Atypical body temperature values can be an indication of abnormal physiological processes associated with several health conditions. Infrared thermal (IRT) imaging is an innocuous imaging modality capable of capturing the natural thermal radiation emitted by the skin surface, which is connected to physiology-related pathological states. The implementation of artificial intelligence (AI) methods for interpretation of thermal data can be an interesting solution to supply a second opinion to physicians in a diagnostic/therapeutic assessment scenario. The aim of this work was to perform a systematic review and meta-analysis concerning different biomedical thermal applications in conjunction with machine learning strategies. The bibliographic search yielded 68 records for a qualitative synthesis and 34 for quantitative analysis. The results show potential for the implementation of IRT imaging with AI, but more work is needed to retrieve significant features and improve classification metrics.

中文翻译：

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机器学习分类器在红外热成像生物医学应用中的应用的荟萃分析和系统综述

非典型体温值可以指示与几种健康状况相关的异常生理过程。红外热（IRT）成像是一种无害的成像方式，能够捕获皮肤表面发出的自然热辐射，该辐射与生理相关的病理状态有关。用于解释热数据的人工智能（AI）方法的实现可能是一个有趣的解决方案，可以在诊断/治疗评估方案中向医生提供第二意见。这项工作的目的是结合机器学习策略，对不同的生物医学热学应用进行系统的综述和荟萃分析。书目搜索产生了68条定性合成的记录和34条定量分析的记录。