Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions

Fan, Rui; Alipour, Kamran; Bowd, Christopher; Christopher, Mark; Brye, Nicole; Proudfoot, James; Goldbaum, Michael H.; Belghith, Akram; Girkin, Christopher A.; Fazio, Massimo A.; Liebmann, Jeffrey M.; Weinreb, Robert N.; Pazzani, Michael J.; Kriegman, David; Zangwill, Linda M.

doi:10.1016/j.xops.2022.100233

Cited by 43 publications

(33 citation statements)

References 34 publications

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“…In the current study, a relatively original strategy was used to train, test, and improve an AI method that could be useful to detect glaucoma in real patient data. The selected deep learning method was the ResNet-50, which is considered adequate, in that several authors with the same objective have already used it and obtained good results similar to other deep learning techniques at least for detecting glaucomas in an ocular hypertension sample [31]. The state-of-the-art typically uses convolutional layers to represent fundus photographs with one-dimensional visual features [10, 31].…”

Section: Discussionmentioning

confidence: 99%

“…Furthermore, these results should be externally validated in a larger multicenter study with prospective data acquired in routine clinical practice. Fan et al [31] affirmed that the AI-based diagnostic accuracy for glaucoma diagnosis using fundus photography and optical coherence tomography imaging was not better in external datasets compared to the original data source. Different glaucoma definitions between the external datasets also may explain the differences in performance (e.g., visual field vs. expert photography review, cup-to-disc ratio, etc.)…”

Section: Discussionmentioning

confidence: 99%

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Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population

Cellini,

Caamaño,

Carrasco

et al. 2023

Ophthalmic Res

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Introduction: Artificial intelligence has real potential for early identification of ocular diseases such as glaucoma. An important challenge is the requirement for large databases properly selected, which are not easily obtained. We used a relatively original strategy: a glaucoma recognition algorithm trained with fundus images from public databases and then tested and retrained with a carefully selected patient database. Methods: The study’s supervised deep learning method was an adapted version of the ResNet-50 architecture previously trained from 10,658 optic head images (glaucomatous or non-glaucomatous) from seven public databases. A total of 1,158 new images labeled by experts from 616 patients were added. The images were categorized after clinical examination including visual fields in 304 (26%) control images or those with ocular hypertension and 347 (30%) images with early, 290 (25%) with moderate, and 217 (19%) with advanced glaucoma. The initial algorithm was tested using 30% of the selected glaucoma database and then re-trained with 70% of this database and tested again. Results: The results in the initial sample showed an area under the curve (AUC) of 76% for all images, and 66% for early, 82% for moderate, and 84% for advanced glaucoma. After retraining the algorithm, the respective AUC results were 82%, 72%, 89%, and 91%. Conclusion: Using combined data from public databases and data selected and labeled by experts facilitated improvement of the system’s precision and identified interesting possibilities for obtaining tools for automatic screening of glaucomatous eyes more affordably.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population

Cellini,

Caamaño,

Carrasco

et al. 2023

Ophthalmic Res

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“…While in this research both ViT and CNN (Res-Net-50) models performed similarly on the OHTS training set, the ViT model outperformed the classical CNN model in external data sets, suggesting the former has greater generalizability. Moreover, saliency and attention maps provided by the ViT model were more focused on neuroretinal rim areas than with the CNN models which showed more diffusely scored maps [14 ▪▪ ]. Longitudinal image acquisition may also be relevant in training AI algorithms as shown by Lin et al , using a Siamese CNN model [15].…”

Section: Detecting Glaucoma With Artificial Intelligence In Different...mentioning

confidence: 98%

“…The same publication proposed an image processing pipeline prior to DL testing to improve generalizability across external data sets and may also play a part in standardizing input quality (see above) [42]. The work previously discussed by Fan et al also suggests different DL architectures, such as ViT, may improve on generalizability [14 ▪▪ ];…”

Section: Challenges and Future Directionsmentioning

confidence: 99%

Artificial intelligence for glaucoma: state of the art and future perspectives

Correia Barão,

Hemelings,

Abegão Pinto

et al. 2023

Current Opinion in Ophthalmology

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Purpose of review To address the current role of artificial intelligence (AI) in the field of glaucoma. Recent findings Current deep learning (DL) models concerning glaucoma diagnosis have shown consistently improving diagnostic capabilities, primarily based on color fundus photography and optical coherence tomography, but also with multimodal strategies. Recent models have also suggested that AI may be helpful in detecting and estimating visual field progression from different input data. Moreover, with the emergence of newer DL architectures and synthetic data, challenges such as model generalizability and explainability have begun to be tackled. Summary : While some challenges remain before AI is routinely employed in clinical practice, new research has expanded the range in which it can be used in the context of glaucoma management and underlined the relevance of this research avenue.

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“…16 Recently, a new data-efficient image transformer algorithm has been proposed as an alternative approach, generating AI models with greater generalizability than ResNet and superior explainability compared with saliency maps. 58 Efforts to explain models' decision-making processes will likely continue to evolve and provide developers and validators with areas to focus on for improving performance.…”

Section: Design Features To Enhance Artificial Intelligence Explainab...mentioning

confidence: 99%

Review of Visualization Approaches in Deep Learning Models of Glaucoma

Sidhu

Weinreb

et al. 2023

Asia-Pacific Journal of Ophthalmology

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Glaucoma is a major cause of irreversible blindness worldwide. As glaucoma often presents without symptoms, early detection and intervention are important in delaying progression. Deep learning (DL) has emerged as a rapidly advancing tool to help achieve these objectives. In this narrative review, data types and visualization approaches for presenting model predictions, including models based on tabular data, functional data, and/or structural data, are summarized, and the importance of data source diversity for improving the utility and generalizability of DL models is explored. Examples of innovative approaches to understanding predictions of artificial intelligence (AI) models and alignment with clinicians are provided. In addition, methods to enhance the interpretability of clinical features from tabular data used to train AI models are investigated. Examples of published DL models that include interfaces to facilitate end-user engagement and minimize cognitive and time burdens are highlighted. The stages of integrating AI models into existing clinical workflows are reviewed, and challenges are discussed. Reviewing these approaches may help inform the generation of user-friendly interfaces that are successfully integrated into clinical information systems. This review details key principles regarding visualization approaches in DL models of glaucoma. The articles reviewed here focused on usability, explainability, and promotion of clinician trust to encourage wider adoption for clinical use. These studies demonstrate important progress in addressing visualization and explainability issues required for successful real-world implementation of DL models in glaucoma.

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Detecting Glaucoma from Fundus Photographs Using Deep Learning without Convolutions

Cited by 43 publications

References 34 publications

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population

Deep Learning Application to Detect Glaucoma with a Mixed Training Approach: Public Database and Expert-Labeled Glaucoma Population

Artificial intelligence for glaucoma: state of the art and future perspectives

Review of Visualization Approaches in Deep Learning Models of Glaucoma

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