Classification of pachychoroid on optical coherence tomography using deep learning

Kang, Nam Yeo; Ra, Ho; Lee, Kook; Lee, Jun Hyuk; Lee, Won Ki; Baek, Jiwon

doi:10.1007/s00417-021-05104-4

Cited by 9 publications

(6 citation statements)

References 32 publications

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“…The models were trained using pretrained ResNet50 architecture on MATLAB 2021b (MathWorks, Inc., Natick, MA, USA) for each image set (B-scan, retinal thickness, mid-retinal, EZ, and choroid). Selection of deep and shallow convolutional neural network (CNN) architectures was based on our previous studies on DL of OCT images for macular diseases 29 , 30 . To evaluate the performance of binodal imaging, two OCT images from each set underwent concatenation, and the resulting image was used for model training, validation, and testing.…”

Section: Methodsmentioning

confidence: 99%

Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

Jee

Yoon

et al. 2022

Sci Rep

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We sought to predict whether central serous chorioretinopathy (CSC) will persist after 6 months using multiple optical coherence tomography (OCT) images by deep convolutional neural network (CNN). This was a multicenter, retrospective, cohort study. Multiple OCT images, including B-scan and en face images of retinal thickness (RT), mid-retina, ellipsoid zone (EZ) layer, and choroidal layer, were collected from 832 eyes of 832 CSC patients (593 self-resolving and 239 persistent). Each image set and concatenated set were divided into training (70%), validation (15%), and test (15%) sets. Training and validation were performed using ResNet50 CNN architecture for predicting CSC requiring treatment. Model performance was analyzed using the test set. The accuracy of prediction was 0.8072, 0.9200, 0.6480, and 0.9200 for B-scan, RT, mid-retina, EZ, and choroid modalities, respectively. When image sets with high accuracy were concatenated, the accuracy was 0.9520, 0.8800, and 0.9280 for B-scan + RT, B-scan + EZ, and EZ + RT, respectively. OCT B-scan, RT, and EZ en face images demonstrated good performances for predicting the prognosis of CSC using CNN. The performance improved when these sets were concatenated. The results of this study can serve as a reference for choosing an optimal treatment for CSC patients.

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Section: Methodsmentioning

confidence: 99%

Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

Jee

Yoon

et al. 2022

Sci Rep

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“…Transitioning to chorioretinal pathologies, Kuwayama et al used a simple CNN for 83% accuracy [160]. Kang et al achieved 96.31% using ResNet50 to classify pachychoroid [161]. Lee et al used ResNet50 and Inception-v3 to simplify pachychoroid classification [111].…”

Section: ) Classification Of Multiple Pathologiesmentioning

confidence: 99%

Artificial Intelligence in Choroid Through Optical Coherence Tomography: A Comprehensive Review

Selvam,

Driban,

Ong

et al. 2023

Preprint

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<p>Vision-threatening conditions, such as age-related macular degeneration (AMD) and central serous chorioretinopathy (CSCR), arise from dysfunctions in the highly vascular choroid layer in the eye’s posterior segment. Optical coherence tomography (OCT) images play a crucial role in diagnosing choroidal structural changes in clinical practice. This review emphasizes the significant efforts in developing precise detection, quantification, and automated disease classification of choroidal biomarkers. The rapid progress of artificial intelligence (AI) has triggered transformative breakthroughs across sectors including medical image analysis. Recently, the integration of AI within the diagnosis and treatment of choroidal diseases has captured significant attention. Multiple studies highlight AI’s potential to enhance diagnostic precision and optimize clinical outcomes in this context. The review provides an extensive overview of AI’s current applications in choroidal analysis using OCT imaging. It encompasses a diverse array of algorithms and techniques employed for biomarker detection, such as thickness and vascularity index, and for identifying diseases like AMD and CSCR. The overarching goal of this review is to provide an updated and comprehensive exploration of AI’s impact on the choroid, highlighting its potential, challenges, and role in driving innovation in the field.</p>

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“…Serener et al evaluated the performance of the ResNet and AlexNet architectures to accurately detect dry age-related macular degeneration and wet age-related macular degeneration [ 22 ]. Kang et al classified pachychoroid and non-pachychoroid eyes based on OCT-B scan images using ResNet50 and InceptionV3 models and achieved 96.1% and 95.25% accuracy values, respectively [ 23 ]. Perdomo et al presented an automatic image analysis method called a custom OCT-NET model based on CNNs for the detection of DME.…”

Section: Introductionmentioning

confidence: 99%

Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal OCT images using vision transformers: a comparative study

Akça,

Garip,

Ekinci

et al. 2024

Lasers Med Sci

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Classifying retinal diseases is a complex problem because the early problematic areas of retinal disorders are quite small and conservative. In recent years, Transformer architectures have been successfully applied to solve various retinal related health problems. Age-related macular degeneration (AMD) and diabetic macular edema (DME), two prevalent retinal diseases, can cause partial or total blindness. Diseases therefore require an early and accurate detection. In this study, we proposed Vision Transformer (ViT), Tokens-To-Token Vision Transformer (T2T-ViT) and Mobile Vision Transformer (Mobile-ViT) algorithms to detect choroidal neovascularization (CNV), drusen, and diabetic macular edema (DME), and normal using optical coherence tomography (OCT) images. The predictive accuracies of ViT, T2T-ViT and Mobile-ViT achieved on the dataset for the classification of OCT images are 95.14%, 96.07% and 99.17% respectively. Experimental results obtained from ViT approaches showed that Mobile-ViT have superior performance with regard to classification accuracy in comparison with the others. Overall, it has been observed that ViT architectures have the capacity to classify with high accuracy in the diagnosis of retinal diseases.

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Classification of pachychoroid on optical coherence tomography using deep learning

Cited by 9 publications

References 32 publications

Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

Predicting persistent central serous chorioretinopathy using multiple optical coherence tomographic images by deep learning

Artificial Intelligence in Choroid Through Optical Coherence Tomography: A Comprehensive Review

Automated classification of choroidal neovascularization, diabetic macular edema, and drusen from retinal OCT images using vision transformers: a comparative study

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