Researchers developed a deep learning system using eye-tracking data to enhance AI-powered biopsy image interpretation.
Key Details
- 1The Pathology Expertise Acquisition Network (PEAN) was trained using expert pathologists’ eye movements while reviewing 5,881 skin lesion slides.
- 2PEAN-C achieved 96.3% accuracy and 0.992 AUC on internal data and 93.0% accuracy and 0.984 AUC on external test data, exceeding the next best AI model by 5.5%.
- 3Using pathologist gaze data, the model identifies relevant tissue regions with much less manual labeling burden than traditional pixel-wise annotation.
- 4Integrating PEAN-generated data with other models improved classification accuracy and AUC, statistically significant by paired t-test (p = 0.0053 and 0.0161).
- 5The aim is to scale to personalized diagnosis and multimodal predictive models, leveraging low-cost data collection from expertise monitoring.
Why It Matters
This study demonstrates a scalable, low-burden way to transfer human diagnostic expertise into AI tools, potentially enhancing accuracy and acceptance in digital pathology and, by extension, inspiring innovations in radiology imaging AI with analogous annotation challenges.
Source
EurekAlert
Related News
•EurekAlert
AI-Enabled Nanoplatforms Combine Ferroptosis, Immunotherapy, and Imaging for Cancer
A groundbreaking review highlights how advanced nanoplatforms can synergistically integrate ferroptosis, immunotherapy, and multimodal imaging to optimize cancer therapy.
•EurekAlert
AI Repurposes Routine CT Scans for Osteoporosis Detection
AI algorithms can extract bone density data from routine CT scans to identify osteoporosis, enabling opportunistic screening.
•EurekAlert
AI Outperforms Radiologists in Detecting Hidden Airway Objects on Chest CT
Southampton researchers developed an AI that surpassed radiologists in detecting hard-to-see airway obstructions on chest CT scans.