  The oxygen saturation level in the blood (SaO2) is crucial for health,
particularly in relation to sleep-related breathing disorders. However,
continuous monitoring of SaO2 is time-consuming and highly variable depending
on patients' conditions. Recently, optical coherence tomography angiography
(OCTA) has shown promising development in rapidly and effectively screening
eye-related lesions, offering the potential for diagnosing sleep-related
disorders. To bridge this gap, our paper presents three key contributions.
Firstly, we propose JointViT, a novel model based on the Vision Transformer
architecture, incorporating a joint loss function for supervision. Secondly, we
introduce a balancing augmentation technique during data preprocessing to
improve the model's performance, particularly on the long-tail distribution
within the OCTA dataset. Lastly, through comprehensive experiments on the OCTA
dataset, our proposed method significantly outperforms other state-of-the-art
methods, achieving improvements of up to 12.28% in overall accuracy. This
advancement lays the groundwork for the future utilization of OCTA in
diagnosing sleep-related disorders. See project website
https://steve-zeyu-zhang.github.io/JointViT
