Bridging AI and Clinical Practice: Integrating Automated Sleep Scoring Algorithm with Uncertainty-Guided Physician Review

Michal Bechny,1,2 Giuliana Monachino,1,2 Luigi Fiorillo,2 Julia van der Meer,3 Markus H Schmidt,3,4 Claudio LA Bassetti,3 Athina Tzovara,1,3 Francesca D Faraci2 1Institute of Computer Science, University of Bern, Bern, Switzerland; 2Institute of Digital Technologies for Personalized Healthcare (Meditech), University of Applied Sciences and Arts of Southern Switzerland, Lugano, Switzerland; 3Department of Neurology, University Hospital of Bern, Bern, Switzerland; 4Ohio Sleep Medicine Institute, Dublin, OH, USACorrespondence: Michal Bechny, Institute of Digital Technologies for Personalized Healthcare, East Campus USI-SUPSI, Via la Santa 1, CH-6962 Lugano-Viganello, Lugano, Switzerland, Tel +41 (0)58 666 65 10, Email [email protected]: 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 (ID) and out-of-domain (OOD) data, and considering subjects’ diagnoses.Patients and Methods: A total of 19,578 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 an additional 8832 PSGs, covering a full spectrum of ages (0– 91 years) 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 κ ≥ 90% with physician intervention, examining less than 29.0% of uncertain epochs was required, substantially reducing physicians’ workload, and facilitating near-perfect agreement.Conclusion: Inter-scorer variability limits the accuracy of the scoring algorithms to ~80%. By integrating an uncertainty estimation with U-Sleep, we enhance the review of predicted hypnograms, to align with the scoring taste of a responsible physician. Validated across ID and OOD data and various sleep-disorders, our approach offers a strategy to boost automated scoring tools’ usability in clinical settings.Keywords: automated sleep scoring, uncertainty quantification, explainable AI, polysomnography, sleep medicin

Arginine Cofactors on the Polymerase Ribozyme

The RNA world hypothesis states that the early evolution of life went through a stage in which RNA served both as genome and as catalyst. The central catalyst in an RNA world organism would have been a ribozyme that catalyzed RNA polymerization to facilitate self-replication. An RNA polymerase ribozyme was developed previously in the lab but it is not efficient enough for self-replication. The factor that limits its polymerization efficiency is its weak sequence-independent binding of the primer/template substrate. Here we tested whether RNA polymerization could be improved by a cationic arginine cofactor, to improve the interaction with the substrate. In an RNA world, amino acid-nucleic acid conjugates could have facilitated the emergence of the translation apparatus and the transition to an RNP world. We chose the amino acid arginine for our study because this is the amino acid most adept to interact with RNA. An arginine cofactor was positioned at ten different sites on the ribozyme, using conjugates of arginine with short DNA or RNA oligonucleotides. However, polymerization efficiency was not increased in any of the ten positions. In five of the ten positions the arginine reduced or modulated polymerization efficiency, which gives insight into the substrate-binding site on the ribozyme. These results suggest that the existing polymerase ribozyme is not well suited to using an arginine cofactor

The effect of implementing undergraduate competency-based medical education on students' knowledge acquisition, clinical performance and perceived preparedness for practice:a comparative study

<p>Background: Little is known about the gains and losses associated with the implementation of undergraduate competency-based medical education. Therefore, we compared knowledge acquisition, clinical performance and perceived preparedness for practice of students from a competency-based active learning (CBAL) curriculum and a prior active learning (AL) curriculum.</p><p>Methods: We included two cohorts of both the AL curriculum (n = 453) and the CBAL curriculum (n = 372). Knowledge acquisition was determined by benchmarking each cohort on 24 interuniversity progress tests against parallel cohorts of two other medical schools. Differences in knowledge acquisition were determined comparing the number of times CBAL and AL cohorts scored significantly higher or lower on progress tests. Clinical performance was operationalized as students' mean clerkship grade. Perceived preparedness for practice was assessed using a survey.</p><p>Results: The CBAL cohorts demonstrated relatively lower knowledge acquisition than the AL cohorts during the first study years, but not at the end of their studies. We found no significant differences in clinical performance. Concerning perceived preparedness for practice we found no significant differences except that students from the CBAL curriculum felt better prepared for 'putting a patient problem in a broad context of political, sociological, cultural and economic factors' than students from the AL curriculum.</p><p>Conclusions: Our data do not support the assumption that competency-based education results in graduates who are better prepared for medical practice. More research is needed before we can draw generalizable conclusions on the potential of undergraduate competency-based medical education.</p>