利用人工智能用于在血友病中检测出血关节炎,使用护理点超声波
Pascal N Tyrrell1,2,3, María Teresa Alvarez-Román4, Nihal Bakeer5,6
1Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada.
Research and practice in thrombosis and haemostasis
|December 16, 2024
概括
人工智能和超声波可以帮助检测血友病患者的关节出血. 这项技术有助于早期识别和治疗关节炎,改善患者的治疗结果.
科学领域:
- 医学技术 医学技术 医学技术
- 医疗保健中的人工智能
- 诊断成像诊断成像的使用
背景情况:
- 血友病的治疗已经取得了进展,但复发性血关节炎和血友性关节病仍然是致病的重要原因.
- 通过体检或患者报告的疼痛,精确识别关节出血可能是具有挑战性的,特别是随着关节病变的进展.
研究的目的:
- 在超声波 (美国) 图像上开发和评估人工智能 (AI) 算法的可靠性,以检测突间隙膨胀 (SRD).
- 使提供者和患者能够在床边和家中使用人工智能驱动的US.检测关节出血.
主要方法:
- 训练有素的人工智能算法使用来自7个国际医疗保健中心的12,145次联合考试的61,501张美国图像数据集.
- 包括健康个体和患有血友病的儿科/成人患者,有或没有SRD.
- 利用卷积神经网络分类器和细分模型,使用专家标记的图像.
主要成果:
- 人工智能算法在所有关节和队列中表现出高性能.
- 膝盖模型实现了97%的准确性,96%的灵敏度,97%的特异性,SRD检测的AUC为0.97.
- 对于细分任务,获得了高子系数 (80%-85%).
结论:
- 由人工智能驱动的美国技术显示出对在血友病中早期检测血液性关节炎的前景.
- 这种方法可以帮助及时管理关节出血,可能降低发病率.
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