研究目的
To develop a new intelligent system based on deep learning for automatically categorizing optical coherence tomography (OCT) images into multiple categories of abnormalities.
研究成果
The deep learning-based system achieved high accuracy in classifying OCT images, comparable to or better than human experts, with potential for clinical application in diagnosing retinal diseases. Future work should involve larger, diverse data sets and address complex images and model interpretability.
研究不足
The OCT images were collected from only one image center, which may affect generalizability. The data set included scans from the same patients over time, potentially reducing diversity. The system performed poorly on complex images with multiple abnormalities. The deep learning model is a 'black box' with unclear decision-making processes.
1：Experimental Design and Method Selection:
The study used a deep learning approach with a 101-layer ResNet convolutional neural network for multiclass classification of OCT images. A 10-fold cross-validation method was applied for training and optimization.
2：Sample Selection and Data Sources:
A total of 60,407 OCT images were collected from the Wuhan University Eye Center, imaged by Cirrus HD-OCT
3：After labeling by 17 retinal experts, 25,134 images were included, categorized into normal, cystoid macular edema, serous macular detachment, epiretinal membrane, and macular hole. List of Experimental Equipment and Materials:
40 Cirrus HD-OCT 4000 (Carl Zeiss Meditec, Inc.) for imaging; software tools including GraphPad Prism version
4：0 and IBM SPSS Statistics 19 for statistical analysis. Experimental Procedures and Operational Workflow:
Images were labeled by experts, split into training (22,017 images) and test (3,317 images) sets. The ResNet model was trained using transfer learning from ImageNet, with four binary classifiers integrated into a system. Performance was evaluated using AUC, accuracy, sensitivity, specificity, and kappa value, and compared to human experts.
5：Data Analysis Methods:
Statistical analysis included calculation of AUC, accuracy, sensitivity, specificity, and kappa value using GraphPad Prism and SPSS software.
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