早期内状层稀薄和视网膜神经纤维层加厚在激发性视网膜损伤使用深度学习辅助光学连贯性断层扫描
Da Ma1,2,3,4, Wenyu Deng5,6, Zain Khera5
1Wake Forest University School of Medicine, 1 Medical Center Blvd, Winston-Salem, NC, 27157, USA. dma@wakehealth.edu.
Acta neuropathologica communications
|February 2, 2024
概括
刺激毒性会导致视网膜退化. 光学连贯断层扫描 (OCT) 的深度学习分析表明,内状层的稀薄预测了早期的损伤,而视网膜神经纤维层的变化表明了炎症和细胞死亡.
科学领域:
- 眼科医生 眼科 眼科
- 神经科学是一个神经科学.
- 医疗成像医学成像
背景情况:
- 刺激性毒性是由氨酸吸收受损所驱动的,与神经退行性疾病和视网膜质细胞死亡有关.
- 刺激性损伤对视网膜不同层的确切影响仍然不太清楚.
研究的目的:
- 为了研究N-甲基-D-酸盐 (NMDA) 诱导的激发性毒性视网膜损伤的纵向影响.
- 利用深度学习辅助的视网膜层厚度估计来评估病理变化.
主要方法:
- 用光谱域光学连贯性断层扫描 (OCT) 在大鼠模型中获得了体积计视网膜图像,持续了4周.
- 十个视网膜层被使用深度学习算法自动细分.
- 在NMDA注射后的多个时间点分析了特定层的视网膜厚度变化.
主要成果:
- 在受伤后3天就观察到内状层的稀释,随后在7天内看到内核层的稀释.
- 视网膜神经纤维层在3天后初步变厚,随后变薄,表明轴突损失之前的炎症.
- 深度学习辅助的OCT揭示了在视网膜层之间明显的病理级联.
结论:
- 内状层作为一种早期的成像生物标志物,用于刺激毒性视网膜退化.
- 最初的视网膜神经纤维层变厚可能表明早期的炎症.
- 使用深度学习细分的纵向OCT监测有助于评估激发性视网膜损伤阶段.
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