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Soh ZD, Tan M, Nongpiur ME, Xu BY, Friedman D, Zhang X, Leung C, Liu Y, Koh V, Aung T, Cheng CY. Assessment of angle closure disease in the age of artificial intelligence: A review. Prog Retin Eye Res 2024; 98:101227. [PMID: 37926242 DOI: 10.1016/j.preteyeres.2023.101227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2023] [Revised: 11/02/2023] [Accepted: 11/02/2023] [Indexed: 11/07/2023]
Abstract
Primary angle closure glaucoma is a visually debilitating disease that is under-detected worldwide. Many of the challenges in managing primary angle closure disease (PACD) are related to the lack of convenient and precise tools for clinic-based disease assessment and monitoring. Artificial intelligence (AI)- assisted tools to detect and assess PACD have proliferated in recent years with encouraging results. Machine learning (ML) algorithms that utilize clinical data have been developed to categorize angle closure eyes by disease mechanism. Other ML algorithms that utilize image data have demonstrated good performance in detecting angle closure. Nonetheless, deep learning (DL) algorithms trained directly on image data generally outperformed traditional ML algorithms in detecting PACD, were able to accurately differentiate between angle status (open, narrow, closed), and automated the measurement of quantitative parameters. However, more work is required to expand the capabilities of these AI algorithms and for deployment into real-world practice settings. This includes the need for real-world evaluation, establishing the use case for different algorithms, and evaluating the feasibility of deployment while considering other clinical, economic, social, and policy-related factors.
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Affiliation(s)
- Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore.
| | - Mingrui Tan
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*Star), 1 Fusionopolis Way, 138632, Singapore.
| | - Monisha Esther Nongpiur
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore.
| | - Benjamin Yixing Xu
- Roski Eye Institute, Keck School of Medicine, University of Southern California, 1450 San Pablo St #4400, Los Angeles, CA, 90033, USA.
| | - David Friedman
- Department of Ophthalmology, Harvard Medical School, 25 Shattuck Street, Boston, MA, 02115, USA; Massachusetts Eye and Ear, Mass General Brigham, 243 Charles Street, Boston, MA, 02114, USA.
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat Sen University, No. 54 Xianlie South Road, Yuexiu District, Guangzhou, China.
| | - Christopher Leung
- Department of Ophthalmology, School of Clinical Medicine, The University of Hong Kong, Cyberport 4, 100 Cyberport Road, Hong Kong; Department of Ophthalmology, Queen Mary Hospital, 102 Pok Fu Lam Road, Hong Kong.
| | - Yong Liu
- Institute of High Performance Computing, Agency for Science, Technology and Research (A*Star), 1 Fusionopolis Way, 138632, Singapore.
| | - Victor Koh
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore; Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 7, 119228, Singapore.
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore.
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, 20 College Road, 169856, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, 21 Lower Kent Ridge Road, 119077, Singapore; Ophthalmology & Visual Sciences Academic Clinical Programme, Academic Medicine, Duke-NUS Medical School, 8 College Road, 169857, Singapore; Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, 1E Kent Ridge Road, NUHS Tower Block, Level 7, 119228, Singapore.
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Wiedenmann CJ, Böhringer D, Reinhard T, Wacker K. [Corneal endothelial cell photography: comparison of smartphones]. DIE OPHTHALMOLOGIE 2023; 120:382-389. [PMID: 36301370 DOI: 10.1007/s00347-022-01742-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/21/2022] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 04/29/2023]
Abstract
BACKGROUND The documentation of ophthalmologic findings using smartphone photography can confirm diagnoses and enable follow-up assessments in outpatient care. Photographing corneal endothelial cells using a smartphone on a slit lamp is complex for both smartphone and examiner. Smartphone models differ in their ability to quickly and safely take images of the corneal endothelium. AIM OF THIS WORK In this paper different smartphone models are presented with respect to their applicability for corneal endothelial cell photography and success factors for good smartphone imaging are described. MATERIAL AND METHODS In a cross-sectional study, a selection of 16 different smartphone models were compared with respect to their use in corneal endothelial cell photography. The smartphones were attached to the slit lamp eyepiece using an adjustable adapter. It was tested whether high-quality endothelial cell images of healthy subjects could be obtained within 3 min using the standard photo app of the respective smartphone. In addition, the subjective difficulty of creating the image was recorded. Factors contributing to successful imaging of corneal endothelial cells were summarized in a figure. RESULTS AND DISCUSSION Distinct differences regarding feasibility and quality of endothelial cell photography were detected between the different smartphones. Not every smartphone is suitable for endothelial cell photography.
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Affiliation(s)
| | - Daniel Böhringer
- Klinik für Augenheilkunde, Universitätsklinikum Freiburg, Killianstr. 5, 79106, Freiburg, Deutschland
| | - Thomas Reinhard
- Klinik für Augenheilkunde, Universitätsklinikum Freiburg, Killianstr. 5, 79106, Freiburg, Deutschland
| | - Katrin Wacker
- Klinik für Augenheilkunde, Universitätsklinikum Freiburg, Killianstr. 5, 79106, Freiburg, Deutschland
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Soh ZD, Jiang Y, S/O Ganesan SS, Zhou M, Nongiur M, Majithia S, Tham YC, Rim TH, Qian C, Koh V, Aung T, Wong TY, Xu X, Liu Y, Cheng CY. From 2 dimensions to 3rd dimension: Quantitative prediction of anterior chamber depth from anterior segment photographs via deep-learning. PLOS DIGITAL HEALTH 2023; 2:e0000193. [PMID: 36812642 PMCID: PMC9931242 DOI: 10.1371/journal.pdig.0000193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Accepted: 01/06/2023] [Indexed: 02/04/2023]
Abstract
Anterior chamber depth (ACD) is a major risk factor of angle closure disease, and has been used in angle closure screening in various populations. However, ACD is measured from ocular biometer or anterior segment optical coherence tomography (AS-OCT), which are costly and may not be readily available in primary care and community settings. Thus, this proof-of-concept study aims to predict ACD from low-cost anterior segment photographs (ASPs) using deep-learning (DL). We included 2,311 pairs of ASPs and ACD measurements for algorithm development and validation, and 380 pairs for algorithm testing. We captured ASPs with a digital camera mounted on a slit-lamp biomicroscope. Anterior chamber depth was measured with ocular biometer (IOLMaster700 or Lenstar LS9000) in data used for algorithm development and validation, and with AS-OCT (Visante) in data used for testing. The DL algorithm was modified from the ResNet-50 architecture, and assessed using mean absolute error (MAE), coefficient-of-determination (R2), Bland-Altman plot and intraclass correlation coefficients (ICC). In validation, our algorithm predicted ACD with a MAE (standard deviation) of 0.18 (0.14) mm; R2 = 0.63. The MAE of predicted ACD was 0.18 (0.14) mm in eyes with open angles and 0.19 (0.14) mm in eyes with angle closure. The ICC between actual and predicted ACD measurements was 0.81 (95% CI 0.77, 0.84). In testing, our algorithm predicted ACD with a MAE of 0.23 (0.18) mm; R2 = 0.37. Saliency maps highlighted the pupil and its margin as the main structures used in ACD prediction. This study demonstrates the possibility of predicting ACD from ASPs via DL. This algorithm mimics an ocular biometer in making its prediction, and provides a foundation to predict other quantitative measurements that are relevant to angle closure screening.
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Affiliation(s)
- Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Yixing Jiang
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | | | - Menghan Zhou
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Monisha Nongiur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Shivani Majithia
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Chaoxu Qian
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Victor Koh
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Department of Ophthalmology, National University Hospital, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
- Tsinghua Medicine, Tsinghua University, China
| | - Xinxing Xu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Yong Liu
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
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Goel R, Macri C, Bahrami B, Casson R, Chan WO. Assessing the subjective quality of smartphone anterior segment photography: a non-inferiority study. Int Ophthalmol 2023; 43:403-410. [PMID: 36018419 PMCID: PMC9411845 DOI: 10.1007/s10792-022-02437-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2022] [Accepted: 07/05/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE To assess the subjective quality of anterior segment photos taken from a smartphone camera adapted to the slit lamp compared to a commercial inbuilt slit-lamp camera. METHODS Non-inferiority study. Five paired images of the anterior segment of normal eyes were taken using an iPhone 11 (Apple, Inc., Calif., USA) camera attached to a universal slit-lamp adaptor and a commercial inbuilt slit-lamp camera (Haag-Streit Diagnostics, Bern, Switzerland). Images were collated into a survey in which ophthalmology students, residents, registrars, and consultants participated to select the image taken from the inbuilt slit-lamp camera. If the image quality was subjectively indistinguishable, we expected a 50:50 split for each photograph that was presented. We selected a 10% non-inferiority margin, with the hypothesis that no less than 40% of images believed to be from the conventional camera were in fact from the smartphone camera. RESULTS There were 27 respondents in the survey: ophthalmology consultants (n = 7), registrars (n = 10), residents (n = 7), intern (n = 1) and students (n = 2). The mean correct identification across the respondents was 11.3 out of 25 (45.2%) images. Overall, the smartphone camera was non-inferior to the inbuilt slit-lamp camera (p < 0.001). The non-inferiority of the smartphone camera was significant for consultants (47.4%, p < 0.01), registrars (47.6%, p < 0.001) and residents (37.7%, p < 0.0001). CONCLUSIONS Anterior segment images obtained with a smartphone camera were non-inferior to the commercial inbuilt slit-lamp camera. Smartphone cameras may be a non-inferior tool for communication of anterior segment images having implications for the ease of access to quality telehealth consultations.
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Affiliation(s)
- Raghav Goel
- Discipline of Ophthalmology and Visual Sciences, The University of Adelaide, North Terrace, Adelaide, South Australia, 5000, Australia.
| | - Carmelo Macri
- grid.416075.10000 0004 0367 1221Department of Ophthalmology, Royal Adelaide Hospital, Port Road, Adelaide, South Australia 5000 Australia
| | - Bobak Bahrami
- grid.1010.00000 0004 1936 7304Discipline of Ophthalmology and Visual Sciences, The University of Adelaide, North Terrace, Adelaide, South Australia 5000 Australia
| | - Robert Casson
- grid.1010.00000 0004 1936 7304Discipline of Ophthalmology and Visual Sciences, The University of Adelaide, North Terrace, Adelaide, South Australia 5000 Australia
| | - Weng Onn Chan
- grid.1010.00000 0004 1936 7304Discipline of Ophthalmology and Visual Sciences, The University of Adelaide, North Terrace, Adelaide, South Australia 5000 Australia
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Qian C, Jiang Y, Soh ZD, Sakthi Selvam G, Xiao S, Tham YC, Xu X, Liu Y, Li J, Zhong H, Cheng CY. Smartphone-Acquired Anterior Segment Images for Deep Learning Prediction of Anterior Chamber Depth: A Proof-of-Concept Study. Front Med (Lausanne) 2022; 9:912214. [PMID: 35814744 PMCID: PMC9259953 DOI: 10.3389/fmed.2022.912214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2022] [Accepted: 05/09/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose To develop a deep learning (DL) algorithm for predicting anterior chamber depth (ACD) from smartphone-acquired anterior segment photographs. Methods For algorithm development, we included 4,157 eyes from 2,084 Chinese primary school students (aged 11–15 years) from Mojiang Myopia Progression Study (MMPS). All participants had with ACD measurement measured with Lenstar (LS 900) and anterior segment photographs acquired from a smartphone (iPhone Xs), which was mounted on slit lamp and under diffuses lighting. The anterior segment photographs were randomly selected by person into training (80%, no. of eyes = 3,326) and testing (20%, no. of eyes = 831) dataset. We excluded participants with intraocular surgery history or pronounced corneal haze. A convolutional neural network was developed to predict ACD based on these anterior segment photographs. To determine the accuracy of our algorithm, we measured the mean absolute error (MAE) and coefficient of determination (R2) were evaluated. Bland Altman plot was used to illustrate the agreement between DL-predicted and measured ACD values. Results In the test set of 831 eyes, the mean measured ACD was 3.06 ± 0.25 mm, and the mean DL-predicted ACD was 3.10 ± 0.20 mm. The MAE was 0.16 ± 0.13 mm, and R2 was 0.40 between the predicted and measured ACD. The overall mean difference was −0.04 ± 0.20 mm, with 95% limits of agreement ranging between −0.43 and 0.34 mm. The generated saliency maps showed that the algorithm mainly utilized central corneal region (i.e., the site where ACD is clinically measured typically) in making its prediction, providing further plausibility to the algorithm's prediction. Conclusions We developed a DL algorithm to estimate ACD based on smartphone-acquired anterior segment photographs. Upon further validation, our algorithm may be further refined for use as a ACD screening tool in rural localities where means of assessing ocular biometry is not readily available. This is particularly important in China where the risk of primary angle closure disease is high and often undetected.
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Affiliation(s)
- Chaoxu Qian
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yixing Jiang
- Institute of High Performance Computing, Agency for Science, Technology and Research (AStar), Singapore, Singapore
| | - Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Ganesan Sakthi Selvam
- Institute of High Performance Computing, Agency for Science, Technology and Research (AStar), Singapore, Singapore
| | - Shuyuan Xiao
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Yih-Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
| | - Xinxing Xu
- Institute of High Performance Computing, Agency for Science, Technology and Research (AStar), Singapore, Singapore
| | - Yong Liu
- Institute of High Performance Computing, Agency for Science, Technology and Research (AStar), Singapore, Singapore
| | - Jun Li
- Department of Ophthalmology, The Second People's Hospital of Yunnan Province, Kunming, China
| | - Hua Zhong
- Department of Ophthalmology, The First Affiliated Hospital of Kunming Medical University, Kunming, China
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Ophthalmology and Visual Sciences Academic Clinical Program (Eye ACP), Duke-NUS Medical School, Singapore, Singapore
- *Correspondence: Ching-Yu Cheng
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A Review of Portable Quantitative and Semi-quantitative Devices for Measurement of Vitamin A in Biological Samples. CURRENT RESEARCH IN BIOTECHNOLOGY 2022; 4:253-274. [PMID: 36033130 PMCID: PMC9407042 DOI: 10.1016/j.crbiot.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 04/27/2022] [Accepted: 04/28/2022] [Indexed: 11/21/2022] Open
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Zhang C, Zhao J, Zhu Z, Li Y, Li K, Wang Y, Zheng Y. Applications of Artificial Intelligence in Myopia: Current and Future Directions. Front Med (Lausanne) 2022; 9:840498. [PMID: 35360739 PMCID: PMC8962670 DOI: 10.3389/fmed.2022.840498] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 12/17/2022] Open
Abstract
With the continuous development of computer technology, big data acquisition and imaging methods, the application of artificial intelligence (AI) in medical fields is expanding. The use of machine learning and deep learning in the diagnosis and treatment of ophthalmic diseases is becoming more widespread. As one of the main causes of visual impairment, myopia has a high global prevalence. Early screening or diagnosis of myopia, combined with other effective therapeutic interventions, is very important to maintain a patient's visual function and quality of life. Through the training of fundus photography, optical coherence tomography, and slit lamp images and through platforms provided by telemedicine, AI shows great application potential in the detection, diagnosis, progression prediction and treatment of myopia. In addition, AI models and wearable devices based on other forms of data also perform well in the behavioral intervention of myopia patients. Admittedly, there are still some challenges in the practical application of AI in myopia, such as the standardization of datasets; acceptance attitudes of users; and ethical, legal and regulatory issues. This paper reviews the clinical application status, potential challenges and future directions of AI in myopia and proposes that the establishment of an AI-integrated telemedicine platform will be a new direction for myopia management in the post-COVID-19 period.
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