Diagnosing Diabetic Retinopathy in OCTA Images Based on Multilevel Information Fusion Using a Deep Learning Framework

Abstract

Objective. As an extension of optical coherence tomography (OCT), optical coherence tomographic angiography (OCTA) provides information on the blood flow status at the microlevel and is sensitive to changes in the fundus vessels. However, due to the distinct imaging mechanism of OCTA, existing models, which are primarily used for analyzing fundus images, do not work well on OCTA images. Effectively extracting and analyzing the information in OCTA images remains challenging. To this end, a deep learning framework that fuses multilevel information in OCTA images is proposed in this study. The effectiveness of the proposed model was demonstrated in the task of diabetic retinopathy (DR) classification. Method. First, a U-Net-based segmentation model was proposed to label the boundaries of large retinal vessels and the foveal avascular zone (FAZ) in OCTA images. Then, we designed an isolated concatenated block (ICB) structure to extract and fuse information from the original OCTA images and segmentation results at different fusion levels. Results. The experiments were conducted on 301 OCTA images. Of these images, 244 were labeled by ophthalmologists as normal images, and 57 were labeled as DR images. An accuracy of 93.1% and a mean intersection over union (mIOU) of 77.1% were achieved using the proposed large vessel and FAZ segmentation model. In the ablation experiment with 6-fold validation, the proposed deep learning framework that combines the proposed isolated and concatenated convolution process significantly improved the DR diagnosis accuracy. Moreover, inputting the merged images of the original OCTA images and segmentation results further improved the model performance. Finally, a DR diagnosis accuracy of 88.1% ( 95 % CI ± 3.6 % ) and an area under the curve (AUC) of 0.92 were achieved using our proposed classification model, which significantly outperforms the state-of-the-art classification models. As a comparison, an accuracy of 83.7 ( 95 % CI ± 1.5 % ) and AUC of 0.76 were obtained using EfficientNet. Significance. The visualization results show that the FAZ and the vascular region close to the FAZ provide more information for the model than the farther surrounding area. Furthermore, this study demonstrates that a clinically sophisticated designed deep learning model is not only able to effectively assist in the diagnosis but also help to locate new indicators for certain illnesses.

Artificial intelligence and hemodynamic studies in optical coherence tomography angiography for diabetic retinopathy evaluation: A review

“…82 Despite the limited number of OCTA image datasets in the U-net algorithm, the classification of diabetic retinopathy eyes was 95% accurate. 74,83 Li et al, developed a DL framework by considering ResNet50 as the basic architecture, consisting of fourstage convolutional frames. The system model is visually analyzed using gradient-weighted class activation mapping.…”

“…After the classified output is revealed, it is inferred that the framework achieved the highest degree of accuracy (88.1%) using the modified ResNet50 algorithm when the merged input image was given. 83 The comparison was carried out for the modified ResNet50 (proposed model) and the existing model with the same OCTA-500 datasets as the input image. During the implementation of AI in healthcare for disease diagnosis, the highest degree of accuracy is expected from the proposed model.…”

“…The output of all performance indicators is calculated based on average value of accuracy, sensitivity, and specificity. 83 Yasser et al, proposed a novel approach to develop a multi-layered CNN model by building up two successive, fully connected convolutional blocks followed by a max-pooling output layer. This layer converts the fully connected layers activation into a class membership function used for the classification of DR.…”

“…82 Despite the limited number of OCTA image datasets in the U-net algorithm, the classification of diabetic retinopathy eyes was 95% accurate. 74,83…”

“…The output of all performance indicators is calculated based on average value of accuracy, sensitivity, and specificity. 83…”