Histopathology

AI-powered image analysis for toxicity markers

Intended for the following roles:

• Toxicologic pathologists: AI assists in segmentation, lesion detection, and routine scoring

• Preclinical scientists /Study directors: gains in throughput and data consistency support decision-making

• Digital pathology specialists /Image analysts: develop, validate, and maintain AI pipelines

• Regulatory affairs /QA: ensures GLP compliance and auditability of AI-assisted analysis

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Digital whole-slide imaging combined with convolutional neural networks and related machine learning methods can support cell and tissue segmentation, lesion detection, and quantitative scoring in toxicologic pathology. The literature reports promising task-level performance, but generalizability across scanners, staining protocols, and laboratories remains a primary constraint. PubMed Central, Nature

Challenges

Pathology evaluation is time consuming and can be subjective. For routine endpoints and high volume studies, manual readouts limit throughput and introduce interobserver variability. Regulatory and GLP contexts additionally require traceability, version control, and reproducible analysis pipelines. These constraints affect whether an automated method can be used to support safety decisions.

AI techniques

Supervised deep learning for segmentation and classification on digitized slides; weakly supervised and multiple instance learning approaches for slide-level labels; and hybrid workflows that combine algorithmic pre-screening with pathologist review. Validation routines include cross-scanner testing and stain augmentation strategies. PubMed Central, Nature

Published examples and programs

• Reviews and surveys of deep learning in toxicologic/pathology contexts summarize state of field and validation concerns. PubMed Central, PubMed

• FDA research programs such as PathologAI and AI4TOX are explicitly exploring frameworks and datasets for nonclinical pathology AI. These programs indicate regulatory interest in standardized approaches to animal pathology data. U.S. Food and Drug Administration

Before adopting these methods…

• Performance is highly conditioned on image acquisition, staining, and preprocessing. Validation must therefore include representative scans and stain variability. PubMed

• For GLP or safety-critical endpoints, maintain pathologist-in-the-loop review and ensure audit trails for model versions, parameters, and inputs. U.S. Food and Drug Administration

• Domain shift and interoperability (scanner/DICOM compatibility) are common failure modes to plan for and test explicitly. Nature

Pilot validation checklist

1. Define the narrow intended use of the pilot and the decision it will inform

2. Collect representative slide images across scanners, stains, and laboratories; reserve an external site hold-out

3. Specify metrics (sensitivity, specificity, calibration) and report confidence intervals

4. Assess domain shift with targeted perturbation tests (stain, resolution, scanner)

5. Implement versioned pipelines, provenance capture, and pathologist review gates for discordant cases.

Experiment design patterns

• Pilot on routine, high-volume lesion classes (for example, steatosis scoring or simple lesion presence) before moving to complex graded severity tasks.

• Use hybrid workflows where AI pre-screens slides and pathologists adjudicate edge cases.

• Prospectively collect a small, annotated validation set from the target operational site to confirm transferability.

Resources

• Nagpal K. et al., Deep learning in diagnostic pathology reviews. Nature

• Review: Deep learning approaches and applications in toxicologic pathology. PubMed Central

• FDA PathologAI and AI4TOX initiatives. U.S. Food and Drug Administration