Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring
Algorithm with Uncertainty-Guided Physician Review
- URL: http://arxiv.org/abs/2312.14996v1
- Date: Fri, 22 Dec 2023 15:58:09 GMT
- Title: Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring
Algorithm with Uncertainty-Guided Physician Review
- Authors: Michal Bechny (1 and 2), Giuliana Monachino (1 and 2), Luigi Fiorillo
(2), Julia van der Meer (3), Markus H. Schmidt (3 and 4), Claudio L. A.
Bassetti (3), Athina Tzovara (1 and 5), Francesca D. Faraci (2) ((1)
Institute of Computer Science, University of Bern, Bern, Switzerland (2)
Institute of Digital Technologies for Personalized Healthcare (MeDiTech),
University of Applied Sciences and Arts of Southern Switzerland, Lugano,
Switzerland (3) Department of Neurology, Inselspital, Bern University
Hospital, University of Bern, Bern, Switzerland (4) Ohio Sleep Medicine
Institute, Dublin, United States (5) Center for Experimental Neurology,
Department of Neurology, Inselspital, Bern University Hospital, University of
Bern, Bern, Switzerland)
- Abstract summary: This study aims to enhance the clinical use of automated sleep-scoring algorithms by incorporating an uncertainty estimation approach.
Total of 19578 PSGs from 13 open-access databases were used to train U-Sleep, a state-of-the-art sleep-scoring algorithm.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Purpose: This study aims to enhance the clinical use of automated
sleep-scoring algorithms by incorporating an uncertainty estimation approach to
efficiently assist clinicians in the manual review of predicted hypnograms, a
necessity due to the notable inter-scorer variability inherent in
polysomnography (PSG) databases. Our efforts target the extent of review
required to achieve predefined agreement levels, examining both in-domain and
out-of-domain data, and considering subjects diagnoses. Patients and methods:
Total of 19578 PSGs from 13 open-access databases were used to train U-Sleep, a
state-of-the-art sleep-scoring algorithm. We leveraged a comprehensive clinical
database of additional 8832 PSGs, covering a full spectrum of ages and
sleep-disorders, to refine the U-Sleep, and to evaluate different
uncertainty-quantification approaches, including our novel confidence network.
The ID data consisted of PSGs scored by over 50 physicians, and the two OOD
sets comprised recordings each scored by a unique senior physician. Results:
U-Sleep demonstrated robust performance, with Cohen's kappa (K) at 76.2% on ID
and 73.8-78.8% on OOD data. The confidence network excelled at identifying
uncertain predictions, achieving AUROC scores of 85.7% on ID and 82.5-85.6% on
OOD data. Independently of sleep-disorder status, statistical evaluations
revealed significant differences in confidence scores between aligning vs
discording predictions, and significant correlations of confidence scores with
classification performance metrics. To achieve K of at least 90% with physician
intervention, examining less than 29.0% of uncertain epochs was required,
substantially reducing physicians workload, and facilitating near-perfect
agreement.
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