Bridging Ai And Clinical Practice

This article discusses the use of an uncertainty estimation approach to improve the accuracy of automated sleep-scoring algorithms in assisting clinicians with manual review of predicted hypnograms. The study utilized a large dataset of polysomnography recordings and evaluated different uncertainty-quantification methods, including a novel confidence network. Results showed that incorporating uncertainty estimation can enhance the clinical use of automated sleep-scoring algorithms, especially for out-of-domain data.