Summary of Artificial Intelligence-based Triaging Of Cutaneous Melanocytic Lesions, by Ruben T. Lucassen et al.
Artificial Intelligence-Based Triaging of Cutaneous Melanocytic Lesions
by Ruben T. Lucassen, Nikolas Stathonikos, Gerben E. Breimer, Mitko Veta, Willeke A. M. Blokx
First submitted to arxiv on: 14 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel artificial intelligence (AI) model is developed for triaging cutaneous melanocytic lesions based on whole slide images, aiming to reduce workload and turnaround times for pathologists. The model was trained and validated using a large retrospective cohort from the UMC Utrecht, comprising 52,202 whole slide images from 27,167 unique specimens. The AI model achieved strong predictive performance in differentiating between high and low complexity lesions, with area under the receiver operating characteristic curve (AUROC) of 0.966 and area under the precision-recall curve (AUPRC) of 0.857 on the in-distribution test set. The model also performed well on out-of-distribution data, with AUROC of 0.899 and AUPRC of 0.498. Simulation experiments show that AI-based triaging could prevent an average of 43.9 initial examinations by general pathologists for every 500 cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new computer program helps doctors quickly figure out which skin cancer cases need more attention. The program looks at special images of the skin and can tell if a cancer is simple or complex, like a common mole versus something that needs closer examination. This could help doctors work faster and make sure they don’t miss any important cases. |
Keywords
» Artificial intelligence » Attention » Precision » Recall
