Drug-induced toxicity remains a leading cause of failure in preclinical development and early clinical trials. Detecting adverse effects at an early stage is critical to reduce attrition and accelerate the development of safe medicines. Histopathological evaluation remains the gold standard for toxicity assessment, but it relies heavily on expert pathologists, creating a bottleneck for large-scale screening. To address this challenge, we introduce an AI-based anomaly detection framework for histopathological whole-slide images (WSIs) in rodent livers from toxicology studies. The system identifies healthy tissue and known pathologies (anomalies) for which training data is available. In addition, it can detect rare pathologies without training data as out-of-distribution (OOD) findings. We generate a novel dataset of pixelwise annotations of healthy tissue and known pathologies and use this data to fine-tune a pre-trained Vision Transformer (DINOv2) via Low-Rank Adaptation (LoRA) in order to do tissue segmentation. Finally, we extract features for OOD detection using the Mahalanobis distance. To better account for class-dependent variability in histological data, we propose the use of class-specific thresholds. We optimize the thresholds using the mean of the false negative and false positive rates, resulting in only 0.16\% of pathological tissue classified as healthy and 0.35\% of healthy tissue classified as pathological. Applied to mouse liver WSIs with known toxicological findings, the framework accurately detects anomalies, including rare OOD morphologies. This work demonstrates the potential of AI-driven histopathology to support preclinical workflows, reduce late-stage failures, and improve efficiency in drug development.

翻译：药物诱导毒性仍是临床前研发和早期临床试验失败的主要原因。早期检测不良反应对于降低损耗和加速安全药物开发至关重要。组织病理学评估仍是毒性评估的金标准，但其高度依赖病理学专家，为大规模筛查造成了瓶颈。为应对这一挑战，我们提出一种基于人工智能的异常检测框架，用于毒理学研究中啮齿动物肝脏的组织病理学全切片图像分析。该系统可识别健康组织及具有可用训练数据的已知病理改变（异常）。此外，该系统还能检测缺乏训练数据的罕见病理改变，将其识别为分布外发现。我们构建了包含健康组织与已知病理改变的像素级标注新型数据集，并利用该数据通过低秩自适应技术对预训练的视觉Transformer模型进行微调，以实现组织分割。最后，我们采用马氏距离提取分布外检测特征。为更好地处理组织学数据中类别依赖的变异性，我们提出使用类别特异性阈值。通过优化假阴性率与假阳性率的均值，我们实现了仅0.16%的病理组织被误判为健康组织，以及0.35%的健康组织被误判为病理组织。将该框架应用于具有已知毒理学发现的小鼠肝脏全切片图像时，系统能准确检测包括罕见分布外形态在内的各类异常。本研究证明了人工智能驱动的组织病理学在支持临床前工作流程、减少后期研发失败以及提升药物开发效率方面的潜力。