Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era

Choi, Joon Yul; Kim, Hyungsu; Kim, Jin Kuk; Lee, In Sik; Ryu, Ik Hee; Kim, Jung Soo; Yoo, Tae Keun

doi:10.1007/s11517-023-02952-6

Cited by 3 publications

(1 citation statement)

References 56 publications

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“…Incorporating the methodology outlined in our study holds significant promise for advancing telemedicine, particularly in the post-COVID era where the demand for remote healthcare services has surged. The utilization of DL to predict corneal curvature from fundus photography exemplifies the innovative approaches emerging in teleophthalmology [ 111 ]. This methodology not only aligns with the broader objectives of enhancing diagnostic precision remotely but also underscores the potential of AI in making specialized ophthalmic assessments more accessible outside traditional clinical settings.…”

Section: Discussionmentioning

confidence: 99%

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review

Grzybowski,

Jin,

Zhou

et al. 2024

Ophthalmol Ther

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We conducted a systematic review of research in artificial intelligence (AI) for retinal fundus photographic images. We highlighted the use of various AI algorithms, including deep learning (DL) models, for application in ophthalmic and non-ophthalmic (i.e., systemic) disorders. We found that the use of AI algorithms for the interpretation of retinal images, compared to clinical data and physician experts, represents an innovative solution with demonstrated superior accuracy in identifying many ophthalmic (e.g., diabetic retinopathy (DR), age-related macular degeneration (AMD), optic nerve disorders), and non-ophthalmic disorders (e.g., dementia, cardiovascular disease). There has been a significant amount of clinical and imaging data for this research, leading to the potential incorporation of AI and DL for automated analysis. AI has the potential to transform healthcare by improving accuracy, speed, and workflow, lowering cost, increasing access, reducing mistakes, and transforming healthcare worker education and training.

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Section: Discussionmentioning

confidence: 99%

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review

Grzybowski,

Jin,

Zhou

et al. 2024

Ophthalmol Ther

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Development of a novel scoring system for glaucoma risk based on demographic and laboratory factors using ChatGPT-4

Choi,

Yoo

2024

Med Biol Eng Comput

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Effective automatic classification methods via deep learning for myopic maculopathy

Zhang,

Gao,

Fang

et al. 2024

Front. Med.

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BackgroundPathologic myopia (PM) associated with myopic maculopathy (MM) is a significant cause of visual impairment, especially in East Asia, where its prevalence has surged. Early detection and accurate classification of myopia-related fundus lesions are critical for managing PM. Traditional clinical analysis of fundus images is time-consuming and dependent on specialist expertise, driving the need for automated, accurate diagnostic tools.MethodsThis study developed a deep learning-based system for classifying five types of MM using color fundus photographs. Five architectures—ResNet50, EfficientNet-B0, Vision Transformer (ViT), Contrastive Language-Image Pre-Training (CLIP), and RETFound—were utilized. An ensemble learning approach with weighted voting was employed to enhance model performance. The models were trained on a dataset of 2,159 annotated images from Shenzhen Eye Hospital, with performance evaluated using accuracy, sensitivity, specificity, F1-Score, Cohen’s Kappa, and area under the receiver operating characteristic curve (AUC).ResultsThe ensemble model achieved superior performance across all metrics, with an accuracy of 95.4% (95% CI: 93.0–97.0%), sensitivity of 95.4% (95% CI: 86.8–97.5%), specificity of 98.9% (95% CI: 97.1–99.5%), F1-Score of 95.3% (95% CI: 93.2–97.2%), Kappa value of 0.976 (95% CI: 0.957–0.989), and AUC of 0.995 (95% CI: 0.992–0.998). The voting ensemble method demonstrated robustness and high generalization ability in classifying complex lesions, outperforming individual models.ConclusionThe ensemble deep learning system significantly enhances the accuracy and reliability of MM classification. This system holds potential for assisting ophthalmologists in early detection and precise diagnosis, thereby improving patient outcomes. Future work could focus on expanding the dataset, incorporating image quality assessment, and optimizing the ensemble algorithm for better efficiency and broader applicability.

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Deep learning prediction of steep and flat corneal curvature using fundus photography in post-COVID telemedicine era

Cited by 3 publications

References 56 publications

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review

Retina Fundus Photograph-Based Artificial Intelligence Algorithms in Medicine: A Systematic Review

Development of a novel scoring system for glaucoma risk based on demographic and laboratory factors using ChatGPT-4

Effective automatic classification methods via deep learning for myopic maculopathy

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