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创建一个多式人工智能模型来区分甲状腺的良性和恶性卵泡瘤:一项概念验证研究

Ann C Lin¹, Zelong Liu², Justine Lee³

¹Department of Surgery, Icahn School of Medicine at Mount Sinai, New York City, NY.

|November 4, 2023

概括

一个新的多式机器学习模型在使用甲状腺超声波图像区分卵泡癌和腺瘤方面表现出有希望. 这种方法可以帮助在手术前诊断甲状腺瘤.

科学领域:

医学成像分析分析医学成像分析
机器学习在诊断中的应用.
甲状腺结节分类的分类

背景情况:

机器学习 (ML) 提高了医疗诊断,特别是甲状腺超声波图像分类.
毛囊性甲状腺新生瘤是一个诊断挑战,需要区分腺瘤和癌症.

研究的目的:

开发一种多式机器学习模型,用于将卵泡癌与腺瘤分类.
评估该模型在手术前分化甲状腺瘤中的有效性.

主要方法:

对患有卵泡腺瘤或癌瘤的患者进行了回顾性研究 (2010-2022年).
超声波图像的放射学分析与临床变量相结合.
随机森林分类器开发并使用leave-one-out交叉验证进行评估.

主要成果:

一个多式模式的模型实现了接收器运行特征曲线 (AUC) 下的面积为0.79.
仅仅临床变量就产生了0.76的AUC,而仅仅图像特征就产生了0.29.
该模型整合了910个提取的图像特征和临床数据.

结论:

多式机器学习模型显示了分类卵泡癌从腺瘤的潜力.
这种方法可以改善卵泡甲状腺新生体的手术前分化.

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