Deep Learning Approaches and Applications in Toxicologic Histopathology: Current Status and Future Perspectives

Area of Science:

• Digital pathology
• Machine learning in toxicology
• Computational pathology

Background:

• Whole slide imaging and deep convolutional neural networks are transforming pathology.
• Digital pathology advances offer opportunities for improved efficiency, objectivity, and decision support in diagnostics.
• However, machine learning, particularly deep learning (DL), adoption in nonclinical toxicology studies lags behind clinical applications.

Purpose of the Study:

• To review deep learning (DL) concepts and applications in toxicologic pathology image analysis.
• To highlight the challenges and future research directions for DL in this field.

Main Methods:

• Review of major DL concepts relevant to image analysis.
• Exploration of emerging DL applications in toxicologic pathology.
• Discussion of specific challenges and future research avenues.

Main Results:

• DL offers significant potential for quantitative and objective assessment of histopathology data in toxicology.
• Current DL methods face limitations in throughput and broad applicability for the high-volume, complex data in toxicologic pathology.
• Regulatory requirements, such as good laboratory practice, present significant adoption barriers.

Conclusions:

• DL has the potential to revolutionize toxicologic pathology by enhancing efficiency and objectivity.
• Overcoming practical limitations (throughput, generalizability) and regulatory hurdles is crucial for widespread DL adoption in toxicology.
• Further research is needed to develop robust, scalable, and regulatory-compliant DL solutions for toxicologic pathology image analysis.