AI Medical Compendium Journal:
Toxicologic pathology

Showing 1 to 10 of 33 articles

Development of a Deep Learning Tool to Support the Assessment of Thyroid Follicular Cell Hypertrophy in the Rat.

Toxicologic pathology
Thyroid tissue is sensitive to the effects of endocrine disrupting substances, and this represents a significant health concern. Histopathological analysis of tissue sections of the rat thyroid gland remains the gold standard for the evaluation for a...

Immunohistochemistry-Free Enhanced Histopathology of the Rat Spleen Using Deep Learning.

Toxicologic pathology
Enhanced histopathology of the immune system uses a precise, compartment-specific, and semi-quantitative evaluation of lymphoid organs in toxicology studies. The assessment of lymphocyte populations in tissues is subject to sampling variability and l...

Inter-Rater and Intra-Rater Agreement in Scoring Severity of Rodent Cardiomyopathy and Relation to Artificial Intelligence-Based Scoring.

Toxicologic pathology
We previously developed a computer-assisted image analysis algorithm to detect and quantify the microscopic features of rodent progressive cardiomyopathy (PCM) in rat heart histologic sections and validated the results with a panel of five veterinary...

Deep Learning-Based Spermatogenic Staging in Tissue Sections of Cynomolgus Macaque Testes.

Toxicologic pathology
The indirect assessment of adverse effects on fertility in cynomolgus monkeys requires that tissue sections of the testis be microscopically evaluated with awareness of the stage of spermatogenesis that a particular cross-section of a seminiferous tu...

Assessment of Color Reproducibility and Mitigation of Color Variation in Whole Slide Image Scanners for Toxicologic Pathology.

Toxicologic pathology
Digital pathology workflows in toxicologic pathology rely on whole slide images (WSIs) from histopathology slides. Inconsistent color reproduction by WSI scanners of different models and from different manufacturers can result in different color repr...

Methodologies and Emerging Technologies for the Evaluation of the Hematopoietic System.

Toxicologic pathology
Hematology and bone marrow analysis is central to our understanding of the hematopoietic system and how it responds to insults, and this session presented during the 2022 STP symposium provided a review of current and novel approaches for the evaluat...

Deep Learning-Based Segmentation of Morphologically Distinct Rat Hippocampal Reactive Astrocytes After Trimethyltin Exposure.

Toxicologic pathology
As regulators of homeostasis, astrocytes undergo morphological changes after injury to limit the insult in central nervous system (CNS). Trimethyltin (TMT) is a known neurotoxicant that induces reactive astrogliosis in rat CNS. To evaluate the degree...

Morphologic Features and Deep Learning-Based Analysis of Canine Spermatogenic Stages.

Toxicologic pathology
In nonclinical toxicity studies, stage-aware evaluation is often expected to assess drug-induced testicular toxicity. Although stage-aware evaluation does not require identification of specific stages, it is important to understand microscopic featur...

Implementation and Practice of Deep Learning-Based Instance Segmentation Algorithm for Quantification of Hepatic Fibrosis at Whole Slide Level in Sprague-Dawley Rats.

Toxicologic pathology
Exponential development in artificial intelligence or deep learning technology has resulted in more trials to systematically determine the pathological diagnoses using whole slide images (WSIs) in clinical and nonclinical studies. In this study, we a...

Artificial Intelligence in Toxicological Pathology: Quantitative Evaluation of Compound-Induced Follicular Cell Hypertrophy in Rat Thyroid Gland Using Deep Learning Models.

Toxicologic pathology
Digital pathology has recently been more broadly deployed, fueling artificial intelligence (AI) application development and more systematic use of image analysis. Here, two different AI models were developed to evaluate follicular cell hypertrophy in...