ReviewJournal of pathology informatics2021
Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives.
Review in Journal of pathology informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 35 citations in OpenAlex.
- Artificial Intelligence-Powered One Health: A Predictive Framework for Managing Persistent Chemical Threats across Human, Animal, and Environmental Systems.Global challenges (Hoboken, NJ) · 2026Review
- Acute Toxicity and Genotoxicity of cMoL, a Lectin From Moringa oleifera Seeds With Antifungal Activity Against Cryptococcus Strains.Journal of applied toxicology : JAT · 2026Article
- A Systematic Literature Review on Integrated Deep Learning and Multiagent Vision-Language Frameworks for Pathology Image Analysis and Report Generation.Computational and structural biotechnology journal · 2026Review
- Digital Pathology and Artificial Intelligence Applied to Nonclinical Toxicology Pathology-The Current State, Challenges, and Future Directions.Toxicologic pathology · 2025Review
- Report of the AI Pathology Task Force, Non-clinical Evaluation Expert Committee, Japan Pharmaceutical Manufacturers Association: questionnaire survey on AI pathology and utilization of whole slide image database.Journal of toxicologic pathology · 2025Article
- Report of the AI Pathology Task Force, Non-clinical Evaluation Expert Committee, Japan Pharmaceutical Manufacturers Association: an overview of recent publications about AI pathology.Journal of toxicologic pathology · 2025Review
- Bridging imaging-based in vitro methods from biomedical research to regulatory toxicology.Archives of toxicology · 2025Review
- Advancements in pathology: Digital transformation, precision medicine, and beyond.Journal of pathology informatics · 2025Review
- Application of deep learning convolutional neural networks to identify gastric squamous cell carcinoma in mice.Frontiers in medicine · 2025Article
- Comparative analysis of chronic progressive nephropathy (CPN) diagnosis in rat kidneys using an artificial intelligence deep learning model.Toxicological research · 2024Article
- Deep Learning-based Modeling for Preclinical Drug Safety Assessment.bioRxiv : the preprint server for biology · 2024Article
- Application of multiple-finding segmentation utilizing Mask R-CNN-based deep learning in a rat model of drug-induced liver injury.Scientific reports · 2023Article
- A comparative study on the implementation of deep learning algorithms for detection of hepatic necrosis in toxicity studies.Toxicological research · 2023Article
- Preparing pathological data to develop an artificial intelligence model in the nonclinical study.Scientific reports · 2023Article
- Accelerating pharmaceutical R&D with a user-friendly AI system for histopathology image analysis.Journal of pathology informatics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Whole slide imaging enables the use of a wide array of digital image analysis tools that are revolutionizing pathology. Recent advances in digital pathology and deep convolutional neural networks have created an enormous opportunity to improve workflow efficiency, provide more quantitative, objective, and consistent assessments of pathology datasets, and develop decision support systems. Such innovations are already making their way into clinical practice. However, the progress of machine learning - in particular, deep learning (DL) - has been rather slower in nonclinical toxicology studies. Histopathology data from toxicology studies are critical during the drug development process that is required by regulatory bodies to assess drug-related toxicity in laboratory animals and its impact on human safety in clinical trials. Due to the high volume of slides routinely evaluated, low-throughput, or narrowly performing DL methods that may work well in small-scale diagnostic studies or for the identification of a single abnormality are tedious and impractical for toxicologic pathology. Furthermore, regulatory requirements around good laboratory practice are a major hurdle for the adoption of DL in toxicologic pathology. This paper reviews the major DL concepts, emerging applications, and examples of DL in toxicologic pathology image analysis. We end with a discussion of specific challenges and directions for future research.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.