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基于深度学习细分模型的甲状腺回声焦点的辅助诊断:一项两中心研究

Yuanzhen Liu¹, Chen Chen², Kai Wang³

¹Department of Diagnostic Ultrasound Imaging and Interventional Therapy, Zhejiang Cancer Hospital, Hangzhou, Zhejiang 310022, China; Wenling Big Data and Artificial Intelligence Institute in Medicine, Taizhou, Zhejiang 317502, China; Hangzhou Institute of Medicine (HIM), Chinese Academy of Sciences, Hangzhou, Zhejiang 310018, China.

European journal of radiology

|August 18, 2023

概括

这项研究开发了一种人工智能辅助的深度学习模型,以帮助医生区分甲状腺结节. 人工智能工具显著提高了所有经验水平的放射科医生的诊断性能,在识别回声焦点时.

科学领域:

医疗成像医学成像
人工智能的人工智能
在瘤学瘤学.

背景情况:

区分恶性和良性甲状腺结节与回声发作焦点是具有挑战性的.
目前的诊断方法在很大程度上依赖于放射科医生的专业知识,这可能有所不同.
人工智能辅助工具在提高诊断准确性方面表现有前途.

研究的目的:

开发和评估一个人工智能辅助的诊断软件,使用深度学习 (DL) 模型进行甲状腺结节分类.
评估这种人工智能工具对具有不同经验水平的放射科医生的诊断性能的影响.
为了改善恶性与良性甲状腺结节的识别,具有回声发作焦点.

主要方法:

来自两个机构的2724次超声波 (美国) 扫描的回顾性分析.
开发和比较三种DL细分模型 (DeepLabV3+,U-Net,PSPNet) 用于具有回声焦点的结节区域.
使用接收器运行特征曲线 (AUROC) 下面的面积 (用和没有人工智能辅助) 评估诊断性能.

主要成果:

使用MobileNetV2骨干的DeepLabV3+模型实现了最佳的细分性能 (MIoU=0.891,MPA=0.945,Dice=0.919).
人工智能辅助显著改善了放射学初学者的AUROC (0.693比0.551).
初级和高级医生在人工智能协助下改善了诊断性能,AUROC,准确性,灵敏度和特异性显著增加.

结论:

基于DL的辅助诊断模型利用美国静态图像可以提高放射科医生的性能.
这款人工智能辅助的软件有助于准确识别甲状腺回声发作焦点.
这项技术有可能改善甲状腺结节的诊断结果.

关键词:

辅助诊断是一种辅助诊断.深度学习是一种深度学习.甲状腺回声焦点是甲状腺的回声焦点.甲状腺结节甲状腺结节超声波超声波是指超声波的使用.

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The Thyroid Gland

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