面部神经麻的评估深度学习:机遇和挑战
Kieran Boochoon1, Ali Mottaghi2, Aya Aziz3
1Department of Otolaryngology - Head and Neck Surgery, University of Nebraska Medical Center, Omaha, Nebraska.
Facial plastic surgery : FPS
|June 8, 2023
概括
使用机器学习 (ML) 的自动面部评估提供了更快,更少的偏见评估. 人工智能工具可以量化面部运动和估计严重程度,帮助临床决策.
科学领域:
- 医学技术 医学技术 医学技术
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
背景情况:
- 目前面部的评估是耗时的,劳动密集的,容易产生临床医生的偏见.
- 深度学习系统可能会对患者进行选,并有效地跟踪恢复.
- 像eFACE尺度和Emotrics这样的现有工具提供了改进,但也有局限性.
研究的目的:
- 审查目前面部评估的现状.
- 探索医疗应用人工智能 (AI) 的最新进展.
- 识别面部评估开发人工智能驱动解决方案的机遇和挑战.
主要方法:
- 对面部评估方法的现有文献的审查.
- 分析医疗图像分析中的当前人工智能和深度学习应用.
- 讨论AI在量化面部对称性和运动方面的潜力.
主要成果:
- 人工智能提供了一种有希望的方法来克服手动面部得分的局限性.
- 由人工智能驱动的系统可以快速提供面部标志和运动的定量数据.
- 理想的AI系统将提供实时分析和临床评分的估计.
结论:
- 人工智能有可能通过提供客观,高效和准确的评估来彻底改变面部的评估.
- 挑战包括数据质量,算法偏见和需要可解释的AI.
- 未来的AI系统旨在补充,而不是取代临床医生的评分,以改善患者护理.
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