Temporal and sleep stage-dependent agreement in manual scoring of respiratory events.

Obstructive sleep apnea diagnosis is based on the manual scoring of respiratory events. The agreement in the manual scoring of the respiratory events lacks an in-depth investigation as most of the previous studies reported only the apnea-hypopnea index or overall agreement, and not temporal, second-by-second or event subtype agreement. We hypothesized the temporal and subtype agreement to be low because the event duration or subtypes are not generally considered in current clinical practice.

Retrospective validation of automatic sleep analysis with grey areas model for human-in-the-loop scoring approach.

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.

Deep learning-based prediction of the dose-volume histograms for volumetric modulated arc therapy of left-sided breast cancer.

Background: The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose-volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics.

Prolonged lung-to-finger circulation time indicates an increased risk of intermittent hypoxaemia in sleep apnoea patients.

Introduction: Intermittent hypoxaemia is closely associated with cardiovascular dysfunction and may be a more accurate indicator of obstructive sleep apnoea (OSA) severity than conventional metrics. Another key factor is the lung-to-finger circulation time (LFCt), defined as the duration from the cessation of a respiratory event to the lowest point of oxygen desaturation. LFCt serves as a surrogate marker for circulatory delay and is linked with cardiovascular function.

Sleep stage continuity is associated with objective daytime sleepiness in patients with suspected obstructive sleep apnea.

Study Objectives: Excessive daytime sleepiness (EDS) in patients with obstructive sleep apnea is poorly explained by standard clinical sleep architecture metrics. We hypothesized that reduced sleep stage continuity mediates this connection independently from standard sleep architecture metrics.

Methods: A total of 1,907 patients with suspected obstructive sleep apnea with daytime sleepiness complaints underwent in-lab diagnostic polysomnography and next-day Multiple Sleep Latency Test.