The paper presents a novel approach, JointViT, to predict oxygen saturation (SaO2) levels from optical coherence tomography angiography (OCTA) data. The key contributions are:
The authors highlight that continuous monitoring of SaO2 is time-consuming and variable, while OCTA offers a promising approach for rapidly and effectively screening eye-related lesions. By predicting SaO2 categories from OCTA data, the proposed method lays the groundwork for utilizing OCTA in diagnosing sleep-related disorders.
The paper first provides background on the importance of SaO2 monitoring, the potential of OCTA, and the challenges posed by the long-tailed distribution in OCTA datasets. It then details the JointViT model architecture, which combines a Vision Transformer backbone with a joint loss function for SaO2 category and value prediction. The balancing augmentation technique is also explained, which addresses the class imbalance in the dataset.
The experimental results showcase the superior performance of JointViT compared to various 2D and 3D medical imaging recognition methods. The model demonstrates significantly higher sensitivity and specificity, particularly in handling the long-tailed distribution. Ablation studies further validate the effectiveness of the joint loss function and the balancing augmentation approach.
Overall, the proposed JointViT model represents a significant advancement in leveraging OCTA data for predicting SaO2 levels, paving the way for the future utilization of OCTA in diagnosing sleep-related disorders.
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