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开发和验证深度学习和放射学组合模型,以区分复杂和无复杂的急性尾炎

Dan Liang¹, Yaheng Fan², Yinghou Zeng²

¹First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, People's Republic of China (D.L.); Department of Radiology, Guangzhou First People's Hospital, School of Medicine, South China University of Technology, Guangzhou, Guangdong, People's Republic of China (D.L., Y.L., D.C., A.C., J.D., X.W.).

Academic radiology

|September 29, 2023

概括

一个新的深度学习和放射学模型准确地区分了复杂的和不复杂的急性尾炎 (AA). 这种由人工智能驱动的方法比AA评估的传统方法提供了更好的诊断性能.

科学领域:

放射学和医学成像学医学成像学
人工智能在医学中的应用
计算病理学计算病理学

背景情况:

诊断急性尾炎 (AA) 可能具有挑战性,复杂的病例需要及时干预.
区分复杂和无复杂的AA对于适当的患者管理和手术规划至关重要.

研究的目的:

开发和验证一个结合深度学习和放射学模型,以区分复杂和不复杂的急性尾炎 (AA).
评估新型模型的诊断性能与传统方法和放射科医生的解释相比.

主要方法:

一项回顾性多中心研究涉及1165名成人AA患者,他们接受了腹部盆腔CT扫描.
开发基于CatBoost的模型,整合临床,CT视觉,深度学习和放射学特征.
外部验证和与常规组合模型,DLR模型和放射科医生视觉诊断使用ROC分析进行比较.

主要成果:

组合模型在培训队列中实现了0.816的AUC,并在验证队列中表现出强的表现 (AUC=0.799).
该模型的表现优于传统的组合模型 (AUC=0.723),DLR模型 (AUC=0.755) 和放射科医生诊断 (AUC=0.679) (P <0.05).
决策曲线分析表明,在预测复杂的AA时,组合模型的净收益优越.

结论:

开发的综合深度学习和放射学模型能够准确区分复杂和不复杂的AA.
这种由人工智能驱动的工具显示出在急性尾炎病例中改善诊断准确性的巨大潜力.

关键词:

急性尾炎是什么情况复杂的尾炎是一个复杂的疾病.深度学习是一种深度学习.无线电学 (Radiomics) 是一种无线电学.

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