Abstract：
BACKGROUND: Machine learning (ML) offers an opportunity in contact dermatitis (CD) research, where with full clinical picture, may support diagnosis and patch test accuracy.
OBJECTIVE: This review aims to summarise the existing literature on how ML can be applied to CD in its entirety.
METHODS: Embase, Medline, IEEE Xplore, and ACM Digital Library were searched from inception to February 7, 2024, for primary literature reporting on ML models in CD.
RESULTS: 7834 articles were identified in the search, with 110 moving to full-text review, and six articles included. Two used ML to identify key biomarkers to help distinguish between allergic contact dermatitis (ACD) and irritant contact dermatitis (ICD), three used image data to distinguish between ACD and ICD, and one used clinical and demographical data to predict the risk of positive patch tests. All studies used supervision in their ML model training with a total of 49 704 patients across all data sets. There was sparse reporting of the accuracy of these models.
CONCLUSIONS: Although the available research is still limited, there is evidence to suggest that ML has potential to support diagnostic outcomes in a clinical setting. Further research on the use of ML in clinical practice is recommended.
OBJECTIVE: This review aims to summarise the existing literature on how ML can be applied to CD in its entirety.
METHODS: Embase, Medline, IEEE Xplore, and ACM Digital Library were searched from inception to February 7, 2024, for primary literature reporting on ML models in CD.
RESULTS: 7834 articles were identified in the search, with 110 moving to full-text review, and six articles included. Two used ML to identify key biomarkers to help distinguish between allergic contact dermatitis (ACD) and irritant contact dermatitis (ICD), three used image data to distinguish between ACD and ICD, and one used clinical and demographical data to predict the risk of positive patch tests. All studies used supervision in their ML model training with a total of 49 704 patients across all data sets. There was sparse reporting of the accuracy of these models.
CONCLUSIONS: Although the available research is still limited, there is evidence to suggest that ML has potential to support diagnostic outcomes in a clinical setting. Further research on the use of ML in clinical practice is recommended.
摘要：
背景：机器学习（ML）为接触性皮炎（CD）研究提供了机会，有了完整的临床表现，可以支持诊断和补丁测试的准确性。
目的：这篇综述旨在总结现有的关于如何将ML整体应用于CD的文献。
方法：Embase，Medline,IEEEXplore,从成立到2024年2月7日，对ACM数字图书馆进行了搜索，以获取CD中ML模型的主要文献报告。
结果：在搜索中发现了7834篇文章，随着110进入全文审查，包括六篇文章。两个使用ML来识别关键生物标志物，以帮助区分过敏性接触性皮炎（ACD）和刺激性接触性皮炎（ICD）。三个使用图像数据来区分ACD和ICD，一个人使用临床和人口统计学数据来预测斑贴试验阳性的风险。所有研究都在他们的ML模型训练中使用监督，在所有数据集中共有49704名患者。这些模型的准确性报告很少。
结论：尽管现有的研究仍然有限，有证据表明,ML有可能支持临床诊断结果.建议进一步研究ML在临床实践中的使用。
目的：这篇综述旨在总结现有的关于如何将ML整体应用于CD的文献。
方法：Embase，Medline,IEEEXplore,从成立到2024年2月7日，对ACM数字图书馆进行了搜索，以获取CD中ML模型的主要文献报告。
结果：在搜索中发现了7834篇文章，随着110进入全文审查，包括六篇文章。两个使用ML来识别关键生物标志物，以帮助区分过敏性接触性皮炎（ACD）和刺激性接触性皮炎（ICD）。三个使用图像数据来区分ACD和ICD，一个人使用临床和人口统计学数据来预测斑贴试验阳性的风险。所有研究都在他们的ML模型训练中使用监督，在所有数据集中共有49704名患者。这些模型的准确性报告很少。
结论：尽管现有的研究仍然有限，有证据表明,ML有可能支持临床诊断结果.建议进一步研究ML在临床实践中的使用。
