AI-Augmented Sleep Apnea Screening in Neurology Clinics: Bridging Sleep and Cognitive Health
DOI:
https://doi.org/10.63501/zmvs0c12Abstract
Dear Editor,
Sleep apnea is a prevalent yet underdiagnosed disorder with profound neurological implications, including cognitive decline, impaired memory, and increased stroke risk. Despite strong evidence linking sleep-disordered breathing to neurocognitive dysfunction, routine screening remains uncommon in neurology clinics due to reliance on polysomnography and limited interdisciplinary coordination. Artificial intelligence (AI)-based tools offer a translational pathway to bridge this gap by providing accurate, non-invasive, and cost-effective screening integrated into clinical workflows.
Recent advances in machine learning and computer vision have enabled automated detection of obstructive sleep apnea (OSA) using facial images, wearable devices, and simple physiological inputs. Deep learning models analyzing craniofacial photographs demonstrated high diagnostic performance (AUC up to 0.916 for apnea–hypopnea index ≥5), suggesting clinical utility even without polysomnography (1). Meta-analyses of wearable AI systems report pooled sensitivity and specificity above 85%, outperforming traditional screening questionnaires (2,3). Furthermore, image-based algorithms incorporating facial and clinical features such as body mass index and neck circumference significantly improve accuracy and enable accessible community-level screening (4,5).
Integrating AI-driven OSA screening into neurology clinics could transform patient management by facilitating early detection among individuals with stroke, Parkinson’s disease, or mild cognitive impairment—populations where untreated sleep apnea worsens outcomes. These tools also align with global health priorities by offering scalable diagnostic solutions adaptable to low-resource settings. To fully realize this potential, a collaborative translational framework is essential, linking data science innovation with clinical validation and ethical oversight.
We call for urgent global action through three measures:
- Validate AI-based OSA screening in neurological populations across diverse ethnic and socioeconomic groups.
- Establish cross-disciplinary collaborations between neurologists, sleep physicians, and data scientists.
- Implement ethical guidelines ensuring transparency, data privacy, and equitable access to AI-assisted diagnostics.
By bridging sleep and cognitive health through intelligent screening, translational neurology can move closer to precision-based, accessible, and data-driven patient care.
References
1. He S, Su H, Li Y, Xu W, Wang X, Han D. Detecting obstructive sleep apnea by craniofacial image-based deep learning. Sleep Breath. 2022;26(4):1693-1703. PMID: 35132533.
2. Abd-Alrazaq A, Aslam H, AlSaad R, Alsahli M, Ahmed A, Damseh R, et al. Detection of Sleep Apnea Using Wearable AI: Systematic Review and Meta-Analysis. J Med Internet Res. 2024;26:e58187. PMID: 39255014.
3. Wang Y, Li W, Wang X, et al. Machine learning for image-based detection of patients with obstructive sleep apnea: an exploratory study. Sleep Breath. 2021;25(4):1923-1931. PMID: 33559004.
4. Su S, Liu Q, Li Z, et al. Deep learning detection of craniofacial abnormalities in obstructive sleep apnea. Sleep Med. 2023;106:40-47. PMID: 37801860.
5. Lee RW, Sutherland K, Chan ASL, et al. Screening patients for risk of sleep apnea using facial photographs. J Clin Sleep Med. 2018;14(10):1747-1755. PMID: 29060289.