Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs

Wang, Ruonan; He, Jiangnan; Ye, Luyao; Sun, Dandan; Yin, Lili; Zhou, Hao; Zhao, Lifeng; Zhu, Jianfeng; Zou, Haidong; Tan, Qichao; Huang, Difeng; Liu, Bo; Fan, Ying; Xu, Xun

doi:10.1007/s40123-022-00621-9

Cited by 16 publications

(6 citation statements)

References 47 publications

(57 reference statements)

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“…In addition, a deep learning-based system was developed to identify the presence of macular degeneration and FT in fundus photographs, with an accuracy up to 93.81% and 91.76% using the ResNet-50 and Inception V3 models, respectively [ 9 ]. Another deep learning system for screening myopic maculopathy based on fundus photographs was developed and obtained AUCs > 0.96 for different types of myopic maculopathy in the external-testing dataset [ 23 ]. Compared with these machine learning methods and deep learning systems, the method for screening FT based on FTD obtained by AI image processing technology used in our study is not only easier but also more available, as it can be achieved by installing software in a fundus camera, but it can also achieve quantitative results with high accuracy.…”

Section: Discussionmentioning

confidence: 99%

Prevalence of Fundus Tessellation and Its Screening Based on Artificial Intelligence in Chinese Children: the Nanjing Eye Study

et al. 2023

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Introduction To investigate the prevalence of fundus tessellation (FT), and the threshold for screening FT using an artificial intelligence (AI) technology in Chinese children. Methods The Nanjing Eye Study was a population-based cohort study conducted in children born between September 2011 and August 2012 in Yuhuatai District of Nanjing. The data presented in this paper were obtained in 2019, when these children were 7 years old and underwent 45° non-mydriatic fundus photography. FT in whole fundus, macular area, and peripapillary area was manually recognized from fundus photographs and classified into three grades. Fundus tessellation density (FTD) in these areas was obtained by calculating the average exposed choroid area per unit area using artificial intelligence (AI) technology based on fundus photographs. The threshold for screening FT using FTD was determined using receiver operating characteristic (ROC) curve analysis. Results Among 1062 enrolled children (mean [± standard deviation] spherical equivalent: − 0.28 ± 0.70 D), the prevalence of FT was 42.18% in the whole fundus (grade 1: 36.53%; grade 2: 5.08%; grade 3: 0.56%), 45.57% in macular area (grade 1: 43.5%; grade 2: 1.60%; grade 3: 0.50%), and 49.72% in peripapillary area (grade 1: 44.44%; grade 2: 4.43%; grade 3: 0.85%), respectively. The threshold value of FTD for screening severe FT (grade ≥ 2) was 0.049 (area under curve [AUC] 0.985; sensitivity 98.3%; specificity 92.3%) in the whole fundus, 0.069 (AUC 0.987; sensitivity 95.5%; specificity 96.2%) in the macular area, and 0.094 (AUC 0.980; sensitivity 94.6%; specificity 94.2%) in the peripapillary area, respectively. Conclusion Fundus tessellation affected approximately 40 in 100 children aged 7 years in China, indicating the importance and necessity of early FT screening. The threshold values of FTD provided by this study had high accuracy for detecting severe FT and might be applied for rapid screening. Supplementary Information The online version contains supplementary material available at 10.1007/s40123-023-00773-2.

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Section: Discussionmentioning

confidence: 99%

Prevalence of Fundus Tessellation and Its Screening Based on Artificial Intelligence in Chinese Children: the Nanjing Eye Study

et al. 2023

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“…In addition, the study evaluated the model's performance using indicators such as the receiver operating characteristic (ROC) curve, demonstrating its high diagnostic accuracy and good clinical application prospects. Wang et al [46] used deep learning technology to screen high myopia patients with maculopathy through color fundus photographs, dividing all color fundus photographs into four categories: normal or mild snowflake fundus, severe snowflake fundus, early pathological myopia, and late pathological myopia. Its accuracy and recall AUC reached 0.922 and 0.781, respectively.…”

Section: Experiments On Hmm Classification Using Alfa-mix+mentioning

confidence: 99%

Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm

Zhu¹,

Wu²,

Zheng³

et al. 2023

Int J Ophthalmol

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AIM: To conduct a classification study of high myopic maculopathy (HMM) using limited datasets, including tessellated fundus, diffuse chorioretinal atrophy, patchy chorioretinal atrophy, and macular atrophy, and minimize annotation costs, and to optimize the ALFA-Mix active learning algorithm and apply it to HMM classification. METHODS: The optimized ALFA-Mix algorithm (ALFA-Mix+) was compared with five algorithms, including ALFA-Mix. Four models, including ResNet18, were established. Each algorithm was combined with four models for experiments on the HMM dataset. Each experiment consisted of 20 active learning rounds, with 100 images selected per round. The algorithm was evaluated by comparing the number of rounds in which ALFA-Mix+ outperformed other algorithms. Finally, this study employed six models, including EfficientFormer, to classify HMM. The best-performing model among these models was selected as the baseline model and combined with the ALFA-Mix+ algorithm to achieve satisfactory classification results with a small dataset. RESULTS: ALFA-Mix+ outperforms other algorithms with an average superiority of 16.6, 14.75, 16.8, and 16.7 rounds in terms of accuracy, sensitivity, specificity, and Kappa value, respectively. This study conducted experiments on classifying HMM using several advanced deep learning models with a complete training set of 4252 images. The EfficientFormer achieved the best results with an accuracy, sensitivity, specificity, and Kappa value of 0.8821, 0.8334, 0.9693, and 0.8339, respectively. Therefore, by combining ALFA-Mix+ with EfficientFormer, this study achieved results with an accuracy, sensitivity, specificity, and Kappa value of 0.8964, 0.8643, 0.9721, and 0.8537, respectively. CONCLUSION: The ALFA-Mix+ algorithm reduces the required samples without compromising accuracy. Compared to other algorithms, ALFA-Mix+ outperforms in more rounds of experiments. It effectively selects valuable samples compared to other algorithms. In HMM classification, combining ALFA-Mix+ with EfficientFormer enhances model performance, further demonstrating the effectiveness of ALFA-Mix+.

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“…Wang et al [28] , fundus images but not OCT scans can be acquired. Yang et al [14] applied an AI system to myopia screening using ocular appearance images and achieved a high screening accuracy, enabling remote monitoring of the refractive status in children with myopia.…”

Section: Artificial Intelligence Application In Myopiamentioning

confidence: 99%

“…Their performance was comparable to that of general ophthalmologists and retinal specialists. Wang et al [28] developed an AI model for the detection and classification of myopic macular lesions based on fundus images. Its performance was comparable to that of experts and could assist ophthalmologists by reducing the workload and saving time during large-scale myopia screening and long-term follow-ups.…”

Section: Artificial Intelligence Application In Myopiamentioning

confidence: 99%

Artificial intelligence-aided diagnosis and treatment in the field of optometry

Du,

Zhang,

Wang

et al. 2023

Int J Ophthalmol

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With the rapid development of computer technology, the application of artificial intelligence (AI) to ophthalmology has gained prominence in modern medicine. As modern optometry is closely related to ophthalmology, AI research on optometry has also increased. This review summarizes current AI research and technologies used for diagnosis in optometry, related to myopia, strabismus, amblyopia, optical glasses, contact lenses, and other aspects. The aim is to identify mature AI models that are suitable for research on optometry and potential algorithms that may be used in future clinical practice.

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Efficacy of a Deep Learning System for Screening Myopic Maculopathy Based on Color Fundus Photographs

Cited by 16 publications

References 47 publications

Prevalence of Fundus Tessellation and Its Screening Based on Artificial Intelligence in Chinese Children: the Nanjing Eye Study

Prevalence of Fundus Tessellation and Its Screening Based on Artificial Intelligence in Chinese Children: the Nanjing Eye Study

Research on classification method of high myopic maculopathy based on retinal fundus images and optimized ALFA-Mix active learning algorithm

Artificial intelligence-aided diagnosis and treatment in the field of optometry

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