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Abstract：
Background: To design a novel anomaly detection and localization approach using artificial intelligence methods using optical coherence tomography (OCT) scans for retinal diseases. Methods: High-resolution OCT scans from the publicly available Kaggle dataset and a local dataset were used by four state-of-the-art self-supervised frameworks. The backbone model of all the frameworks was a pre-trained convolutional neural network (CNN), which enabled the extraction of meaningful features from OCT images. Anomalous images included choroidal neovascularization (CNV), diabetic macular edema (DME), and the presence of drusen. Anomaly detectors were evaluated by commonly accepted performance metrics, including area under the receiver operating characteristic curve, F1 score, and accuracy. Results: A total of 25,315 high-resolution retinal OCT slabs were used for training. Test and validation sets consisted of 968 and 4000 slabs, respectively. The best performing across all anomaly detectors had an area under the receiver operating characteristic of 0.99. All frameworks were shown to achieve high performance and generalize well for the different retinal diseases. Heat maps were generated to visualize the quality of the frameworks\' ability to localize anomalous areas of the image. Conclusions: This study shows that with the use of pre-trained feature extractors, the frameworks tested can generalize to the domain of retinal OCT scans and achieve high image-level ROC-AUC scores. The localization results of these frameworks are promising and successfully capture areas that indicate the presence of retinal pathology. Moreover, such frameworks have the potential to uncover new biomarkers that are difficult for the human eye to detect. Frameworks for anomaly detection and localization can potentially be integrated into clinical decision support and automatic screening systems that will aid ophthalmologists in patient diagnosis, follow-up, and treatment design. This work establishes a solid basis for further development of automated anomaly detection frameworks for clinical use.
摘要：
背景：使用光学相干断层扫描（OCT）扫描的人工智能方法设计一种新颖的异常检测和定位方法，用于视网膜疾病。方法：来自公开可用的Kaggle数据集和本地数据集的高分辨率OCT扫描由四个最新的自监督框架使用。所有框架的骨干模型都是预训练的卷积神经网络(CNN)，这使得能够从OCT图像中提取有意义的特征。异常图像包括脉络膜新生血管（CNV），糖尿病性黄斑水肿（DME），还有玻璃疣的存在.异常检测器通过普遍接受的性能指标进行评估，包括接收器工作特性曲线下的面积，F1得分,和准确性。结果：共使用25,315块高分辨率视网膜OCT平板进行训练。测试和验证集由968和4000个平板组成，分别。在所有异常检测器中表现最好的是接收器工作特性下的区域为0.99。所有框架均显示出高性能，并且对于不同的视网膜疾病具有良好的推广性。生成热图以可视化框架的质量，以定位图像的异常区域。结论：这项研究表明，通过使用预先训练的特征提取器，测试的框架可以推广到视网膜OCT扫描领域,并获得高图像水平的ROC-AUC评分.这些框架的定位结果是有希望的，并成功捕获了表明存在视网膜病变的区域。此外，这样的框架有可能发现人眼难以检测的新生物标志物。用于异常检测和定位的框架可以潜在地集成到临床决策支持和自动筛查系统中，以帮助眼科医生进行患者诊断。后续行动,和治疗设计。这项工作为进一步开发用于临床的自动化异常检测框架奠定了坚实的基础。
