Abstract #1902

Deep Learning for Classifying Patients with Obstructive Sleep Apnea from Healthy Controls using High-resolution T1-weighted Images

Bo Pang¹, Bhaswati Roy², Milena Lai², Luke Ehlert², Ravi S. Aysola ³, Daniel W. Kang³, Ariana Anderson^1,4, and Rajesh Kumar^2,5,6,7

¹Statistics, University of California at Los Angeles, Los Angeles, CA, United States, ²Anesthesiology, University of California at Los Angeles, Los Angeles, CA, United States, ³Medicine, University of California at Los Angeles, Los Angeles, CA, United States, ⁴Psychiatry and Biobehavioral Sciences, University of California at Los Angeles, Los Angeles, CA, United States, ⁵Radiology, University of California at Los Angeles, Los Angeles, CA, United States, ⁶Bioengineering, University of California at Los Angeles, Los Angeles, CA, United States, ⁷Brain Research Institute, University of California at Los Angeles, Los Angeles, CA, United States

Deep learning has demonstrated impressive performance in a wide range of complex and high-dimensional imaging data, including medical image classification and segmentation. One major challenge of harnessing the power of neural networks in image analysis is the small sample size. The present work utilizes deep learning models to classify high-resolution T1-weighted images of obstructive sleep apnea patients (OSA) from healthy controls. Using 193 participants and with adopted model regularization and exponential moving averaging of model weights, we showed 65% testing accuracy and 80% sensitivity. The findings demonstrate the potential for applying neural network models in assisting image-based OSA diagnoses.

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