Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

,; Liu, Yun-Fang; Fei, Fang-Qin; ,; Chen, Nai-Mei; ,; Zhu, Zhen-Tao; ,; Fei, Xing-Zhen; ,

doi:10.18240/ijo.2023.09.05

Cited by 3 publications

References 87 publications

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Artificial Intelligence Support Improves Diagnosis Accuracy in Anterior Segment Eye Diseases

Maehara,

Ueno,

Yamaguchi

et al. 2024

Preprint

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CorneAI, a deep learning model designed for diagnosing cataracts and corneal diseases, was assessed for its impact on ophthalmologists' diagnostic accuracy. In the study, 40 ophthalmologists (20 specialists and 20 residents) classified 100 images, including iPhone 13 Pro photos (50 images) and diffuser slit-lamp photos (50 images), into nine categories (normal condition, infectious keratitis, immunological keratitis, corneal scar, corneal deposit, bullous keratopathy, ocular surface tumor, cataract/intraocular lens opacity, and primary angle-closure glaucoma). The iPhone and slit-lamp images represented the same cases. After initially answering without CorneAI, the same ophthalmologists responded to the same cases with CorneAI 2–4 weeks later. With CorneAI's support, the overall accuracy of ophthalmologists increased significantly from 79.2–88.8% (P < 0.001). Specialists' accuracy rose from 82.8–90.0%, and residents' from 75.6–86.2% (P < 0.001). Smartphone image accuracy improved from 78.7–85.5% and slit-lamp image accuracy from 81.2–90.6% (both, P < 0.001). In this study, CorneAI's own accuracy was 86%, but its support enhanced ophthalmologists' accuracy beyond the CorneAI's baseline. This study demonstrated that CorneAI, despite being trained on diffuser slit-lamp images, effectively improved diagnostic accuracy, even with smartphone images.

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Artificial Intelligence Support Improves Diagnosis Accuracy in Anterior Segment Eye Diseases

Maehara,

Ueno,

Yamaguchi

et al. 2024

Preprint

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The application of artificial intelligence in diabetic retinopathy: progress and prospects

Xu,

Zhang,

Huang

et al. 2024

Front. Cell Dev. Biol.

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In recent years, artificial intelligence (AI), especially deep learning models, has increasingly been integrated into diagnosing and treating diabetic retinopathy (DR). From delving into the singular realm of ocular fundus photography to the gradual development of proteomics and other molecular approaches, from machine learning (ML) to deep learning (DL), the journey has seen a transition from a binary diagnosis of “presence or absence” to the capability of discerning the progression and severity of DR based on images from various stages of the disease course. Since the FDA approval of IDx-DR in 2018, a plethora of AI models has mushroomed, gradually gaining recognition through a myriad of clinical trials and validations. AI has greatly improved early DR detection, and we’re nearing the use of AI in telemedicine to tackle medical resource shortages and health inequities in various areas. This comprehensive review meticulously analyzes the literature and clinical trials of recent years, highlighting key AI models for DR diagnosis and treatment, including their theoretical bases, features, applicability, and addressing current challenges like bias, transparency, and ethics. It also presents a prospective outlook on the future development in this domain.

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Detection and diagnosis of diabetic eye diseases using two phase transfer learning approach

Madduri,

Rao

2024

PeerJ Computer Science

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Background Early diagnosis and treatment of diabetic eye disease (DED) improve prognosis and lessen the possibility of permanent vision loss. Screening of retinal fundus images is a significant process widely employed for diagnosing patients with DED or other eye problems. However, considerable time and effort are required to detect these images manually. Methods Deep learning approaches in machine learning have attained superior performance for the binary classification of healthy and pathological retinal fundus images. In contrast, multi-class retinal eye disease classification is still a difficult task. Therefore, a two-phase transfer learning approach is developed in this research for automated classification and segmentation of multi-class DED pathologies. Results In the first step, a Modified ResNet-50 model pre-trained on the ImageNet dataset was transferred and learned to classify normal diabetic macular edema (DME), diabetic retinopathy, glaucoma, and cataracts. In the second step, the defective region of multiple eye diseases is segmented using the transfer learning-based DenseUNet model. From the publicly accessible dataset, the suggested model is assessed using several retinal fundus images. Our proposed model for multi-class classification achieves a maximum specificity of 99.73%, a sensitivity of 99.54%, and an accuracy of 99.67%.

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Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Cited by 3 publications

References 87 publications

Artificial Intelligence Support Improves Diagnosis Accuracy in Anterior Segment Eye Diseases

Artificial Intelligence Support Improves Diagnosis Accuracy in Anterior Segment Eye Diseases

The application of artificial intelligence in diabetic retinopathy: progress and prospects

Detection and diagnosis of diabetic eye diseases using two phase transfer learning approach

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