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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Deep learning quantifies pathologists' visual patterns for whole slide image diagnosis.

Tianhang Nan¹, Song Zheng^2,3, Siyuan Qiao⁴

¹College of Medicine and Biological Information Engineering, Northeastern University, Shenyang, China.

Nature Communications

|July 2, 2025

Summary

This summary is machine-generated.

This study introduces a new AI system that uses eye-tracking to learn from pathologists, significantly reducing annotation time for whole slide images (WSI) and improving diagnostic accuracy.

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Area of Science:

Digital pathology
Artificial intelligence in medicine
Computational pathology

Background:

Pixelwise manual annotation of whole slide images (WSI) is crucial for training deep learning models in diagnostic pathology.
Current annotation methods are time-consuming, burdening pathologists and limiting dataset size and model precision.

Purpose of the Study:

To develop a method for acquiring pathologist expertise with minimal workload.
To design a deep learning system that decodes visual patterns for precise WSI diagnostics.

Main Methods:

Collected pathologist image review patterns using eye-tracking devices.
Developed the Pathology Expertise Acquisition Network (PEAN), a deep learning system utilizing visual patterns.
Evaluated PEAN on 5881 WSIs across 5 skin lesion categories.

Main Results:

Eye-tracking reduced WSI annotation time to 4% of manual annotation.
PEAN achieved an area under the curve of 0.992 and 96.3% accuracy in diagnostic prediction.
The system effectively learns from pathologist diagnostic processes.

Conclusions:

PEAN offers efficient data annotation, significantly reducing pathologist workload.
The system enables precise diagnostics, aiding large-scale data collection and clinical care.
This approach addresses limitations in current models by incorporating pathologist diagnostic processes.