面部修复:使用计算机视觉和深度学习进行贝尔的诊断和评估的移动应用程序
Amira Mohamed1, Doha Eid1, Mariam M Ezzat1
1Department of Systems and Biomedical, Faculty of Engineering, Cairo University, Egypt.
Biomedical physics & engineering express
|September 27, 2024
概括
一个新的移动应用程序,Facia-Fix,使用人工智能进行快速准确的面部诊断. 该工具提供客观的实时评估,改善患者护理和结果.
科学领域:
- 医疗技术 医疗技术 医学技术
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 面部 (FP) 诊断是具有挑战性的,因为传统方法的局限性,包括时间,患者的舒适性和临床医生的可用性.
- 早期和准确的FP检测对于及时干预至关重要,可以预防并发症并改善患者的治疗结果.
- 对于FP的先进诊断工具,医疗保健提供者并不能普遍获得.
研究的目的:
- 推出Facia-Fix,这是一款旨在使用计算机视觉和深度学习来诊断贝尔的移动应用程序.
- 提供面部严重程度的实时,客观和非侵入性评估.
- 增强临床医生和患者的诊断和随访经验.
主要方法:
- 开发了Facia-Fix,这是一款集计算机视觉和深度学习用于面部地标分析的移动应用程序.
- 在YouTube FP (YFP) 数据集上训练有素的分类算法,使用House-Brackmann (HB) 方法进行标记.
- 评估了各种深度学习模型,包括MobileNet,CNN,MLP,VGG16和视觉转换器,用于FP严重程度的分类.
主要成果:
- 使用转移学习的MobileNet模型表现出高精度 (0.9812),精度 (0.9753),回忆 (0.9727) 和F1评分 (0.974) 的卓越性能.
- 面部修复器提供了准确,客观,非侵入性和实时的FP严重性的定量评估.
- 初步结果表明,Facia-Fix有可能显著改善FP诊断和患者随访.
结论:
- 面部修复提供了一种新的方法来诊断面部,使用先进的深度学习.
- 该应用程序提供了一个可靠和可访问的工具,用于客观,实时评估FP严重程度.
- 这项技术有可能彻底改变FP诊断,使医疗保健提供者和患者都受益.
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