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基于胆血管镜的卷曲神经网络与共聚焦激光内显微镜对瘤胆道狭窄的识别

Carlos Robles-Medranda¹, Jorge Baquerizo-Burgos¹, Miguel Puga-Tejada¹

¹Gastroenterology, Instituto Ecuatoriano de Enfermedades Digestivas - IECED, Guayaquil, Ecuador.

Endoscopy international open

|October 14, 2024

概括

一个人工智能 (AI) 模型的诊断性能与基于探针的共聚焦激光内显微镜 (pCLE) 类似,用于检测胆道狭窄的瘤. 需要进一步的试验来证实这些发现,用于AI在胆血管镜检查.

科学领域:

胃肠病学和肝病学胃肠学和肝病学
医疗成像医学成像
人工智能在医学中的应用

背景情况:

人工智能 (AI) 模型在数字单操作员胆管透视 (DSOC) 期间诊断瘤方面表现有前途.
没有任何研究直接将人工智能与基于DSOC引导的探针的共聚焦激光内显微镜 (DSOC-pCLE) 对瘤检测进行了比较.

研究的目的:

为了比较基于DSOC的AI模型与DSOC-pCLE的诊断准确度,在确定不确定的胆道狭窄内识别瘤.

主要方法:

对90名接受DSOC和DSOC-pCLE的患者进行了回顾性队列研究 (2014年6月至2022年5月).
诊断方法包括DSOC可视化,DSOC-pCLE,DSOC记录的离线AI分析,以及DSOC/pCLE引导的活检.
通过使用临床,成像或手术数据进行12个月的随访来证实瘤诊断.

主要成果:

95.5%的患者患有瘤病变 (主要是胆管癌).
DSOC-AI模型实现了0.79.7的接收机操作员曲线 (AUC) 下的面积.
性能与DSOC直接可视化 (AUC 0.74),DSOC-pCLE (AUC 0.72) 和DSOC/pCLE导向活检 (AUC 0.83) 相比.

结论:

DSOC-AI模型展示了离线诊断性能,与DSOC-pCLE和活检等既定方法相美.
建议进行更大规模的,前性的多中心试验,以验证这些发现,并探索AI在胆道狭窄诊断中的作用.

关键词:

胆透镜检查是一种胆透镜检查.胰腺胆性疾病 (ERCP/PTCD) 是一种严格的限制严格的限制组织诊断组织诊断

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