Experimental and Clinical Research Center, a cooperation between the Max Delbrück Center for Molecular Medicine in the Helmholtz Association and Charité Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Berlin, Germany.
Nocturne GmbH, Berlin, Germany.
Ann Clin Transl Neurol. 2022 Nov;9(11):1682-1691. doi: 10.1002/acn3.51632. Epub 2022 Oct 25.
The diagnosis of multiple sclerosis (MS) requires demyelinating events that are disseminated in time and space. Peripapillary retinal nerve fiber layer (pRNFL) thickness as measured by optical coherence tomography (OCT) distinguishes eyes with a prior history of acute optic neuritis (ON) and may provide evidence to support a demyelinating attack.
To investigate whether a deep learning (DL)-based network can distinguish between eyes with prior ON and healthy control (HC) eyes using peripapillary ring scans.
We included 1033 OCT scans from 415 healthy eyes (213 HC subjects) and 510 peripapillary ring scans from 164 eyes with prior acute ON (140 patients with MS). Data were split into 70% training, 15% validation, and 15% test data. We included 102 OCT scans from 80 healthy eyes (40 HC) and 61 scans from 40 ON eyes (31 MS patients) from an independent second center. Receiver operating characteristic curve analyses with area under the curve (AUC) were used to investigate performance.
We used a dilated residual convolutional neural network for the classification. The final network had an accuracy of 0.85 and an AUC of 0.86, whereas pRNFL only had an AUC of 0.77 in recognizing ON eyes. Using data from a second center, the network achieved an accuracy of 0.77 and an AUC of 0.90 compared to pRNFL, which had an AUC of 0.84.
DL-based disease classification of prior ON is feasible and has the potential to outperform thickness-based classification of eyes with and without history of prior ON.
多发性硬化症（MS）的诊断需要时间和空间上弥散的脱髓鞘事件。光学相干断层扫描（OCT）测量的视盘周围视网膜神经纤维层（pRNFL）厚度可区分有急性视神经炎（ON）病史的眼睛，并可为脱髓鞘发作提供证据。
研究基于深度学习（DL）的网络是否可以使用视盘周围环扫来区分有既往 ON 病史的眼睛和健康对照（HC）眼睛。
我们纳入了来自 415 只健康眼（213 例 HC 受试者）的 1033 个 OCT 扫描和来自 164 只既往急性 ON 眼（140 例 MS 患者）的 510 个视盘周围环扫。数据分为 70%训练、15%验证和 15%测试数据。我们纳入了来自第二个中心的 80 只健康眼（40 例 HC）的 102 个 OCT 扫描和来自 40 只 ON 眼（31 例 MS 患者）的 61 个扫描。使用曲线下面积（AUC）的接收者操作特征曲线分析来研究性能。
我们使用扩张残差卷积神经网络进行分类。最终网络的准确率为 0.85，AUC 为 0.86，而 pRNFL 在识别 ON 眼时的 AUC 仅为 0.77。使用第二个中心的数据，网络的准确率为 0.77，AUC 为 0.90，而 pRNFL 的 AUC 为 0.84。
基于 DL 的既往 ON 疾病分类是可行的，并且有可能优于有无既往 ON 病史的眼睛的基于厚度的分类。
