Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review

Clinical Snapshot

Key Findings

• P Value: Not applicable for scoping review

• Effect Size: Generally high accuracy (≥90% in seven studies)

• Primary Outcome: AI model performance in pre-clinical respiratory histology analysis

• Nnt Or Sensitivity: Varied validation metrics across studies with inconsistent external validation

• Confidence Interval: Not applicable for scoping review synthesis

Clinical Application

High technical feasibility but requires standardisation of validation approaches and reproducibility measures Relevant to Australian research institutions with respiratory disease programs; aligns with NHMRC emphasis on research reproducibility and translation Research laboratories conducting pre-clinical respiratory disease studies

Abstract

Histological analysis is a cornerstone of pre-clinical respiratory disease research. It enables assessment of pathology, therapeutic effects, and mechanisms. However, conventional approaches rely on manual scoring, which is subjective, time-consuming, and difficult to scale. Artificial intelligence (AI), particularly deep learning, offers potential to automate histology workflows. To date, its use in pre-clinical respiratory models has not been synthesised.We conducted a scoping review following the Joanna Briggs Institute guidelines. We searched MEDLINE and Embase (inception - January 2025) for pre-clinical studies using AI to analyse histology in respiratory disease models. Screening, full-text review, and data extraction were performed in duplicate.Of 6271 studies screened, 29 met inclusion criteria. Most used murine models (76%) and investigated lung cancer (28%), pulmonary fibrosis (24%), or tuberculosis (17%). Haematoxylin and eosin was the most common stain (48%), with others targeting collagen or immune markers. AI tasks included image classification (n=20), segmentation (n=10), and object detection (n=4), predominantly using convolutional neural networks (69%). Pre-processing methods (e.g. stain normalisation) were common, but annotation and training practices were inconsistently reported. AI model performance was generally high (accuracy ≥90%; seven studies); however, validation metrics varied, and external validation was absent. Most studies used "black box" models, with minimal application of explainability techniques. Reproducibility measures, such as sharing datasets or code were rarely reported.AI tools are poised to transform histological analysis in pre-clinical respiratory research. The field will be able to further harness AI to automate pre-clinical respiratory histological analysis by addressing gaps that we have identified in validation, transparency, and standardisation.

References

1. 1.Kuhar, E., Park, J., Jahandideh, F., Komeili, M., Sadeknury, A., Kang, N., Karunamurthy, P., Zarei, M. R., Ebrahimi, A., Gill, S. E., Liaw, P. C., Fergusson, D. A., Stewart, D. J., Mer, A., & Lalu, M. M. (2026). Artificial intelligence-enabled histological analysis in pre-clinical respiratory disease models: a scoping review. European Respiratory Review, 35(140), Article 0243-2025. https://doi.org/10.1183/16000617.0243-2025