Abstract：
BACKGROUND: With the rapid advancement of deep learning network technology, the application of facial recognition technology in the medical field has received increasing attention.
OBJECTIVE: This study aims to systematically review the literature of the past decade on facial recognition technology based on deep learning networks in the diagnosis of rare dysmorphic diseases and facial paralysis, among other conditions, to determine the effectiveness and applicability of this technology in disease identification.
METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on 31 December 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. A total of 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past 10 years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software.
RESULTS: The study collected 22 articles with a total sample size of 57 539 cases, of which 43 301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)].
CONCLUSIONS: The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology.
OBJECTIVE: This study aims to systematically review the literature of the past decade on facial recognition technology based on deep learning networks in the diagnosis of rare dysmorphic diseases and facial paralysis, among other conditions, to determine the effectiveness and applicability of this technology in disease identification.
METHODS: This study followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines for literature search and retrieved relevant literature from multiple databases, including PubMed, on 31 December 2023. The search keywords included deep learning convolutional neural networks, facial recognition, and disease recognition. A total of 208 articles on facial recognition technology based on deep learning networks in disease diagnosis over the past 10 years were screened, and 22 articles were selected for analysis. The meta-analysis was conducted using Stata 14.0 software.
RESULTS: The study collected 22 articles with a total sample size of 57 539 cases, of which 43 301 were samples with various diseases. The meta-analysis results indicated that the accuracy of deep learning in facial recognition for disease diagnosis was 91.0% [95% CI (87.0%, 95.0%)].
CONCLUSIONS: The study results suggested that facial recognition technology based on deep learning networks has high accuracy in disease diagnosis, providing a reference for further development and application of this technology.
摘要：
背景：随着深度学习网络技术的飞速发展，面部识别技术在医疗领域的应用日益受到重视。
目的：本研究旨在系统回顾近十年来基于深度学习网络的面部识别技术在罕见畸形和面瘫诊断中的文献，除其他条件外，确定该技术在疾病识别中的有效性和适用性。
方法：本研究遵循系统评价和荟萃分析的首选报告项目进行文献检索，并从多个数据库中检索相关文献。包括PubMed,2023年12月31日搜索关键词包括深度学习卷积神经网络，面部识别,疾病识别。共筛选了近10年来基于深度学习网络的人脸识别技术在疾病诊断中的相关文章208篇,选择22篇文章进行分析。Meta分析采用Stata14.0软件进行。
结果：该研究收集了22篇文章，总样本量为57539例，其中43301个是患有各种疾病的样本。荟萃分析结果表明，深度学习在面部识别中用于疾病诊断的准确率为91.0%[95%CI(87.0%，95.0%）]。
结论：研究结果表明，基于深度学习网络的面部识别技术在疾病诊断中具有较高的准确性，为该技术的进一步发展和应用提供参考。
目的：本研究旨在系统回顾近十年来基于深度学习网络的面部识别技术在罕见畸形和面瘫诊断中的文献，除其他条件外，确定该技术在疾病识别中的有效性和适用性。
方法：本研究遵循系统评价和荟萃分析的首选报告项目进行文献检索，并从多个数据库中检索相关文献。包括PubMed,2023年12月31日搜索关键词包括深度学习卷积神经网络，面部识别,疾病识别。共筛选了近10年来基于深度学习网络的人脸识别技术在疾病诊断中的相关文章208篇,选择22篇文章进行分析。Meta分析采用Stata14.0软件进行。
结果：该研究收集了22篇文章，总样本量为57539例，其中43301个是患有各种疾病的样本。荟萃分析结果表明，深度学习在面部识别中用于疾病诊断的准确率为91.0%[95%CI(87.0%，95.0%）]。
结论：研究结果表明，基于深度学习网络的面部识别技术在疾病诊断中具有较高的准确性，为该技术的进一步发展和应用提供参考。
