Artificial intelligence technology for myopia challenges: A review

Zhang, Juzhao; Zou, Haidong

doi:10.3389/fcell.2023.1124005

Cited by 7 publications

(5 citation statements)

References 83 publications

(86 reference statements)

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“…CNNs have been the standard for automated medical image diagnosis over the last decade 29 . Transformers, particularly ViTs, have recently gained prominence.…”

Section: Methodsmentioning

confidence: 99%

RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

Zhang,

Lin,

Cheng

et al. 2024

npj Digit. Med.

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Visual impairments and blindness are major public health concerns globally. Effective eye disease screening aided by artificial intelligence (AI) is a promising countermeasure, although it is challenged by practical constraints such as poor image quality in community screening. The recently developed ophthalmic foundation model RETFound has shown higher accuracy in retinal image recognition tasks. This study developed an RETFound-enhanced deep learning (DL) model for multiple-eye disease screening using real-world images from community screenings. Our results revealed that our DL model improved the sensitivity and specificity by over 15% compared with commercial models. Our model also shows better generalisation ability than AI models developed using traditional processes. Additionally, decision curve analysis underscores the higher net benefit of employing our model in both urban and rural settings in China. These findings indicate that the RETFound-enhanced DL model can achieve a higher net benefit in community-based screening, advocating its adoption in low- and middle-income countries to address global eye health challenges.

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“…CNNs have been the standard for automated medical image diagnosis over the last decade 29 . Transformers, particularly ViTs, have recently gained prominence.…”

Section: Methodsmentioning

confidence: 99%

RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

Zhang,

Lin,

Cheng

et al. 2024

npj Digit. Med.

Self Cite

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“…The larger the dataset, the more accurate the AI model. However, in many studies, the dataset used for AI model training and verification is small, which will affect the reliability of AI models performance [77] . Therefore, the dataset used for AI research should contain a sufficient sample size.…”

Section: Limitations and Challengesmentioning

confidence: 99%

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Liu,

Fei,

Chen

et al. 2023

Int J Ophthalmol

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Diabetic retinopathy (DR) is one of the most common retinal vascular diseases and one of the main causes of blindness worldwide. Early detection and treatment can effectively delay vision decline and even blindness in patients with DR. In recent years, artificial intelligence (AI) models constructed by machine learning and deep learning (DL) algorithms have been widely used in ophthalmology research, especially in diagnosing and treating ophthalmic diseases, particularly DR. Regarding DR, AI has mainly been used in its diagnosis, grading, and lesion recognition and segmentation, and good research and application results have been achieved. This study summarizes the research progress in AI models based on machine learning and DL algorithms for DR diagnosis and discusses some limitations and challenges in AI research.

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“…Furthermore, deep learning models are often referred to as "black boxes," meaning it is difficult to comprehend their decision-making process. In critical fields like healthcare, interpretability is crucial for ensuring legal and ethical compliance [21]. To address these concerns, AI decision-making "explanations" are necessary to illuminate the reasoning behind the AI's conclusions [22].…”

Section: Introductionmentioning

confidence: 99%

Towards Trustworthy Myopia Detection: Integration Methodology of Deep Learning Approach, XAI Visualization, and User Interface System

Esam Noori,

Albahri

2023

Applied Data Science and Analysis

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Myopia, a prevalent vision disorder with potential complications if untreated, requires early and accurate detection for effective treatment. However, traditional diagnostic methods often lack trustworthiness and explainability, leading to biases and mistrust. This study presents a four-phase methodology to develop a robust myopia detection system. In the initial phase, the dataset containing training and testing images is located, preprocessed, and balanced. Subsequently, two models are deployed: a pre-trained VGG16 model renowned for image classification tasks, and a sequential CNN with convolution layers. Performance evaluation metrics such as accuracy, recall, F1-Score, sensitivity, and logloss are utilized to assess the models' effectiveness. The third phase integrates explainability, trustworthiness, and transparency through the application of Explainable Artificial Intelligence (XAI) techniques. Specifically, Local Interpretable Model-Agnostic Explanations (LIME) are employed to provide insights into the decision-making process of the deep learning model, offering explanations for the classification of images as myopic or normal. In the final phase, a user interface is implemented for the myopia detection and XAI model, bringing together the aforementioned phases. The outcomes of this study contribute to the advancement of objective and explainable diagnostic methods in the field of myopia detection. Notably, the VGG16 model achieves an impressive accuracy of 96%, highlighting its efficacy in diagnosing myopia. The LIME results provide valuable interpretations for myopia cases. The proposed methodology enhances transparency, interpretability, and trust in the myopia detection process.

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Artificial intelligence technology for myopia challenges: A review

Cited by 7 publications

References 83 publications

RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

RETFound-enhanced community-based fundus disease screening: real-world evidence and decision curve analysis

Research progress in artificial intelligence assisted diabetic retinopathy diagnosis

Towards Trustworthy Myopia Detection: Integration Methodology of Deep Learning Approach, XAI Visualization, and User Interface System

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