A comprehensive AI model development framework for consistent Gleason grading

A comprehensive AI model development framework for consistent Gleason grading

Communications Medicine

10.1038/s43856-024-00502-1

https://doi.org/10.1038/s43856-024-00502-1

Xinmi Huo, Kok Haur Ong, Kah Weng Lau, Laurent Gole, David M. Young, Char Loo Tan, Xiaohui Zhu, Chongchong Zhang, Yonghui Zhang, Longjie Li, Hao Han, Haoda Lu, Jing Zhang, Jun Hou, Huanfen Zhao, Hualei Gan, Lijuan Yin, Xingxing Wang, Xiaoyue Chen, Hong Lv, Haotian Cao, Xiaozhen Yu, Yabin Shi, Ziling Huang, Gabriel Marini, Jun Xu, Bingxian Liu, Bingxian Chen, Qiang Wang, Kun Gui, Wenzhao Shi, Yingying Sun, Wanyuan Chen, Dalong Cao, Stephan J. Sanders, Hwee Kuan Lee, Susan Swee-Shan Hue, Weimiao Yu, Soo Yong Tan

artificial intelligence

09 May 2024

Huo, X., Ong, K. H., Lau, K. W., Gole, L., Young, D. M., Tan, C. L., Zhu, X., Zhang, C., Zhang, Y., Li, L., Han, H., Lu, H., Zhang, J., Hou, J., Zhao, H., Gan, H., Yin, L., Wang, X., Chen, X., … Tan, S. Y. (2024). A comprehensive AI model development framework for consistent Gleason grading. Communications Medicine, 4(1). https://doi.org/10.1038/s43856-024-00502-1

Artificial Intelligence(AI)-based solutions for Gleason grading hold promise for pathologists, while image quality inconsistency, continuous data integration needs, and limited generalizability hinder their adoption and scalability.MethodsWe present a comprehensive digital pathology workflow for AI-assisted Gleason grading. It incorporates A!MagQC (image quality control), A!HistoClouds (cloud-based annotation), Pathologist-AI Interaction (PAI) for continuous model improvement, Trained on Akoya-scanned images only, the model utilizes color augmentation and image appearance migration to address scanner variations. We evaluate it on Whole Slide Images (WSI) from another five scanners and conduct validations with pathologists to assess AI efficacy and PAI.ResultsOur model achieves an average F1 score of 0.80 on annotations and 0.71 Quadratic Weighted Kappa on WSIs for Akoya-scanned images. Applying our generalization solution increases the average F1 score for Gleason pattern detection from 0.73 to 0.88 on images from other scanners. The model accelerates Gleason scoring time by 43% while maintaining accuracy. Additionally, PAI improve annotation efficiency by 2.5 times and led to further improvements in model performance.ConclusionsThis pipeline represents a notable advancement in AI-assisted Gleason grading for improved consistency, accuracy, and efficiency. Unlike previous methods limited by scanner specificity, our model achieves outstanding performance across diverse scanners. This improvement paves the way for its seamless integration into clinical workflows.

Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)

This research is supported by core funding from: ASTAR - Bioinformatics Institute (BII).

This research / project is supported by the Agency for Science, Technology and Research - Innovation & Enterprise (I&E) Gap-funded Project (I22D1AG067)
Grant Reference no. : I22D1AG067

https://oar.a-star.edu.sg/communities-collections/articles/22201

2730-664X

Bioinformatics Institute