Sleep Disorders

[Would artificial intelligence pass a Geriatrics exam?: evaluating conversational responses and their accuracy].

OBJECTIVE: To evaluate the accuracy of six different artificial intelligence (AI) models in answering questions from Spain's MIR (Médico Interno Residente) exam related to Geriatrics. METHODS: The performance of six AI models was analyzed by comparing their conversational responses with the official answer keys of the MIR exam. The accuracy of each model was measured based on the number of correct...

Deep learning for multimodal physiological signal based assessment of sleep disordered breathing.

Sleep disordered breathing (SDB) is commonly assessed using polysomnography (PSG), which records multiple physiological signals during sleep. Accurate automated assessment of SDB related breathing abnormalities remains challenging because respiratory events occur within complex and stage dependent sleep dynamics. To address this problem, this study proposes a multimodal physiological signal based ...

Construction and validation of a nomogram prediction model for postoperative residual risk of pediatric obstructive sleep apnea based on OCTA and LASSO machine learning algorithm.

OBJECTIVE: To evaluate the value of retinal optical coherence tomography angiography (OCTA) microcirculatory parameters combined with clinical, bioche...

Anchor-free temporal localization of apnea events from EEG/EOG with state-space models.

Reduced-channel polysomnography (PSG) and electroencephalography/electrooculography (EEG/EOG) signals can support obstructive sleep apnea (OSA) screen...

Explainable Machine-Learning Model for Predicting Severe Obstructive Sleep Apnea in Patients Undergoing Metabolic Bariatric Surgery.

BACKGROUND: Severe obstructive sleep apnea (SOSA) is associated with an increased risk of perioperative complications in patients undergoing metabolic...

"Temporarily Forgetting Stress": Exploring the Multimodal Features and Audience Experience of AI-Generated Psychological Healing Videos.

This study examines the content characteristics, audience interaction, and experience feedback of artificial intelligence (AI)-generated psychological...

Artificial Intelligence Diagnosis of Obstructive Sleep Apnea Using Overnight Pulse Oximetry: A Systematic Review and Bayesian Meta-Analysis.

BACKGROUND: Obstructive sleep apnea (OSA) affects 38% of the population, yet over 90% of cases remain undiagnosed. The gold standard for diagnosis, po...

Machine learning-based insomnia symptom risk classification model for residents of Hebei province.

OBJECTIVES: The aim is to develop and externally validate a machine learning-based risk classification model for insomnia symptoms in a community-base...

PANDA pediatric arousal neural detection architecture.

We developed and validated PANDA, a deep-learning model for pediatric arousal detection in polysomnography. PANDA uses a U-Net-style encoder-decoder a...

Neuroimaging-derived biotypes of self-reported insomnia symptoms are differentially associated with affective symptoms.

BACKGROUND: Sleep problems are among the most prevalent health complaints during adolescence and show a reciprocal relationship with the development o...

Development and validation of machine learning models for obstructive sleep apnea risk stratification in high-risk pregnant women.

BACKGROUND: Obstructive sleep apnea (OSA) affects approximately 15% of pregnancies and is associated with adverse maternal and fetal outcomes. Althoug...

Clock gene signature predicts insomnia and links to sleep/circadian parameters.

Chronic Insomnia is a prevalent sleep disorder that remains difficult to diagnose due to subjective symptoms and heterogeneous presentations. The most...

Artificial intelligence-based automated sleep staging using heart rate variability: Assessment of performance and clinical prospects.

Background Some artificial intelligence models use heart rate variability (HRV) features to classify sleep stages. Estimation of HRV indices requires ...

Energy-efficient real-time 4-stage sleep classification at 10-second resolution.

Sleep stage classification is critical for diagnosing and managing disorders like sleep apnea and insomnia. However, conventional methods like polysom...

A machine learning-based screening tool for classifying post-traumatic stress disorder in firefighters: Firefighter Latent Risk Evaluation (FLARE).

BACKGROUND: The prevalence of post-traumatic stress disorder (PTSD) among South Korean firefighters is likely to be under-reported because of stigma a...

Innovative screening for OSAS in patients with head and neck cancer using the apneal application: Protocol for a prospective single-centre study.

INTRODUCTION: OSAS is a common yet underdiagnosed condition, particularly among patients with head and neck cancers (HNC). Anatomical changes caused b...

Development of Data-Driven Models for Just-in-Time Digital Self-Management Advice to Improve Physical Functioning in Hip and Knee Osteoarthritis: Protocol for the e-cOAch Cross-Over Study.

BACKGROUND: Clinical guidelines recommend a stepped-care strategy for patients with hip and knee osteoarthritis that begins with nonoperative approach...

Beyond surgical risk: Multidimensional predictors of postoperative arm morbidity after breast cancer treatment.

BACKGROUND: Postoperative upper-limb morbidity following breast cancer treatment is common and clinically significant, encompassing pain, functional i...

Machine learning for scalable obstructive sleep apnea risk screening using digital phenotyping from wearable devices and clinical scales.

Obstructive sleep apnea (OSA) is common yet frequently underdiagnosed, partly because overnight polysomnography (PSG) is logistically burdensome and a...

Neuroprotective effects of Chaihu-Longgu-Muli Decoction in an insomnia-depression comorbidity model via Galectin-3-dependent JAK2/STAT3 inhibition.

ETHNOPHARMACOLOGICAL RELEVANCE: Chaihu-Longgu-Muli Decoction (CLMD) is a classical Traditional Chinese Medicine (TCM) formula from the Shanghan Lun co...