Pereira Pedro, Mascarenhas Miguel, Ribeiro Tiago, Afonso João, Ferreira João P S, Vilas-Boas Filipe, Parente Marco P L, Jorge Renato N, Macedo Guilherme
Department of Gastroenterology, São João University Hospital, Porto, Portugal.
WGO Gastroenterology and Hepatology Training Center, Porto, Portugal.
Endosc Int Open. 2022 Mar 14;10(3):E262-E268. doi: 10.1055/a-1723-3369. eCollection 2022 Mar.
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. 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. 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. 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.
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. 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. 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. 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.
不确定的胆管狭窄带来了重大的临床挑战。在数字单操作者胆管镜检查（D-SOC）期间，扩张、不规则且迂曲的血管（常被描述为肿瘤血管）在具有高恶性潜能的胆管狭窄中经常被报道。近年来，用于内镜实践的人工智能（AI）算法的开发受到了深入研究。我们旨在开发一种用于自动检测D-SOC图像中肿瘤血管（TVs）的AI算法。开发了一种卷积神经网络（CNN）。纳入了85例接受D-SOC（Spyglass，美国波士顿科学公司，马萨诸塞州马尔伯勒）的患者的总共6475张图像。对每一帧进行肿瘤血管存在情况的评估。通过计算曲线下面积（AUC）、敏感性、特异性、阳性和阴性预测值来衡量CNN的性能。敏感性、特异性、阳性预测值和阴性预测值分别为99.3%、99.4%、99.6%和98.7%。AUC为1.00。我们的CNN能够高精度地检测肿瘤血管。AI算法的开发可能会增强对与胆管恶性肿瘤高可能性相关的宏观特征的检测，从而优化对不确定胆管狭窄患者诊断检查。
