Comparison between Deep-Learning-Based Ultra-Wide-Field Fundus Imaging and True-Colour Confocal Scanning for Diagnosing Glaucoma

Shin, Younji; Cho, Hyunsoo; Shin, Yong Un; Seong, Mincheol; Choi, Jun Won; Lee, Won June

doi:10.3390/jcm11113168

Cited by 7 publications

(4 citation statements)

References 41 publications

(70 reference statements)

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“…Transfer learning is a DL method in which a model trained for one task is repurposed for a related second task. We utilized the weight of the backbone network by applying this fine-tuning of the transfer learning method with SS-OCT images [24,25] using ResNet18 pre-trained with ImageNet (Figure S1). For pre-training, the SS-OCT RNFL thickness map (12 × 9 mm) of glaucoma and normal groups was used (Figure 1).…”

Section: Techniques: Proposed Methodsmentioning

confidence: 99%

“…All participants underwent WF-OCTA imaging with the same SS-OCT device (Topcon, DRI OCT Triton), and the glaucoma was diagnosed by a glaucoma specialist. Diagnosis of glaucoma and selection of the control group were performed similarly to that in previous studies (Supplementary Materials) [24,25]. To eliminate ambiguity, this study excluded patients with high myopia (sph < −6.0D), retinal diseases, and glaucoma suspect states without definite visual field impairment or RNFL defects.…”

Section: Study Design and Participantsmentioning

confidence: 99%

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Diagnosis of Glaucoma Based on Few-Shot Learning with Wide-Field Optical Coherence Tomography Angiography

Yang,

Lee,

Shin

et al. 2024

Biomedicines

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This study evaluated the utility of incorporating deep learning into the relatively novel imaging technique of wide-field optical coherence tomography angiography (WF-OCTA) for glaucoma diagnosis. To overcome the challenge of limited data associated with this emerging imaging, the application of few-shot learning (FSL) was explored, and the advantages observed during its implementation were examined. A total of 195 eyes, comprising 82 normal controls and 113 patients with glaucoma, were examined in this study. The system was trained using FSL instead of traditional supervised learning. Model training can be presented in two distinct ways. Glaucoma feature detection was performed using ResNet18 as a feature extractor. To implement FSL, the ProtoNet algorithm was utilized to perform task-independent classification. Using this trained model, the performance of WF-OCTA through the FSL technique was evaluated. We trained the WF-OCTA validation method with 10 normal and 10 glaucoma images and subsequently examined the glaucoma detection effectiveness. FSL using the WF-OCTA image achieved an area under the receiver operating characteristic curve (AUC) of 0.93 (95% confidence interval (CI): 0.912–0.954) and an accuracy of 81%. In contrast, supervised learning using WF-OCTA images produced worse results than FSL, with an AUC of 0.80 (95% CI: 0.778–0.823) and an accuracy of 50% (p-values < 0.05). Furthermore, the FSL method using WF-OCTA images demonstrated improvement over the conventional OCT parameter-based results (all p-values < 0.05). This study demonstrated the effectiveness of applying deep learning to WF-OCTA for glaucoma diagnosis, highlighting the potential of WF-OCTA images in glaucoma diagnostics. Additionally, it showed that FSL could overcome the limitations associated with a small dataset and is expected to be applicable in various clinical settings.

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Section: Techniques: Proposed Methodsmentioning

confidence: 99%

Section: Study Design and Participantsmentioning

confidence: 99%

Diagnosis of Glaucoma Based on Few-Shot Learning with Wide-Field Optical Coherence Tomography Angiography

Yang,

Lee,

Shin

et al. 2024

Biomedicines

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“…Few studies have been published regarding AI algorithms for glaucoma detection from widefield fundus photos. Shin et al 31 compared CNN-based algorithms using ultrawide-field (UWF) fundus images versus true-color confocal scans for the diagnosis of glaucoma. In this study, the DL algorithm based on UWF imaging achieved a higher accuracy and AUC (ACC 83.62%, AUC 0.904 vs. ACC 81.46%, AUC 0.868).…”

Section: Widefield Fundus Photo Algorithmsmentioning

confidence: 99%

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Chen

Anran

Tan

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Diagnosis and detection of progression of glaucoma remains challenging. Artificial intelligence-based tools have the potential to improve and standardize the assessment of glaucoma but development of these algorithms is difficult given the multimodal and variable nature of the diagnosis. Currently, most algorithms are focused on a single imaging modality, specifically screening and diagnosis based on fundus photos or optical coherence tomography images. Use of anterior segment optical coherence tomography and goniophotographs is limited. The majority of algorithms designed for disease progression prediction are based on visual fields. No studies in our literature search assessed the use of artificial intelligence for treatment response prediction and no studies conducted prospective testing of their algorithms. Additional challenges to the development of artificial intelligence-based tools include scarcity of data and a lack of consensus in diagnostic criteria. Although research in the use of artificial intelligence for glaucoma is promising, additional work is needed to develop clinically usable tools.

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“…It can be used for automated central fundus lesion detection, even in external datasets (collected by different types of cameras) from subjects with various ethnic backgrounds in two countries. In 2022, Shin et al [36] evaluated and compared the performance of UWF imaging and truecolor confocal scanning images in detecting glaucoma based on the DL classifier. They found that the ability of DL-based UWF imaging and true-color confocal scanning to diagnose glaucoma was comparable to that of the OCT parameter-based method.…”

Section: Introductionmentioning

confidence: 99%

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images

Tang,

Wang,

et al. 2024

Int J Ophthalmol

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AIM: To summarize the application of deep learning in detecting ophthalmic disease with ultrawide-field fundus images and analyze the advantages, limitations, and possible solutions common to all tasks. METHODS: We searched three academic databases, including PubMed, Web of Science, and Ovid, with the date of August 2022. We matched and screened according to the target keywords and publication year and retrieved a total of 4358 research papers according to the keywords, of which 23 studies were retrieved on applying deep learning in diagnosing ophthalmic disease with ultrawide-field images. RESULTS: Deep learning in ultrawide-field images can detect various ophthalmic diseases and achieve great performance, including diabetic retinopathy, glaucoma, age-related macular degeneration, retinal vein occlusions, retinal detachment, and other peripheral retinal diseases. Compared to fundus images, the ultrawide-field fundus scanning laser ophthalmoscopy enables the capture of the ocular fundus up to 200° in a single exposure, which can observe more areas of the retina. CONCLUSION: The combination of ultrawide-field fundus images and artificial intelligence will achieve great performance in diagnosing multiple ophthalmic diseases in the future.

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Comparison between Deep-Learning-Based Ultra-Wide-Field Fundus Imaging and True-Colour Confocal Scanning for Diagnosing Glaucoma

Cited by 7 publications

References 41 publications

Diagnosis of Glaucoma Based on Few-Shot Learning with Wide-Field Optical Coherence Tomography Angiography

Diagnosis of Glaucoma Based on Few-Shot Learning with Wide-Field Optical Coherence Tomography Angiography

Applications of Artificial Intelligence and Deep Learning in Glaucoma

Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images

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