, with an area under the curve of 0.904. The model demonstrated robust detection performance, achieving a mean IoU of 70.3%. Performance was maintained across demographic variables and centers.

DISCUSSION: This first multicentric validation study demonstrates real-world performance of AI-assisted D-SOC analysis across multiples continents and devices, with robust accuracy for BS detection and differentiation. These findings support AI as an adjunctive tool in D-SOC, enhancing a more accurate evaluation of patients with indeterminate BS.

Для цитирования: Mascarenhas Miguel, Widmer Jessica, Mendes Francisco, Ribeiro Tiago, Pinto da Costa Antonio, Agudo Belén, Martins Miguel, Afonso João, Mota Joana, Almeida Maria João, Cardoso Pedro, Frias Joana, Araújo Catarina, Cardoso Hélder, Plaza González Maria, Pérez-González Ana, Lera Dos Santos Marcos Eduardo, Moris Maria, Garcia de Paredes Ana Garcia, Foruny José, Bicudo de Oliveira Luiza, Ferreira de Carvalho Matheus, Maluf-Filho Fauze, Ferreira Maria Clara, Prince Tomazo, Velasquez Andrea, González Ivan Enrique, Ferreira João, Kim Grace E, Siddiqui Uzma D, Omrani Laleh R, Alrossais Naif, Keegan Mathew, Aslam Perveen, Aggarwal Vipul, Vilas-Boas Filipe, Pereira Pedro, Sabbagh Luis Carlos, Almuhaidb Aymen, Guimarães Hourneaux De Moura Eduardo, Macedo Guilherme, González-Haba Mariano. Clinical Validation of AI-Assisted Evaluation of Indeterminate Biliary Strictures in Digital-Single Operator Cholangioscopy: A Transcontinental Multicentric Study.. Clinical and translational gastroenterology. 2026. DOI: 10.14309/ctg.0000000000001015.

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