机器学习和脊髓损伤中的深度学习:对诊断和预后算法进行叙述性审查
Satoshi Maki1,2, Takeo Furuya1, Masahiro Inoue1
1Department of Orthopaedic Surgery, Graduate School of Medicine, Chiba University, Chiba 260-8670, Japan.
Journal of clinical medicine
|February 10, 2024
概括
机器学习和深度学习可以提高脊髓损伤的诊断和预后. 这些计算方法改善了脊椎骨折的识别,表征和风险评估,以获得更好的患者护理.
科学领域:
- 脊柱医学是指脊柱医学.
- 医学成像医学成像
- 人工智能的人工智能是人工智能.
背景情况:
- 脊柱损伤,包括骨折,构成重大公共卫生挑战.
- 机器学习 (ML) 和深度学习 (DL) 的进步为脊髓损伤护理的诊断和预后提供了新的途径.
研究的目的:
- 系统地审查ML和DL在脊髓损伤管理中的实际应用.
- 关注这些计算方法在医学成像 (CT,MRI) 和临床数据中的实用性.
主要方法:
- 对39项研究进行了叙述性审查.
- 基于ML/DL在与脊髓损伤相关的诊断或预后任务中的应用,分析了这些研究.
主要成果:
- 34项研究专注于诊断应用,使用DL用于脊椎骨折识别,良性与恶性骨折差异化以及AO骨折分类.
- 5项研究专注于预后应用,使用ML预测脊椎崩和未来骨折风险等结果.
结论:
- ML和DL显示出显著的潜力,以提高脊柱损伤护理的诊断能力.
- 这些技术可以改善骨折特征,风险评估和脊柱损伤的个性化治疗计划.
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