未经证实:使用羟氯喹导致的视网膜毒性表现为光感受器丢失和在谱域(SD)OCT成像上可检测到的椭圆体区(EZ)反射带的破坏。这项研究调查了基于深度学习的自动算法是否可以检测和量化SDOCT图像上的EZ损失,其准确性与人类注释相当。
未经评估:对前瞻性数据进行回顾性分析,单中心,病例对照研究。
未经评估:85名长期使用羟氯喹的患者(168只眼)(平均暴露时间,14±7.2年)。
UNASSIGNED:在单个OCTB扫描上实现并训练了基于掩模区域的卷积神经网络(M-RCNN)。将逐次扫描检测汇总以产生每个3维SDOCT体积图像的EZ损失的正面图。为了提高EZ损失图的准确性和鲁棒性,提出了一种双重网络架构,该架构学习使用水平(基于水平掩码区域的卷积神经网络[M-RCNNH])和垂直(基于垂直掩码区域的卷积神经网络[M-RCNNV])B扫描并行检测EZ损失。为了量化准确性,进行10倍交叉验证。
未经评估:Precision,召回,联合交叉点(IOU),F1分数指标,将测得的总EZ损失面积与人类分级者注释进行比较,并根据推荐的筛选指南确定毒性。
UNASSIGNED:组合投影网络展示了最佳的整体性能:精度,0.90±0.09;召回,0.88±0.08;F1评分,0.89±0.07。组合模型的性能优于仅M-RCNNH模型(精度,0.79±0.17;召回,0.96±0.04;借据,0.78±0.15;F1评分,0.86±0.12)和仅M-RCNNV模型(精度,0.71±0.21;召回,0.94±0.06;IOU,0.69±0.21;F1评分,0.79±0.16)。准确性与人类专家的变异性相当:精度,0.85±0.09;召回,0.98±0.01;借据,0.82±0.12;F1评分,0.91±0.06。自动生成的正面EZ损失图提供了定量的SDOCT指标,可与其他功能测试相结合,用于准确确定毒性。
UNASSIGNED:该算法可以提供快速,目标,用于测量具有EZ损失的区域的自动方法,并且可以作为定量辅助工具来筛选患者的毒性的存在和程度。
UNASSIGNED: Retinal toxicity resulting from hydroxychloroquine use manifests photoreceptor loss and disruption of the ellipsoid zone (EZ) reflectivity band detectable on spectral-domain (SD) OCT imaging. This study investigated whether an automatic deep learning-based algorithm can detect and quantitate EZ loss on SD OCT images with an accuracy comparable with that of human annotations.
UNASSIGNED: Retrospective analysis of data acquired in a prospective, single-center, case-control study.
UNASSIGNED: Eighty-five patients (168 eyes) who were long-term hydroxychloroquine users (average exposure time, 14 ± 7.2 years).
UNASSIGNED: A mask region-based convolutional neural network (M-RCNN) was implemented and trained on individual OCT B-scans. Scan-by-scan detections were aggregated to produce an en face map of EZ loss per 3-dimensional SD OCT volume image. To improve the accuracy and robustness of the EZ loss map, a dual network architecture was proposed that learns to detect EZ loss in parallel using horizontal (horizontal mask region-based convolutional neural network [M-RCNNH]) and vertical (vertical mask region-based convolutional neural network [M-RCNNV]) B-scans independently. To quantify accuracy, 10-fold cross-validation was performed.
UNASSIGNED: Precision, recall, intersection over union (IOU), F1-score metrics, and measured total EZ loss area were compared against human grader annotations and with the determination of toxicity based on the recommended screening guidelines.
UNASSIGNED: The combined projection network demonstrated the best overall performance: precision, 0.90 ± 0.09; recall, 0.88 ± 0.08; and F1 score, 0.89 ± 0.07. The combined model performed superiorly to the M-RCNNH only model (precision, 0.79 ± 0.17; recall, 0.96 ± 0.04; IOU, 0.78 ± 0.15; and F1 score, 0.86 ± 0.12) and M-RCNNV only model (precision, 0.71 ± 0.21; recall, 0.94 ± 0.06; IOU, 0.69 ± 0.21; and F1 score, 0.79 ± 0.16). The accuracy was comparable with the variability of human experts: precision, 0.85 ± 0.09; recall, 0.98 ± 0.01; IOU, 0.82 ± 0.12; and F1 score, 0.91 ± 0.06. Automatically generated en face EZ loss maps provide quantitative SD OCT metrics for accurate toxicity determination combined with other functional testing.
UNASSIGNED: The algorithm can provide a fast, objective, automatic method for measuring areas with EZ loss and can serve as a quantitative assistance tool to screen patients for the presence and extent of toxicity.