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

IF 2.2 Q3 GASTROENTEROLOGY & HEPATOLOGY
Endoscopy International Open Pub Date : 2024-10-10 eCollection Date: 2024-10-01 DOI:10.1055/a-2404-5699
Carlos Robles-Medranda, Jorge Baquerizo-Burgos, Miguel Puga-Tejada, Domenica Cunto, Maria Egas-Izquierdo, Juan Carlos Mendez, Martha Arevalo-Mora, Juan Alcivar Vasquez, Hannah Lukashok, Daniela Tabacelia
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Abstract

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.

基于胆道镜的卷积神经元网络与共聚焦激光内窥镜在识别肿瘤性胆道狭窄中的对比。
背景和研究目的 人工智能(AI)模型在数字单刀胆道镜(DSOC)检查中识别肿瘤方面表现出了很高的诊断性能。迄今为止,还没有研究将人工智能与 DSOC 引导的探针基共焦点激光内窥镜(DSOC-pCLE)进行直接比较。因此,我们旨在比较基于 DSOC 的人工智能模型与 DSOC-pCLE 在确定胆道狭窄患者肿瘤方面的诊断准确性。患者和方法 这项基于队列的回顾性诊断准确性研究纳入了接受 DSOC 和 DSOC-pCLE 的年龄≥ 18 岁的患者(2014 年 6 月至 2022 年 5 月)。四种方法用于诊断每位患者的胆道结构,包括 DSOC 直接观察、DSOC-pCLE、在 DSOC 记录中进行的基于 DSOC 的离线人工智能模型分析以及 DSOC/pCLE 引导下的活检。肿瘤的参考标准是在 12 个月的随访期间根据进一步的临床演变、影像学或手术标本结果做出的诊断。结果 共有90名患者被纳入研究。其中 86 例(95.5%)为肿瘤性病变,包括胆管癌(98.8%)和输卵管乳头状腺瘤(1.2%)。四例为炎症性病变,包括两例慢性炎症和两例原发性硬化性胆管炎。与接收者运算曲线下面积(AUC)为 0.79 的 DSOC-AI 相比,DSOC 直接显像的 AUC 为 0.74 ( P = 0.763),DSOC-pCLE 的 AUC 为 0.72 ( P = 0.634),DSOC 和 pCLE 引导活检的 AUC 为 0.83 ( P = 0.809)。结论 DSOC-AI 模型的离线诊断性能与 DSOC-pCLE、单独 DSOC 和 DSOC/pCLE 引导的活检相似。为了证实所获得的结果,最好进行更大规模的多中心、前瞻性、头对头试验,并在肿瘤性和非肿瘤性病例中按比例抽取样本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Endoscopy International Open
Endoscopy International Open GASTROENTEROLOGY & HEPATOLOGY-
自引率
3.80%
发文量
270
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