Abstract：
Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) is one of the most prevalent chronic liver diseases worldwide. Thermal imaging combined with advanced image-processing and machine learning analysis accurately classified disease status in a study on mice; this study aimed to develop this tool for humans. This prospective study included 46 patients who underwent liver biopsy. Liver thermal imaging was performed on the same day as liver biopsy. We developed an image-processing algorithm that measured the relative spatial thermal variation across the skin covering the liver. The texture parameters obtained from the thermal images were input into the machine learning algorithm. Patients were diagnosed with MASLD and stratified according to nonalcoholic fatty liver disease activity score (NAS) and fibrosis stage using the METAVIR score. Twenty-one of 46 patients were diagnosed with MASLD. Using thermal imaging followed by processing, detection accuracy for patients with NAS >4 was 0.72.
摘要：
代谢功能障碍相关的脂肪变性肝病（MASLD）是世界上最常见的慢性肝病之一。热成像结合先进的图像处理和机器学习分析在小鼠研究中准确分类疾病状态;这项研究旨在为人类开发这种工具。这项前瞻性研究包括46例接受肝活检的患者。在肝活检的同一天进行肝脏热成像。我们开发了一种图像处理算法，可以测量覆盖肝脏的皮肤的相对空间热变化。将从热图像获得的纹理参数输入到机器学习算法中。患者被诊断为MASLD，并根据非酒精性脂肪性肝病活动评分（NAS）和纤维化阶段使用METAVIR评分进行分层。46例患者中有21例被诊断为MASLD。使用热成像，然后进行处理，NAS>4患者的检测准确率为0.72.
