视野数据的自监督去噪提升青光眼进展检测
Self-supervised denoising of visual field data improves detection of glaucoma progression
AI总结：
本研究利用自监督深度学习对4000余例患者视野数据去噪，比较掩码自编码器与变分自编码器，并加入位置p值，使青光眼进展检出率提高4.7%、进展预测提前2.3个月，实现更早更准的进展检测。
摘要：
视野测量可反映患者的周边视觉和日常功能，是识别青光眼视觉损害进展的主要结局指标。然而，视野数据可能含有噪声，表现出较高方差，尤其是在损害加重时更为明显。本研究展示了自监督深度学习在去噪超过4000例患者视野数据中的效用，以提高其信噪比及检测真实青光眼进展的能力。我们部署了变分自编码器（VAE）和掩码自编码器，以确定哪种自监督模型能在平滑视野数据的同时重建出噪声更少、更能预测疾病恶化的显著特征。结果表明，在每个视野位置加入分类p值可改善视野数据的平滑效果。与变分自编码器等先前方法相比，掩码自编码器产生了更干净的去噪数据。使用逐点线性回归（PLR）检测进展眼的检出率提高了4.7%。与未加入p值时相比，加入p值后，掩码自编码器和变分自编码器平滑后的数据预测青光眼进展的时间提前了2.3个月。更快的进展时间（TTP）预测和更高的进展检出百分比支持了我们的假设，即在训练过程中掩蔽视野元素并在每个位置加入p值将改善视野进展检测任务。本研究在训练神经网络去噪视野数据时关于掩蔽策略具有临床相关意义，可实现更早、更准确地检测青光眼进展。该去噪模型可集成到未来的视野分析模型中，以增强青光眼进展检测。
英文摘要：
Perimetric measurements provide insight into a patient's peripheral vision and day-to-day functioning and are the main outcome measure for identifying progression of visual damage from glaucoma. However, visual field data can be noisy, exhibiting high variance, especially with increasing damage. In this study, we demonstrate the utility of self-supervised deep learning in denoising visual field data from over 4000 patients to enhance its signal-to-noise ratio and its ability to detect true glaucoma progression. We deployed both a variational autoencoder (VAE) and a masked autoencoder to determine which self-supervised model best smooths the visual field data while reconstructing salient features that are less noisy and more predictive of worsening disease. Our results indicate that including a categorical p-value at every visual field location improves the smoothing of visual field data. Masked autoencoders led to cleaner denoised data than previous methods, such as variational autoencoders. A 4.7% increase in detection of progressing eyes with pointwise linear regression (PLR) was observed. The masked and variational autoencoders' smoothed data predicted glaucoma progression 2.3 months earlier when p-values were included compared to when they were not. The faster prediction of time to progression (TTP) and the higher percentage progression detected support our hypothesis that masking out visual field elements during training while including p-values at each location would improve the task of detection of visual field progression. Our study has clinically relevant implications regarding masking when training neural networks to denoise visual field data, resulting in earlier and more accurate detection of glaucoma progression. This denoising model can be integrated into future models for visual field analysis to enhance detection of glaucoma progression.
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