Predicting Central Serous Chorioretinopathy Recurrence Using Machine Learning.

Purpose: To predict central serous chorioretinopathy (CSC) recurrence 3 and 6 months after laser treatment by using machine learning. Methods: Clinical and imaging features of 461 patients (480 eyes) with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The ZOC data (416 eyes of 401 patients) were used as the training dataset and the internal test dataset, while the XEC data (64 eyes of 60 patients) were used as the external test dataset. Six different machine learning algorithms and an ensemble model were trained to predict recurrence in patients with CSC. After completing the initial detailed investigation, we designed a simplified model using only clinical data and OCT features. Results: The ensemble model exhibited the best performance among the six algorithms, with accuracies of 0.941 (internal test dataset) and 0.970 (external test dataset) at 3 months and 0.903 (internal test dataset) and 1.000 (external test dataset) at 6 months. The simplified model showed a comparable level of predictive power. Conclusion: Machine learning achieves high accuracies in predicting the recurrence of CSC patients. The application of an intelligent recurrence prediction model for patients with CSC can potentially facilitate recurrence factor identification and precise individualized interventions.

Predicting Post-Therapeutic Visual Acuity and OCT Images in Patients With Central Serous Chorioretinopathy by Artificial Intelligence.

To predict visual acuity (VA) and post-therapeutic optical coherence tomography (OCT) images 1, 3, and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC) by artificial intelligence (AI). Real-world clinical and imaging data were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data obtained from ZOC (416 eyes of 401 patients) were used as the training set; the data obtained from XEC (64 eyes of 60 patients) were used as the test set. Six different machine learning algorithms and a blending algorithm were used to predict VA, and a pix2pixHD method was adopted to predict post-therapeutic OCT images in patients after laser treatment. The data for VA predictions included clinical features obtained from electronic medical records (20 features) and measured features obtained from fundus fluorescein angiography, indocyanine green angiography, and OCT (145 features). The data for OCT predictions included 480 pairs of pre- and post-therapeutic OCT images. The VA and OCT images predicted by AI were compared with the ground truth. In the VA predictions of XEC dataset, the mean absolute errors (MAEs) were 0.074-0.098 logMAR (within four to five letters), and the root mean square errors were 0.096-0.127 logMAR (within five to seven letters) for the 1-, 3-, and 6-month predictions, respectively; in the post-therapeutic OCT predictions, only about 5.15% (5 of 97) of synthetic OCT images could be accurately identified as synthetic images. The MAEs of central macular thickness of synthetic OCT images were 30.15 ± 13.28 µm and 22.46 ± 9.71 µm for the 1- and 3-month predictions, respectively. This is the first study to apply AI to predict VA and post-therapeutic OCT of patients with CSC. This work establishes a reliable method of predicting prognosis 6 months in advance; the application of AI has the potential to help reduce patient anxiety and serve as a reference for ophthalmologists when choosing optimal laser treatments.

Predicting subretinal fluid absorption with machine learning in patients with central serous chorioretinopathy.

BACKGROUND: Machine learning was used to predict subretinal fluid absorption (SFA) at 1, 3 and 6 months after laser treatment in patients with central serous chorioretinopathy (CSC). METHODS: The clinical and imaging data from 480 eyes of 461 patients with CSC were collected at Zhongshan Ophthalmic Center (ZOC) and Xiamen Eye Center (XEC). The data included clinical features from electronic medical records and measured features from fundus fluorescein angiography (FFA), indocyanine green angiography (ICGA), optical coherence tomography angiography (OCTA), and optical coherence tomography (OCT). A ZOC dataset was used for training and internal validation. An XEC dataset was used for external validation. Six machine learning algorithms and a blending algorithm were trained to predict SFA in patients with CSC after laser treatment. The SFA results predicted by machine learning were compared with the actual patient prognoses. Based on the initial detailed investigation, we constructed a simplified model using fewer clinical features and OCT features for convenient application. RESULTS: During the internal validation, random forest performed best in SFA prediction, with accuracies of 0.651±0.068, 0.753±0.065 and 0.818±0.058 at 1, 3 and 6 months, respectively. In the external validation, XGBoost performed best at SFA prediction with accuracies of 0.734, 0.727, and 0.900 at 1, 3 and 6 months, respectively. The simplified model showed a comparable level of predictive power. CONCLUSIONS: Machine learning can achieve high accuracy in long-term SFA predictions and identify the features relevant to CSC patients' prognoses. Our study provides an individualized reference for ophthalmologists to treat and create a follow-up schedule for CSC patients.

Retinal Image Enhancement Using Cycle-Constraint Adversarial Network.

Retinal images are the most intuitive medical images for the diagnosis of fundus diseases. Low-quality retinal images cause difficulties in computer-aided diagnosis systems and the clinical diagnosis of ophthalmologists. The high quality of retinal images is an important basis of precision medicine in ophthalmology. In this study, we propose a retinal image enhancement method based on deep learning to enhance multiple low-quality retinal images. A generative adversarial network is employed to build a symmetrical network, and a convolutional block attention module is introduced to improve the feature extraction capability. The retinal images in our dataset are sorted into two sets according to their quality: low and high quality. Generators and discriminators alternately learn the features of low/high-quality retinal images without the need for paired images. We analyze the proposed method both qualitatively and quantitatively on public datasets and a private dataset. The study results demonstrate that the proposed method is superior to other advanced algorithms, especially in enhancing color-distorted retinal images. It also performs well in the task of retinal vessel segmentation. The proposed network effectively enhances low-quality retinal images, aiding ophthalmologists and enabling computer-aided diagnosis in pathological analysis. Our method enhances multiple types of low-quality retinal images using a deep learning network.

Fundus Image Enhancement Method Based on CycleGAN.

In this paper, we propose a retinal image enhancement method, called Cycle-CBAM, which is based on CycleGAN to realize the migration from poor quality fundus images to good quality fundus images. It does not require paired training set any more, that is critical since it is quite difficult to obtain paired medical images. In order to solve the degeneration of texture and detail caused by training unpaired images, we enhance the CycleGAN by adopting the Convolutional Block Attention Module (CBAM). To verify the enhancement effect of our method, we not only analyzed the enhanced fundus image quantitatively and qualitatively, but also introduced a diabetic retinopathy (DR) classification module to evaluate the DR level of the fundus images before and after enhancement. The experiments show that our method of integrating CBAM into CycleGAN has superior performance than CycleGAN both in quantitative and qualitative results.