Machine learning approach for obstructive sleep apnea screening using brain diffusion tensor imaging

Abstract

Patients with obstructive sleep apnea (OSA) show autonomic, mood, cognitive, and breathing dysfunctions that are linked to increased morbidity and mortality, which can be improved with early screening and intervention. The gold standard and other available methods for OSA diagnosis are complex, require whole-night data, and have significant wait periods that potentially delay intervention. Our aim was to examine whether using faster and less complicated machine learning models, including support vector machine (SVM) and random forest (RF), with brain diffusion tensor imaging (DTI) data can classify OSA from healthy controls. We collected two DTI series from 59 patients with OSA [age: 50.2 ± 9.9 years; body mass index (BMI): 31.5 ± 5.6 kg/m 2 ; apnea-hypopnea index (AHI): 34.1 ± 21.2 events/h 23 female] and 96 controls (age: 51.8 ± 9.7 years; BMI: 26.2 ± 4.1 kg/m 2 ; 51 female) using a 3.0-T magnetic resonance imaging scanner. Using DTI data, mean diffusivity maps were calculated from each series, realigned and averaged, normalised to a common space, and used to conduct cross-validation for model training and selection and to predict OSA. The RF model showed 0.73 OSA and controls classification accuracy and 0.85 area under the curve (AUC) value on the receiver-operator curve. Cross-validation showed the RF model with comparable fitting over SVM for OSA and control data (SVM; accuracy, 0.77; AUC, 0.84). The RF ML model performs similar to SVM, indicating the comparable statistical fitness to DTI data. The findings indicate that RF model has similar AUC Bo Pang and Suraj Doshi contributed equally to this study.

Achieving Better Understanding of Obstructive Sleep Apnea Treatment Effects on Cardiovascular Disease Outcomes through Machine Learning Approaches: A Narrative Review

“…Image-based ML has also been applied to brain MRI scans in OSA patients. Pang et al [ 85 ] used the support vector machine and random forest to accurately classify OSA based on diffusion tensor MRI scans of the brain. In another study, Liu et al [ 86 ] used ML analysis of resting-state functional MRI (rs-fMRI) scans of the brain to identify OSA patients with and without cognitive impairment.…”

Is obstructive sleep apnea a circadian rhythm disorder?

“…As OSA remains highly underdiagnosed (Young et al, 2004), increased efforts are aimed at developing complementary diagnostic methods. Machine learning models (Pang et al, 2022) and possible OSA biomarkers may facilitate OSA screening and diagnosis, however, future studies are needed for their validation (Gaspar et al, 2022; Nowak et al, 2021). As standard PSG does not assess night‐to‐night variability in sleep parameters, which is significant but underreported, multiple‐night sleep recording via portable devices may be of interest (Chouraki et al, 2022).…”

Artificial intelligence in sleep medicine: Present and future

“…There is a certain pattern in the lipid profile, which is indicative of sleep apnea, as noted in a recent study[ 12 ]. Magnetic resonance imaging brain using diffusion tensor imaging with ML can diagnose sleep apnea with 73%-77% accuracy[ 13 ]. The Cleveland Clinic Foundation has developed a sleep app for general consumers to fill out a few questionnaires.…”