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[使用基于CT和临床特征的深度学习融合模型在原发性骨瘤中识别骨和软骨质矩阵矿化:多中心回顾性研究]

Caolin Liu¹, Qingqing Zou², Menghong Wang¹

¹Department of Radiology, Third Affiliated Hospital of Southern Medical University (Academy of Orthopedics of Guangdong Province), Guangzhou 510630, China.

Nan fang yi ke da xue xue bao = Journal of Southern Medical University

|December 26, 2024

概括

一个新的深度学习融合模型 (SC-Net) 准确地使用CT扫描和临床数据对原发性骨瘤进行分类. 这种先进的人工智能模型显示了提高骨质原性vs. chondrogenic瘤的诊断准确性的潜力.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
在瘤学瘤学.

背景情况:

精确区分骨质原发性和骨质原发性原发性骨瘤对于有效治疗至关重要.
目前的诊断方法在区分这些瘤类型方面可能受到限制.

研究的目的:

开发和评估一个深度学习融合模型 (SC-Net) 来分类主要骨瘤.
将SC-Net的诊断性能与现有模型和放射科医生进行比较.

主要方法:

从四个医疗中心收集了276名原发性骨瘤患者的CT扫描数据.
基于卷积神经网络 (CNN) 的深度学习模型 (SC-Net) 的开发,集成成像和临床特征.
使用接收器操作特征曲线 (AUC) 下的面积和F1分数来评估诊断性能.

主要成果:

在外部测试组中,SC-Net融合模型实现了0.901的AUC和0.857的F1得分.
与放射科医生相比,SC-Net的表现优于基线深度学习模型 (S-Net) 的表现,并且表现出比放射科医生更好的分类性能.
该模型在分类原发性骨瘤方面实现了83.7%的准确性.

结论:

开发的深度学习融合模型 (SC-Net) 在主要骨瘤中对骨和冠状腺基质矿化进行分类时表现出高准确性.
SC-Net有可能提高骨质原始骨瘤与体原始骨瘤的诊断准确性.
多中心CT图像和临床特征的整合显著改善了骨瘤分类的深度学习模型性能.

关键词:

冠状腺母线矿化矿化计算机断层扫描 (CT) 是一种计算机断层扫描.深度学习是一种深度学习.骨质状矩阵矿化矿化原发性骨瘤是一个主要的骨瘤.

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