MyoVision-US:一种由人工智能驱动的软件,用于对骨肌肉超声波的自动分析
medRxiv : the preprint server for health sciences
|May 15, 2024
概括
人工智能 (AI) 显著加快了下肢肌肉超声波分析的速度,将时间缩短了99.8%. 与专家手动分析相比,AI工具MyoVision-US表现出卓越的一致性和强大的可预测性.
科学领域:
- 生物医学工程 生物医学工程
- 医疗成像医学成像
- 人工智能在医学中的应用
背景情况:
- 手动分析肌肉超声波图像进行形态测量是耗时的,需要广泛的培训.
- 对肌肉形态学的客观量化对于临床实践和研究至关重要.
- 现有的方法在效率和一致性方面面临挑战.
研究的目的:
- 开发和验证一个人工智能驱动的软件工具 (MyoVision-US) 用于自动下肢肌肉超声波分析.
- 评估人工智能工具的一致性和可预测性,与专家手动分析对比.
- 评估人工智能工具在不同患者群体 (健康,急性和慢性疾病) 的表现.
主要方法:
- 深度学习模型 (MyoVision-US) 开发用于自动化肌肉形态分析.
- 四头肌综合体 (QC) 和前肌 (TA) 肌肉超声波图像来自健康的ICU和肺癌患者.
- 类内相关系数 (ICC) 用于确定手动和自动化分析之间的一致性.
- 调整后的线性回归 (adj.R2) 用于计算可预测性.
主要成果:
- 人工智能分析将图像处理时间减少了大约99.8% (247秒比24小时).
- 在AI和QC和TA肌肉的手动分析之间观察到良好的至优秀的一致性 (ICC:0.85-0.99).
- 在所有学科组中,人工智能测量与手动分析相比具有很强的可预测性 (adj.R 2: 0.56-0.97).
- 人工智能显示出高的一致性,即使对于严重病人和肺癌患者的图像.
结论:
- 下肢肌肉超声波分析的AI驱动自动化提供了出色的一致性和强大的可预测性.
- MyoVision-US工具显著提高了肌肉形态测量量量的效率.
- 未来的研究应该将AI模型扩展到其他骨肌群.
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