A deep learning model matched or beat expert agreement on sleep arousal scoring, outperforming interscorer reliability across 10 scorers in an independent dataset. Researchers from the University of Eastern Finland, Kuopio University Hospital, and collaborating centers built a fully convolutional neural network trained to detect arousals from polysomnograms (PSGs), drawing on EEG, chin electromyogram, and nasal pressure signals. The model was trained on 1847 PSGs from the Multi-Ethnic Study of Atherosclerosis (MESA) and tested in 205 MESA hold-out recordings as well as an independent cohort of 50 PSGs from the Sleep Revolution (SR) project, each annotated separately by 10 expert scorers across 7 sleep centers in Europe and Australia. In the MESA test set, the model reached an event-by-event F1-score of 0.77, a 1-second κ of 0.67, and an arousal index intraclass correlation coefficient (ICC) of 0.83. In the SR dataset, the model achieved median event-by-event F1-score, 1-second κ, and arousal index ICC of 0.61, 0.50, and 0.66, respectively, each exceeding the corresponding median values achieved by the 10 manual scorers against one another (0.57, 0.46, and 0.59). Against the majority consensus of at least 5 of the 10 scorers, model performance