一种基于优化的超听觉方法,用于准确预测和分类膝关节骨关节炎
Amal G Diab1, El-Sayed M El-Kenawy2,3, Nihal F F Areed4
1Department of Communications and Electronics Engineering, MISR Higher Institute for Engineering and Technology, Mansoura, 35511, Egypt. amal.galal@std.mans.edu.eg.
Scientific reports
|May 14, 2025
概括
本研究介绍了用于早期膝关节骨关节炎 (KOA) 风险评估的先进深度学习 (DL) 方法. 这种新方法准确地预测了KOA患者的疼痛演变,提高了诊断效率.
科学领域:
- 整形外科和生物医学工程
- 医疗保健中的人工智能
- 医学成像分析 医学成像分析
背景情况:
- 膝关节关节炎 (KOA) 给全球健康和经济带来了重大挑战.
- 目前的KOA手动诊断方法耗时且容易变化.
- 早期检测和干预对于管理KOA进展和患者结果至关重要.
研究的目的:
- 开发先进的深度学习 (DL) 模型,用于预测KOA患者的疼痛演变.
- 提高KOA诊断和风险评估的效率和准确性.
- 为预测疾病进展和指导治疗策略创造一个强大的框架.
主要方法:
- 一种集体转移学习方法,利用VGG19,ResNet50,AlexNet和GoogleNet从KOA图像中提取特征.
- 使用二进制 Greylag Goose (bGGO) 优化器进行特征选择,以确定最相关的属性.
- 使用深度学习 (DL) 和机器学习 (ML) 技术对选定的特征进行分类.
- 用GGO算法对CNN超参数进行优化.
主要成果:
- 拟议的DL模型实现了高性能指标:精度 (0.9887),灵敏度 (0.9802) 和特异性 (0.9901).
- bGGO优化器有效地减少了特征维度,提高了模型效率.
- 与现有方法相比,综合DL和ML方法显示出优越的诊断能力.
- 统计分析证实了研究结果的可靠性和有效性.
结论:
- 开发的深度学习方法为KOA风险评估和疼痛演变预测提供了高度准确和高效的工具.
- 这种方法可以显著帮助临床医生在早期KOA诊断和个性化治疗规划.
- 这项研究强调了先进的人工智能技术在改善患者护理方面的潜力,这些技术可以改善KOA等虚弱关节疾病的患者护理.
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