Drug-induced liver injury (DILI) is a challenging disease to diagnose, a leading cause of acute liver failure, and responsible for drug withdrawal from the market. There is no symptom, no biomarker or test for detection, no therapy, but discontinuation of the drug. Pharmaceutical companies spend huge money, time, and scientific research efforts to test DILI effects and drug efficacy. A preclinical diagnostic support system is designed and proposed for DILI detection and classification on liver biopsy histopathology images. Heterogeneity features and automated machine learning (AutoML) models were tested to classify DILI injury patterns on whole slide image. Fractal and lacunarity values were used to detect hepatocellular necrotic injury patterns caused on a rat liver (in vivo) by 10 drugs at four dose levels. Correlations between fractal and lacunarity values were statistically analyzed for the 10 drugs; the Pearson correlation (r = 0.9809), p-value (1.6612E-06), and R2 (0.9582) were found to be high in the case of carbon tetrachloride. The AutoML model was tested to understand the injury patterns on a subset of 1,277 histology images. The AutoML algorithm was able to classify necrotic injury patterns accurately with an average precision of 98.6% on a score threshold of 0.5.

中文翻译：

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自动化机器学习诊断支持系统作为计算生物标志物，用于检测全玻片肝脏病理图像中药物引起的肝损伤模式。

药物性肝损伤（DILI）是一种难以诊断的疾病，是急性肝衰竭的主要原因，也是导致药物退出市场的原因。没有症状，没有生物标志物或检测测试，没有治疗，但停药。制药公司花费大量资金、时间和科研精力来测试 DILI 效果和药效。设计并提出了一种临床前诊断支持系统，用于肝活检组织病理学图像的 DILI 检测和分类。测试了异质性特征和自动机器学习 (AutoML) 模型，以对整个幻灯片图像上的 DILI 损伤模式进行分类。使用分形和空隙度值来检测四种剂量水平的 10 种药物对大鼠肝脏（体内）造成的肝细胞坏死损伤模式。对 10 种药物的分形值和空隙度值之间的相关性进行了统计分析；四氯化碳的 Pearson 相关性 (r = 0.9809)、p 值 (1.6612E-06) 和 R2 (0.9582) 较高。AutoML 模型经过测试，旨在了解 1,277 个组织学图像子集的损伤模式。AutoML 算法能够准确地对坏死损伤模式进行分类，在分数阈值为 0.5 时，平均精度为 98.6%。