使用基于姿势的深度学习模型对有或没有宫性头痛或部和肩部疼痛的办公室工作者进行分类:一项多中心回顾性研究
Ui-Jae Hwang1, Junghun Han2, Oh-Yun Kwon3
1Department of Rehabilitation Sciences, The Hong Kong Polytechnic University, Hong Kong SAR, China.
Frontiers in pain research (Lausanne, Switzerland)
|July 22, 2025
概括
深度学习模型可以从坐姿图像中分类宫性头痛 (CH) 和部/肩部疼痛 (NSP). ResNet50对CH有希望,突出了早期检测肌肉骨疾病的潜力.
科学领域:
- 生物医学工程 生物医学工程
- 计算机科学 计算机科学
- 肌肉骨健康 肌肉骨健康
背景情况:
- 办公室工作者经常经历宫性头痛 (CH) 和部/肩部疼痛 (NSP).
- 习惯的坐姿是导致这些情况的潜在因素.
- 需要客观的方法来识别姿势风险.
研究的目的:
- 开发和评估深度学习模型,用于对具有或没有CH和/或NSP的办公室工作者进行分类.
- 分析习惯性坐姿图像,寻找与这些情况相关的独特模式.
主要方法:
- 一项多中心的回顾性研究分析了531名办公室工作者的904张坐姿图像.
- 三种深度学习模型 (VGG19,ResNet50,EfficientNet B5) 被训练来分类CH,NSP,以及合并的CH+NSP.
- 使用AUC,精度,灵敏度,特异性和F1得分来评估性能,并使用类激活映射 (CAM) 来进行可视化.
主要成果:
- 在CH分类中,ResNet50获得了最高的性能 (AUC为0.782,精度为0.750).
- NSP分类给出了适度的结果 (ResNet50准确度为0.677).
- EfficientNet B5显示了CH+NSP联合分类 (0.744) 的最高AUC. CAM确定了特定条件的姿势焦点区域.
结论:
- 深度学习模型表明,基于坐姿来对CH和NSP进行分类的潜力.
- 这些模型可以检测出暗示肌肉骨疾病的微妙姿势模式,这表明早期检测中的应用.
- 多模式评估方法仍然至关重要,因为姿势和疼痛之间存在复杂的联系.
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