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机器学习算法用于预测Cobb角超过25度的女性青少年异常学脊椎病患者

Shuhei Ohyama¹, Satoshi Maki¹, Toshiaki Kotani²

¹Department of Orthopedic Surgery, Graduate School of Medicine, Chiba University, Chiba, Japan.

|March 13, 2024

概括

这项研究开发了一种机器学习模型,使用初始患者数据来预测青少年异常学脊椎病 (AIS) 的进展. 该模型准确预测曲线的进展,帮助早期干预策略.

科学领域:

整形外科和运动医学
生物医学工程生物医学工程
在医疗保健中的数据科学.

背景情况:

预测青少年异常学脊椎病 (AIS) 曲线的进展是具有挑战性的.
早期识别潜在的进展对于及时干预至关重要.

研究的目的:

开发一个机器学习 (ML) 模型来预测AIS的进展.
使用最少的放射和问卷数据从第一次访问.

主要方法:

对1119名女性AIS患者进行了回顾性队列研究.
开发了对近距离胸部 (PT),主要胸部 (MT) 和胸腔/腰部 (TLL) 曲线的二元分类ML模型.
使用曲线下面面积 (AUC) 和回忆得分的优化模型.

主要成果:

获得的高AUC值为:0.94 (PT),0.89 (MT),0.84 (TLL).
获得的召回率很高:0.90 (PT),0.85 (MT),0.81 (TLL).
确定了关键预测因素:初始的科布角度 (PT),初始期 (MT),瑞塞等级 (TLL).

结论:

一个有效的ML模型可以预测AIS角度进展.
第一次访问时的最小数据足以进行准确的预测.
为AIS提供了个性化治疗策略.

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