Institute for Ophthalmic Research, University of Tübingen, Tübingen, Germany.
University Eye Clinic, University of Tübingen, Tübingen, Germany.
Transl Vis Sci Technol. 2023 Apr 3;12(4):12. doi: 10.1167/tvst.12.4.12.
The purpose of this study was to provide a comparison of performance and explainability of a multitask convolutional deep neuronal network to single-task networks for activity detection in neovascular age-related macular degeneration (nAMD).
From 70 patients (46 women and 24 men) who attended the University Eye Hospital Tübingen, 3762 optical coherence tomography B-scans (right eye = 2011 and left eye = 1751) were acquired with Heidelberg Spectralis, Heidelberg, Germany. B-scans were graded by a retina specialist and an ophthalmology resident, and then used to develop a multitask deep learning model to predict disease activity in neovascular age-related macular degeneration along with the presence of sub- and intraretinal fluid. We used performance metrics for comparison to single-task networks and visualized the deep neural network (DNN)-based decision with t-distributed stochastic neighbor embedding and clinically validated saliency mapping techniques.
The multitask model surpassed single-task networks in accuracy for activity detection (94.2% vs. 91.2%). The area under the curve of the receiver operating curve was 0.984 for the multitask model versus 0.974 for the single-task model. Furthermore, compared to single-task networks, visualizations via t-distributed stochastic neighbor embedding and saliency maps highlighted that multitask networks' decisions for activity detection in neovascular age-related macular degeneration were highly consistent with the presence of both sub- and intraretinal fluid.
Multitask learning increases the performance of neuronal networks for predicting disease activity, while providing clinicians with an easily accessible decision control, which resembles human reasoning.
By improving nAMD activity detection performance and transparency of automated decisions, multitask DNNs can support the translation of machine learning research into clinical decision support systems for nAMD activity detection.
本研究旨在比较多任务卷积深度神经网络与单任务网络在新生血管性年龄相关性黄斑变性（nAMD）活动检测中的性能和可解释性。
从在图宾根大学眼科医院就诊的 70 名患者（46 名女性和 24 名男性）中，获取了 70 名患者（46 名女性和 24 名男性）的 3762 个光学相干断层扫描 B 扫描（右眼=2011，左眼=1751），使用德国海德堡 Spectralis 采集。B 扫描由一名视网膜专家和一名眼科住院医师进行分级，然后用于开发多任务深度学习模型，以预测新生血管性年龄相关性黄斑变性的疾病活动以及是否存在视网膜下和视网膜内液。我们使用性能指标与单任务网络进行比较，并使用 t 分布随机邻居嵌入和经过临床验证的显着性映射技术可视化基于深度神经网络（DNN）的决策。
多任务模型在活动检测的准确性方面优于单任务网络（94.2%比 91.2%）。多任务模型的接收器操作曲线下面积为 0.984，单任务模型为 0.974。此外，与单任务网络相比，通过 t 分布随机邻居嵌入和显着性映射的可视化突出表明，多任务网络在新生血管性年龄相关性黄斑变性中的活动检测决策与视网膜下和视网膜内液的存在高度一致。
多任务学习提高了预测疾病活动的神经元网络的性能，同时为临床医生提供了易于访问的决策控制，这类似于人类的推理。
医麦客
Zotero 插件
