Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure

Randhawa, Jasmeen; Chiang, Michael F.; Porporato, Natalia; Pardeshi, Anmol A.; Dredge, Justin; Apolo, Galo; Tun, Tin A.; Quah, Joanne HuiMin; Tan, Marcus; Higashita, Risa; Aung, Tin; Varma, Rohit; Xu, Benjamin

doi:10.1136/bjophthalmol-2021-319470

Cited by 16 publications

(13 citation statements)

References 43 publications

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“…Also, a study showed the consistent performance of an OCT-based DL classifier for detecting gonioscopic angle closure among different populations. 32 Forth, we categorized the eyes based on gonioscopy and ONH evaluations. Labeling was performed primarily by 1 expert examiner, which could limit classifier generalizability due to inherent interexaminer differences in gonioscopy grading.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography

Eslami

Kouzehkanan

Farzinvash

et al. 2023

Journal of Glaucoma

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Précis: We developed a deep learning-based classifier that can discriminate primary angle closure suspects (PACS), primary angle closure (PAC)/primary angle closure glaucoma (PACG), and also control eyes with open angle with acceptable accuracy. Purpose: To develop a deep learning-based classifier for differentiating subtypes of primary angle closure disease, including PACS and PAC/PACG, and also normal control eyes. Materials and Methods: Anterior segment optical coherence tomography images were used for analysis with 5 different networks including MnasNet, MobileNet, ResNet18, ResNet50, and EfficientNet. The data set was split with randomization performed at the patient level into a training plus validation set (85%), and a test data set (15%). Then 4-fold cross-validation was used to train the model. In each mentioned architecture, the networks were trained with original and cropped images. Also, the analyses were carried out for single images and images grouped on the patient level (case-based). Then majority voting was applied to the determination of the final prediction. Results: A total of 1616 images of normal eyes (87 eyes), 1055 images of PACS (66 eyes), and 1076 images of PAC/PACG (66 eyes) eyes were included in the analysis. The mean ± SD age was 51.76 ± 15.15 years and 48.3% were males. MobileNet had the best performance in the model, in which both original and cropped images were used. The accuracy of MobileNet for detecting normal, PACS, and PAC/PACG eyes was 0.99 ± 0.00, 0.77 ± 0.02, and 0.77 ± 0.03, respectively. By running MobileNet in a case-based classification approach, the accuracy improved and reached 0.95 ± 0.03, 0.83 ± 0.06, and 0.81 ± 0.05, respectively. For detecting the open angle, PACS, and PAC/PACG, the MobileNet classifier achieved an area under the curve of 1, 0.906, and 0.872, respectively, on the test data set. Conclusion: The MobileNet-based classifier can detect normal, PACS, and PAC/PACG eyes with acceptable accuracy based on anterior segment optical coherence tomography images.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography

Eslami

Kouzehkanan

Farzinvash

et al. 2023

Journal of Glaucoma

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“…Measurements of AOD and TISA are associated with IOP and anatomical variations in PACD eyes and may predict a higher risk of PACD progression or poor angle widening after LPI. 8,15,16 In addition, automated measurements of ACW and LV could be beneficial for IOL selection: ACW is helpful in sizing anterior chamber and phakic IOLs, and there is evidence that LV could play an important role in determining effective lens position and calculating IOL power. [18][19][20][21][22][23] Our results demonstrate that rates of scleral spur detection are highly variable under realworld conditions without eyelid retraction during imaging, even among experienced graders.…”

Section: Discussionmentioning

confidence: 99%

“…3,8,9 Quantitative OCT-based methods could complement gonioscopy, which remains the current standard for assessing the ACA despite being subjective, qualitative, variably reproducible, and weakly correlated with AS-OCT measurements of angle width. [10][11][12][13][14][15][16] In IOL selection, biometric parameters, including corneal curvature, anterior chamber depth (ACD), and lens thickness (LT), are measured using optical or ultrasound methods and factored into modern IOL calculators. 17 Anterior chamber width (ACW), also referred to as white-to-white (WTW) distance, is important for sizing anterior chamber and phakic IOLs.…”

Section: Introductionmentioning

confidence: 99%

Automated Expert-level Scleral Spur Detection and Quantitative Biometric Analysis on the ANTERION Anterior Segment OCT System

Bolo

Apolo

Pardeshi

et al. 2022

Preprint

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Aim: To perform an independent validation of deep learning (DL) algorithms for automated scleral spur detection and measurement of scleral spur-based biometric parameters in anterior segment optical coherence tomography (AS-OCT) images. Methods: Patients receiving routine eye care underwent AS-OCT imaging using the ANTERION OCT system (Heidelberg Engineering, Heidelberg, Germany). Scleral spur locations were marked by three human graders (Reference, Expert, and Novice) and predicted using DL algorithms developed by Heidelberg Engineering that prioritize a false positive rate <4% (FPR4) or true positive rate >95% (TPR95). Performance of human graders and DL algorithms were evaluated based on agreement of scleral spur locations and biometric measurements with the Reference Grader. Results: 1,308 AS-OCT images were obtained from 117 participants. Median differences in scleral spur locations from reference locations were significantly smaller (p<0.001) for the FPR4 (52.6±48.6μm) and TPR95 (55.5±50.6μm) algorithms compared to the Expert (61.1±65.7μm) and Novice (79.4±74.9μm) Graders. Inter-grader reproducibility of biometric measurements was excellent overall for all four (intraclass correlation coefficient [ICC] range 0.918-0.997). Inter-grader reproducibility of the Expert Grader [0.567-0.965] and DL algorithms [0.746-0.979] exceeded that of the Novice Grader [0.146-0.929] for images with narrow angles, defined as angle opening distance 500μm from the scleral spur (AOD500) <150μm. Conclusions: DL algorithms on the ANTERION approximate expert-level measurement of scleral spur-based biometric parameters in an independent patient population. These algorithms could enhance clinical utility of AS-OCT imaging, especially for evaluating patients with angle closure and performing intraocular lens (IOL) calculations.

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“…As opposed to diagnosis at a more advanced stage, early treatment initiation can both prevent irreversible vision loss and avoid expensive, invasive techniques used for later glaucoma stages, as the cost of management increases with glaucoma progression; its performance, however, still needs to be improved (327). The implementation of DL to AS-OCT is a fieldattracting attention; Li et al have very recently developed a novel 3D deep-learning-based digital gonioscopy system that identified angle closure suspects and that could be used as a screening method for primary angle closure glaucoma (371)(372)(373)(374)(375)(376).…”

Section: Artificial Intelligence and Glaucomamentioning

confidence: 99%

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

et al. 2022

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The field of ophthalmic imaging has grown substantially over the last years. Massive improvements in image processing and computer hardware have allowed the emergence of multiple imaging techniques of the eye that can transform patient care. The purpose of this review is to describe the most recent advances in eye imaging and explain how new technologies and imaging methods can be utilized in a clinical setting. The introduction of optical coherence tomography (OCT) was a revolution in eye imaging and has since become the standard of care for a plethora of conditions. Its most recent iterations, OCT angiography, and visible light OCT, as well as imaging modalities, such as fluorescent lifetime imaging ophthalmoscopy, would allow a more thorough evaluation of patients and provide additional information on disease processes. Toward that goal, the application of adaptive optics (AO) and full-field scanning to a variety of eye imaging techniques has further allowed the histologic study of single cells in the retina and anterior segment. Toward the goal of remote eye care and more accessible eye imaging, methods such as handheld OCT devices and imaging through smartphones, have emerged. Finally, incorporating artificial intelligence (AI) in eye images has the potential to become a new milestone for eye imaging while also contributing in social aspects of eye care.

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Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure

Cited by 16 publications

References 43 publications

Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography

Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography

Automated Expert-level Scleral Spur Detection and Quantitative Biometric Analysis on the ANTERION Anterior Segment OCT System

The Development and Clinical Application of Innovative Optical Ophthalmic Imaging Techniques

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