Whole Slide Imaging, Artificial Intelligence, and Machine Learning in Pediatric and Perinatal Pathology: Current...

Area of Science:

• Digital pathology and artificial intelligence (AI) applications.
• Machine learning in histopathology.

Background:

• Artificial intelligence and deep learning are increasingly integrated into healthcare, impacting research and clinical practice.
• Digital pathology, utilizing whole slide images (WSI), enables AI-driven analysis for diagnostic support and workflow optimization.
• AI applications in pathology include tumor segmentation, classification, grading, and outcome prediction.

Purpose of the Study:

• To provide an overview of AI applications in pediatric and developmental pathology.
• To discuss the recent developments and challenges of applying AI to WSI in this subspecialty.
• To highlight AI's potential to improve diagnostic efficiency and address specific challenges in pediatric histopathology.

Main Methods:

• Review of recent developments in AI and machine learning applied to whole slide images (WSI).
• Focus on applications within pediatric and developmental pathology specimens.
• Analysis of challenges and potential benefits of AI integration.

Main Results:

• AI integration in digital pathology shows promising performance and clinical potential.
• AI can aid pathologists in diagnostic tasks, improve workflow, and address workforce shortages.
• AI tools can enhance objectivity and reproducibility in diagnostic evaluations, mitigating medicolegal risks.

Conclusions:

• AI adoption in pediatric and developmental histopathology can improve diagnostic efficiency and automate tasks.
• AI offers solutions for standardizing placental pathology and autopsy findings, and mitigating subspecialty staffing shortages.
• Overcoming challenges related to evidence quality, regulation, and clinical evaluation is crucial for widespread AI integration.