1
|
Chen D, Han Y, Duncan J, Jia L, Shan J. Generative Artificial Intelligence Enhancements for Reducing Image-based Training Data Requirements. OPHTHALMOLOGY SCIENCE 2024; 4:100531. [PMID: 39071920 PMCID: PMC11283142 DOI: 10.1016/j.xops.2024.100531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/07/2023] [Revised: 04/08/2024] [Accepted: 04/09/2024] [Indexed: 07/30/2024]
Abstract
Objective Training data fuel and shape the development of artificial intelligence (AI) models. Intensive data requirements are a major bottleneck limiting the success of AI tools in sectors with inherently scarce data. In health care, training data are difficult to curate, triggering growing concerns that the current lack of access to health care by under-privileged social groups will translate into future bias in health care AIs. In this report, we developed an autoencoder to grow and enhance inherently scarce datasets to alleviate our dependence on big data. Design Computational study with open-source data. Subjects The data were obtained from 6 open-source datasets comprising patients aged 40-80 years in Singapore, China, India, and Spain. Methods The reported framework generates synthetic images based on real-world patient imaging data. As a test case, we used autoencoder to expand publicly available training sets of optic disc photos, and evaluated the ability of the resultant datasets to train AI models in the detection of glaucomatous optic neuropathy. Main Outcome Measures Area under the receiver operating characteristic curve (AUC) were used to evaluate the performance of the glaucoma detector. A higher AUC indicates better detection performance. Results Results show that enhancing datasets with synthetic images generated by autoencoder led to superior training sets that improved the performance of AI models. Conclusions Our findings here help address the increasingly untenable data volume and quality requirements for AI model development and have implications beyond health care, toward empowering AI adoption for all similarly data-challenged fields. Financial Disclosures The authors have no proprietary or commercial interest in any materials discussed in this article.
Collapse
Affiliation(s)
- Dake Chen
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California
| | - Ying Han
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California
| | - Jacque Duncan
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California
| | - Lin Jia
- Digillect LLC, San Francisco, California
| | - Jing Shan
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California
| |
Collapse
|
2
|
Porporato N, Xu BY, Tan B, Chang YQ, Tun TA, Perera S, Husain R, Ho CL, Sultana R, Aung T, Schmetterer L. Novel hyperparallel optical coherence tomography for angle closure assessment: comparison with swept-source OCT and gonioscopy. Br J Ophthalmol 2024:bjo-2023-323429. [PMID: 39214678 DOI: 10.1136/bjo-2023-323429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Accepted: 06/21/2024] [Indexed: 09/04/2024]
Abstract
AIM To evaluate reproducibility and agreement of angle closure assessment by a novel hyperparallel optical coherence tomography (OCT) system (HP-OCT, Cylite Optics, Melbourne, Australia), in comparison with swept-source OCT (SS-OCT, CASIA SS-1000, Tomey Corporation, Nagoya, Japan) and gonioscopy. METHODS Cross-sectional study. Phakic subjects >40 years, with no relevant ophthalmic history were consecutively recruited from the glaucoma clinic. Subjects underwent same-day evaluation with HP-OCT, SS-OCT and gonioscopy. The primary outcome was the presence of angle closure, defined as iridotrabecular contact in HP-OCT and SS-OCT images at 0°-180° meridional and as non-visibility of the posterior trabecular meshwork (TM) by gonioscopy. Visibility of TM was also assessed (secondary outcome). Intra and interdevice agreement analysis (Gwet AC1) and logistic regression analysis were performed for primary and secondary outcomes, respectively. RESULTS 154 sectors from horizontal scans of 77 subjects were analysed. The reproducibility of angle closure assessment by HP-OCT was excellent (AC1 of 0.95 for temporal angle and 1.00 for nasal). Agreement for angle closure detection was very good between HP-OCT and SS-OCT (AC1 of 0.88 for temporal and 0.81 for nasal angle) and good between HP-OCT and gonioscopy (AC1 of 0.71 for temporal and 0.78 for nasal angle). TM was identifiable in 64.4% (94/146) of unprocessed HP-OCT images (both open and closed angles), however not visible in any of the SS-OCT unprocessed images. CONCLUSIONS HP-OCT showed excellent reproducibility for angle closure assessment and good agreement with SS-OCT and gonioscopy. HP-OCT technology also provides a unique capability to visualise regions around TM and Schlemm's canal, opening new avenues for clinical research of distal outflow pathways.
Collapse
Affiliation(s)
- Natalia Porporato
- Singapore Eye Research Institute, Singapore
- Duke-NUS Graduate Medical School, Singapore
| | - Benjamin Y Xu
- Ophthalmology, Roski Eye Institute, Keck School of Medicine, University of Southern California, Los Angeles, California, USA
| | - Bingyao Tan
- Ocular Imaging, Singapore Eye Research Institute, Singapore
- Nanyang Technological University School of Chemical and Biomedical Engineering, Singapore
| | - Yu Q Chang
- University of Medicine and Health Sciences, Dublin, Ireland
| | - Tin A Tun
- Singapore Eye Research Institute, Singapore
- Duke-NUS Graduate Medical School, Singapore
| | - Shamira Perera
- Singapore Eye Research Institute, Singapore
- Duke-NUS Graduate Medical School, Singapore
| | - Rahat Husain
- Singapore Eye Research Institute, Singapore
- Duke-NUS Graduate Medical School, Singapore
| | - Ching Lin Ho
- Duke-NUS Graduate Medical School, Singapore
- Glaucoma, Singapore Eye Research Institute, Singapore
| | | | - Tin Aung
- Singapore Eye Research Institute, Singapore
- Duke-NUS Graduate Medical School, Singapore
| | - Leopold Schmetterer
- Singapore Eye Research Institute, Singapore
- Duke-NUS Graduate Medical School, Singapore
- Ocular Imaging, Singapore Eye Research Institute, Singapore
- Singapore National Eye Centre Ocular Imaging Research Group, Singapore
| |
Collapse
|
3
|
Li F, Wang D, Yang Z, Zhang Y, Jiang J, Liu X, Kong K, Zhou F, Tham CC, Medeiros F, Han Y, Grzybowski A, Zangwill LM, Lam DSC, Zhang X. The AI Revolution in Glaucoma: Bridging Challenges with Opportunities. Prog Retin Eye Res 2024:101291. [PMID: 39186968 DOI: 10.1016/j.preteyeres.2024.101291] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 08/28/2024]
Abstract
Recent advancements in artificial intelligence (AI) herald transformative potentials for reshaping glaucoma clinical management, improving screening efficacy, sharpening diagnosis precision, and refining the detection of disease progression. However, incorporating AI into healthcare usages faces significant hurdles in terms of developing algorithms and putting them into practice. When creating algorithms, issues arise due to the intensive effort required to label data, inconsistent diagnostic standards, and a lack of thorough testing, which often limits the algorithms' widespread applicability. Additionally, the "black box" nature of AI algorithms may cause doctors to be wary or skeptical. When it comes to using these tools, challenges include dealing with lower-quality images in real situations and the systems' limited ability to work well with diverse ethnic groups and different diagnostic equipment. Looking ahead, new developments aim to protect data privacy through federated learning paradigms, improving algorithm generalizability by diversifying input data modalities, and augmenting datasets with synthetic imagery. The integration of smartphones appears promising for using AI algorithms in both clinical and non-clinical settings. Furthermore, bringing in large language models (LLMs) to act as interactive tool in medicine may signify a significant change in how healthcare will be delivered in the future. By navigating through these challenges and leveraging on these as opportunities, the field of glaucoma AI will not only have improved algorithmic accuracy and optimized data integration but also a paradigmatic shift towards enhanced clinical acceptance and a transformative improvement in glaucoma care.
Collapse
Affiliation(s)
- Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Deming Wang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Zefeng Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Yinhang Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Jiaxuan Jiang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Xiaoyi Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Kangjie Kong
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| | - Fengqi Zhou
- Ophthalmology, Mayo Clinic Health System, Eau Claire, Wisconsin, USA.
| | - Clement C Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Felipe Medeiros
- Bascom Palmer Eye Institute, University of Miami Miller School of Medicine, Miami, Florida, USA.
| | - Ying Han
- University of California, San Francisco, Department of Ophthalmology, San Francisco, CA, USA; The Francis I. Proctor Foundation for Research in Ophthalmology, University of California, San Francisco, California, USA.
| | - Andrzej Grzybowski
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Linda M Zangwill
- Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology, Shiley Eye Institute, University of California, San Diego, CA, USA.
| | - Dennis S C Lam
- The International Eye Research Institute of The Chinese University of Hong Kong (Shenzhen), Shenzhen, China; The C-MER Dennis Lam & Partners Eye Center, C-MER International Eye Care Group, Hong Kong, China.
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou 510060, China.
| |
Collapse
|
4
|
Guo PY, Zhang X, Li F, Lin C, Nguyen A, Sakata R, Higashita R, Okamoto K, Yu M, Aihara M, Aung T, Lin S, Leung CKS. Diagnostic criteria of anterior segment swept-source optical coherence tomography to detect gonioscopic angle closure. Br J Ophthalmol 2024; 108:1130-1136. [PMID: 38594062 PMCID: PMC11287563 DOI: 10.1136/bjo-2023-323860] [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: 05/07/2023] [Accepted: 11/27/2023] [Indexed: 04/11/2024]
Abstract
AIMS To compare the diagnostic performance of 360° anterior segment optical coherence tomography assessment by applying normative percentile cut-offs versus iris trabecular contact (ITC) for detecting gonioscopic angle closure. METHODS In this multicentre study, 394 healthy individuals were included in the normative dataset to derive the age-specific and angle location-specific normative percentiles of angle open distance (AOD500) and trabecular iris space area (TISA500) which were measured every 10° for 360°. 119 healthy participants and 170 patients with angle closure by gonioscopy were included in the test dataset to investigate the diagnostic performance of three sets of criteria for detection of gonioscopic angle closure: (1) the 10th and (2) the 5th percentiles of AOD500/TISA500, and (3) ITC (ie, AOD500/TISA500=0 mm/mm2). The number of angle locations with angle closure defined by each set of the criteria for each eye was used to generate the receiver operating characteristic (ROC) curve for the discrimination between gonioscopic angle closure and open angle. RESULTS Of the three sets of diagnostic criteria examined, the area under the ROC curve was greatest for the 10th percentile of AOD500 (0.933), whereas the ITC criterion AOD500=0 mm showed the smallest area under the ROC (0.852) and the difference was statistically significant with or without adjusting for age and axial length (p<0.001). The criterion ≥90° of AOD500 below the 10th percentile attained the best sensitivity 87.6% and specificity 84.9% combination for detecting gonioscopic angle closure. CONCLUSIONS Applying the normative percentiles of angle measurements yielded a higher diagnostic performance than ITC for detecting angle closure on gonioscopy.
Collapse
Affiliation(s)
- Philip Yawen Guo
- Department of Ophthalmology, The University of Hong Kong, Pok Fu Lam, People's Republic of China
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, Guangdong, China
| | - Chen Lin
- Shenzhen Aier Eye Hospital, Shenzhen, China
| | - Anwell Nguyen
- Department of Ophthalmology, University of California San Francisco, San Francisco, California, USA
| | - Rei Sakata
- Ophthalmology, The University of Tokyo, Bunkyo-ku, Japan
| | | | | | - Marco Yu
- Singapore Eye Research Institute, Singapore
| | - Makoto Aihara
- Ophthalmology, Tokyo Daigaku Daigakuin Igakukei Kenkyuka Igakubu, Tokyo, Japan
| | - Tin Aung
- Glaucoma, Singapore National Eye Centre, Singapore
| | - Shan Lin
- Department of Ophthalmology, University of California San Francisco, San Francisco, California, USA
| | | |
Collapse
|
5
|
Yoshitsugu K, Shimizu E, Nishimura H, Khemlani R, Nakayama S, Takemura T. Development of the AI Pipeline for Corneal Opacity Detection. Bioengineering (Basel) 2024; 11:273. [PMID: 38534547 DOI: 10.3390/bioengineering11030273] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2024] [Revised: 03/03/2024] [Accepted: 03/07/2024] [Indexed: 03/28/2024] Open
Abstract
Ophthalmological services face global inadequacies, especially in low- and middle-income countries, which are marked by a shortage of practitioners and equipment. This study employed a portable slit lamp microscope with video capabilities and cloud storage for more equitable global diagnostic resource distribution. To enhance accessibility and quality of care, this study targets corneal opacity, which is a global cause of blindness. This study has two purposes. The first is to detect corneal opacity from videos in which the anterior segment of the eye is captured. The other is to develop an AI pipeline to detect corneal opacities. First, we extracted image frames from videos and processed them using a convolutional neural network (CNN) model. Second, we manually annotated the images to extract only the corneal margins, adjusted the contrast with CLAHE, and processed them using the CNN model. Finally, we performed semantic segmentation of the cornea using annotated data. The results showed an accuracy of 0.8 for image frames and 0.96 for corneal margins. Dice and IoU achieved a score of 0.94 for semantic segmentation of the corneal margins. Although corneal opacity detection from video frames seemed challenging in the early stages of this study, manual annotation, corneal extraction, and CLAHE contrast adjustment significantly improved accuracy. The incorporation of manual annotation into the AI pipeline, through semantic segmentation, facilitated high accuracy in detecting corneal opacity.
Collapse
Affiliation(s)
- Kenji Yoshitsugu
- Graduate School of Information Science, University of Hyogo, Kobe Information Science Campus, Kobe 6500047, Japan
- OUI Inc., Tokyo 1070062, Japan
| | - Eisuke Shimizu
- OUI Inc., Tokyo 1070062, Japan
- Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan
| | - Hiroki Nishimura
- OUI Inc., Tokyo 1070062, Japan
- Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan
- Yokohama Keiai Eye Clinic, Kanagawa 2400065, Japan
| | - Rohan Khemlani
- OUI Inc., Tokyo 1070062, Japan
- Yokohama Keiai Eye Clinic, Kanagawa 2400065, Japan
| | - Shintaro Nakayama
- OUI Inc., Tokyo 1070062, Japan
- Department of Ophthalmology, Keio University School of Medicine, Tokyo 1608582, Japan
| | - Tadamasa Takemura
- Graduate School of Information Science, University of Hyogo, Kobe Information Science Campus, Kobe 6500047, Japan
| |
Collapse
|
6
|
Gupta V, Birla S, Varshney T, Somarajan BI, Gupta S, Gupta M, Panigrahi A, Singh A, Gupta D. In vivo identification of angle dysgenesis and its relation to genetic markers associated with glaucoma using artificial intelligence. Indian J Ophthalmol 2024; 72:339-346. [PMID: 38146977 PMCID: PMC11001234 DOI: 10.4103/ijo.ijo_1456_23] [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: 06/04/2023] [Revised: 08/18/2023] [Accepted: 10/06/2023] [Indexed: 12/27/2023] Open
Abstract
PURPOSE To predict the presence of angle dysgenesis on anterior-segment optical coherence tomography (ADoA) by using deep learning (DL) and to correlate ADoA with mutations in known glaucoma genes. PARTICIPANTS In total, 800 high-definition anterior-segment optical coherence tomography (AS-OCT) images were included, of which 340 images were used to build the machine learning (ML) model. Images used to build the ML model included 170 scans of primary congenital glaucoma (16 patients), juvenile-onset open-angle glaucoma (62 patients), and adult-onset primary open-angle glaucoma eyes (37 patients); the rest were controls (n = 85). The genetic validation dataset consisted of another 393 images of patients with known mutations that were compared with 320 images of healthy controls. METHODS ADoA was defined as the absence of Schlemm's canal, the presence of hyperreflectivity over the region of the trabecular meshwork, or a hyperreflective membrane. DL was used to classify a given AS-OCT image as either having angle dysgenesis or not. ADoA was then specifically looked for on AS-OCT images of patients with mutations in the known genes for glaucoma. RESULTS The final prediction, which was a consensus-based outcome from the three optimized DL models, had an accuracy of >95%, a specificity of >97%, and a sensitivity of >96% in detecting ADoA in the internal test dataset. Among the patients with known gene mutations, ( MYOC, CYP1B1, FOXC1, and LTBP2 ) ADoA was observed among all the patients in the majority of the images, compared to only 5% of the healthy controls. CONCLUSION ADoA can be objectively identified using models built with DL.
Collapse
Affiliation(s)
- Viney Gupta
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Shweta Birla
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Jawaharlal Nehru University, New Delhi, India
| | - Toshit Varshney
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Bindu I Somarajan
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Shikha Gupta
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Mrinalini Gupta
- Department of Biomedical Engineering Technical University of Munich, Munich, Germany
| | - Arnav Panigrahi
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Abhishek Singh
- Dr. Rajendra Prasad Centre for Ophthalmic Sciences, All India Institute of Medical Sciences (AIIMS), New Delhi, India
| | - Dinesh Gupta
- Translational Bioinformatics Group, International Centre for Genetic Engineering and Biotechnology (ICGEB), Jawaharlal Nehru University, New Delhi, India
| |
Collapse
|
7
|
Pradhan S, Sah RK, Bhandari G, Bhandari S, Byanju R, Kandel RP, Thompson IJB, Stevens VM, Aromin KM, Oatts JT, Ou Y, Lietman TM, O'Brien KS, Keenan JD. Anterior Segment OCT for Detection of Narrow Angles: A Community-Based Diagnostic Accuracy Study. Ophthalmol Glaucoma 2024; 7:148-156. [PMID: 37611749 DOI: 10.1016/j.ogla.2023.08.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2023] [Revised: 08/13/2023] [Accepted: 08/14/2023] [Indexed: 08/25/2023]
Abstract
PURPOSE To assess the diagnostic accuracy of anterior segment OCT (AS-OCT) screening for detecting gonioscopically narrow angles. DESIGN Population-based cross-sectional study. PARTICIPANTS A stratified random sample of individuals aged ≥ 60 years, selected from a door-to-door census performed in low-lying Nepal. TESTING Participants underwent AS-OCT, posterior segment OCT, and intraocular pressure (IOP) testing in the community. Those meeting referral criteria in either eye were invited to have a comprehensive eye examination including gonioscopy. Referral criteria included (i) the lowest 2.5% of AS-OCT measurements, (ii) retinal OCT results suggestive of glaucomatous optic neuropathy, diabetic retinopathy, or age-related macular degeneration, and (iii) elevated IOP. MAIN OUTCOME MEASURES Sensitivity and specificity of 5 semiautomated AS-OCT parameters relative to gonioscopically narrow angles, defined as the absence of visible trabecular meshwork for ≥ 180° on nonindentation gonioscopy. RESULTS Of 17 656 people aged ≥ 60 years enumerated from 102 communities, 12 633 (71.6%) presented for AS-OCT testing. Referral was recommended for 697 participants based on AS-OCT criteria and 2419 participants based on other criteria, of which 858 had gonioscopy performed by a glaucoma specialist. Each of the 5 AS-OCT parameters offered good diagnostic information for predicting eyes with gonioscopically narrow angles, with areas under the receiver operating characteristic curve ranging from 0.85 to 0.89. The angle opening distance at 750 μm from the scleral spur (AOD750) provided the most diagnostic information, providing an optimal sensitivity of 87% (95% confidence interval [CI], 75%-96%) and specificity of 77% (71%-83%) at a cutpoint of 367 μm, and a sensitivity of 65% (95% CI, 54%-74%) when specificity was constrained to 90% (cutpoint, 283 μm). CONCLUSIONS On AS-OCT, the AOD750 parameter detected approximately two-thirds of cases of gonioscopically narrow angles when test specificity was set to 90%. Although such a sensitivity may not be sufficient when screening solely for narrow angles, AS-OCT requires little additional effort if posterior segment OCT is already being performed and thus could provide incremental benefit when performing OCT-based screening. FINANCIAL DISCLOSURE(S) The authors have no proprietary or commercial interest in any materials discussed in this article.
Collapse
Affiliation(s)
| | | | | | | | | | | | - Isabel J B Thompson
- Francis I. Proctor Foundation, University of California, San Francisco, California
| | - Valerie M Stevens
- Francis I. Proctor Foundation, University of California, San Francisco, California
| | - Krisianne M Aromin
- Francis I. Proctor Foundation, University of California, San Francisco, California
| | - Julius T Oatts
- Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Yvonne Ou
- Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Thomas M Lietman
- Francis I. Proctor Foundation, University of California, San Francisco, California; Department of Ophthalmology, University of California San Francisco, San Francisco, California; Department of Epidemiology & Biostatistics, University of California San Francisco, San Francisco, California; Institute for Global Health Sciences, University of California San Francisco, San Francisco, California
| | - Kieran S O'Brien
- Francis I. Proctor Foundation, University of California, San Francisco, California; Department of Ophthalmology, University of California San Francisco, San Francisco, California
| | - Jeremy D Keenan
- Francis I. Proctor Foundation, University of California, San Francisco, California; Department of Ophthalmology, University of California San Francisco, San Francisco, California.
| |
Collapse
|
8
|
Shah SN, Zhou S, Sanvicente C, Burkemper B, Apolo G, Li C, Li S, Liu L, Lum F, Moghimi S, Xu B. Prevalence and Risk Factors of Blindness Among Primary Angle Closure Glaucoma Patients in the United States: An IRIS Registry Analysis. Am J Ophthalmol 2024; 259:131-140. [PMID: 37944688 PMCID: PMC10922147 DOI: 10.1016/j.ajo.2023.11.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 11/03/2023] [Accepted: 11/03/2023] [Indexed: 11/12/2023]
Abstract
PURPOSE To assess the prevalence and risk factors of blindness among patients newly diagnosed with primary angle closure glaucoma (PACG) in the United States. DESIGN Retrospective cross-sectional study. METHODS Eligible patients from the American Academy of Ophthalmology (AAO) Intelligent Research in Sight (IRIS) Registry had newly diagnosed PACG, defined as: 1) observable during a 24-month lookback period from index date of PACG diagnosis; 2) no history of eye drops, laser, or cataract surgery unless preceded by a diagnosis of anatomical narrow angle (ANA); and 3) no history of glaucoma surgery. Logistic regression models were developed to identify risk factors for any (one or both eyes) or bilateral (both eyes) blindness (visual acuity ≤20/200) at first diagnosis of PACG. RESULTS Among 43,901 eligible patients, overall prevalence of any and bilateral blindness were 11.5% and 1.8%, respectively. Black and Hispanic patients were at higher risk of any (odds ratios [ORs] 1.42 and 1.21, respectively; P < .001) and bilateral (ORs 2.04 and 1.53, respectively; P < .001) blindness compared with non-Hispanic White patients adjusted for ocular comorbidities. Age <50 or >80 years, male sex, Medicaid or Medicare insurance product, and Southern or Western practice region also conferred a higher risk of blindness (OR > 1.28; P ≤ .01). CONCLUSIONS Blindness affects 1 of 9 patients with newly diagnosed PACG in the IRIS Registry. Black and Hispanic patients and Medicaid and Medicare recipients are at significantly higher risk. These findings highlight the severe ocular morbidity among patients with PACG and the need for improved disease awareness and detection methods.
Collapse
Affiliation(s)
- Sona N Shah
- From the Roski Eye Institute (S.N.S., S.Z., B.B., G.A., B.X.), Department of Ophthalmology, University of Southern California, Los Angeles, California, USA
| | - Sarah Zhou
- From the Roski Eye Institute (S.N.S., S.Z., B.B., G.A., B.X.), Department of Ophthalmology, University of Southern California, Los Angeles, California, USA
| | - Carina Sanvicente
- Harvey & Bernice Jones Eye Institute (C.S.), University of Arkansas for Medical Sciences, Little Rock, Arkansas, USA
| | - Bruce Burkemper
- From the Roski Eye Institute (S.N.S., S.Z., B.B., G.A., B.X.), Department of Ophthalmology, University of Southern California, Los Angeles, California, USA
| | - Galo Apolo
- From the Roski Eye Institute (S.N.S., S.Z., B.B., G.A., B.X.), Department of Ophthalmology, University of Southern California, Los Angeles, California, USA
| | - Charles Li
- American Academy of Ophthalmology (C.L., S.L., L.L., F.L.), San Francisco, California, USA
| | - Siying Li
- American Academy of Ophthalmology (C.L., S.L., L.L., F.L.), San Francisco, California, USA
| | - Lynn Liu
- American Academy of Ophthalmology (C.L., S.L., L.L., F.L.), San Francisco, California, USA
| | - Flora Lum
- American Academy of Ophthalmology (C.L., S.L., L.L., F.L.), San Francisco, California, USA
| | - Sasan Moghimi
- Hamilton Glaucoma Center (S.M.), Shiley Eye Institute, Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA
| | - Benjamin Xu
- From the Roski Eye Institute (S.N.S., S.Z., B.B., G.A., B.X.), Department of Ophthalmology, University of Southern California, Los Angeles, California, USA.
| |
Collapse
|
9
|
Shan J, Li Z, Ma P, Tun TA, Yonamine S, Wu Y, Baskaran M, Nongpiur ME, Chen D, Aung T, Li S, He M, Yang Y, Han Y. Deep Learning Classification of Angle Closure based on Anterior Segment OCT. Ophthalmol Glaucoma 2024; 7:8-15. [PMID: 37437884 DOI: 10.1016/j.ogla.2023.06.011] [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: 12/15/2022] [Revised: 06/28/2023] [Accepted: 06/29/2023] [Indexed: 07/14/2023]
Abstract
PURPOSE To assess the performance and generalizability of a convolutional neural network (CNN) model for objective and high-throughput identification of primary angle-closure disease (PACD) as well as PACD stage differentiation on anterior segment swept-source OCT (AS-OCT). DESIGN Cross-sectional. PARTICIPANTS Patients from 3 different eye centers across China and Singapore were recruited for this study. Eight hundred forty-one eyes from the 2 Chinese centers were divided into 170 control eyes, 488 PACS, and 183 PAC + PACG eyes. An additional 300 eyes were recruited from Singapore National Eye Center as a testing data set, divided into 100 control eyes, 100 PACS, and 100 PAC + PACG eyes. METHODS Each participant underwent standardized ophthalmic examination and was classified by the presiding physician as either control, primary angle-closure suspect (PACS), primary angle closure (PAC), or primary angle-closure glaucoma (PACG). Deep Learning model was used to train 3 different CNN classifiers: classifier 1 aimed to separate control versus PACS versus PAC + PACG; classifier 2 aimed to separate control versus PACD; and classifier 3 aimed to separate PACS versus PAC + PACG. All classifiers were evaluated on independent validation sets from the same region, China and further tested using data from a different country, Singapore. MAIN OUTCOME MEASURES Area under receiver operator characteristic curve (AUC), precision, and recall. RESULTS Classifier 1 achieved an AUC of 0.96 on validation set from the same region, but dropped to an AUC of 0.84 on test set from a different country. Classifier 2 achieved the most generalizable performance with an AUC of 0.96 on validation set and AUC of 0.95 on test set. Classifier 3 showed the poorest performance, with an AUC of 0.83 and 0.64 on test and validation data sets, respectively. CONCLUSIONS Convolutional neural network classifiers can effectively distinguish PACD from controls on AS-OCT with good generalizability across different patient cohorts. However, their performance is moderate when trying to distinguish PACS versus PAC + PACG. FINANCIAL DISCLOSURES The authors have no proprietary or commercial interest in any materials discussed in this article.
Collapse
Affiliation(s)
- Jing Shan
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California
| | - Zhixi Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Ping Ma
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California; Department of Ophthalmology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Tin A Tun
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Sean Yonamine
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California
| | - Yangyan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Mani Baskaran
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Monisha E Nongpiur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Dake Chen
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore; Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
| | - Shuning Li
- Beijing Tongren Eye Center, Beijing Tongren Hospital, Capital Medical University, Beijing Ophthalmology and Visual Sciences Key Laboratory, Beijing, China.
| | - Mingguang He
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China; Centre for Eye Research Australia, University of Melbourne, Royal Victorian Eye and Ear Hospital, East Melbourne, VIC, Australia.
| | - Yangfan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China.
| | - Ying Han
- Department of Ophthalmology, University of California, San Francisco, San Francisco, California; Ophthalmology Section, Surgical Service, San Francisco Veterans Affairs Medical Center, San Francisco, California.
| |
Collapse
|
10
|
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.
Collapse
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.
| |
Collapse
|
11
|
Soh ZD, Tan M, Nongpiur ME, Yu M, Qian C, Tham YC, Koh V, Aung T, Xu X, Liu Y, Cheng CY. Deep Learning-based Quantification of Anterior Segment OCT Parameters. OPHTHALMOLOGY SCIENCE 2024; 4:100360. [PMID: 37869016 PMCID: PMC10587633 DOI: 10.1016/j.xops.2023.100360] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 06/22/2023] [Accepted: 06/26/2023] [Indexed: 10/24/2023]
Abstract
Objective To develop and validate a deep learning algorithm that could automate the annotation of scleral spur (SS) and segmentation of anterior chamber (AC) structures for measurements of AC, iris, and angle width parameters in anterior segment OCT (ASOCT) scans. Design Cross-sectional study. Subjects Data from 2 population-based studies (i.e., the Singapore Chinese Eye Study and Singapore Malay Eye Study) and 1 clinical study on angle-closure disease were included in algorithm development. A separate clinical study on angle-closure disease was used for external validation. Method Image contrast of ASOCT scans were first enhanced with CycleGAN. We utilized a heat map regression approach with coarse-to-fine framework for SS annotation. Then, an ensemble network of U-Net, full resolution residual network, and full resolution U-Net was used for structure segmentation. Measurements obtained from predicted SSs and structure segmentation were measured and compared with measurements obtained from manual SS annotation and structure segmentation (i.e., ground truth). Main Outcome Measures We measured Euclidean distance and intraclass correlation coefficients (ICC) to evaluate SS annotation and Dice similarity coefficient for structure segmentation. The ICC, Bland-Altman plot, and repeatability coefficient were used to evaluate agreement and precision of measurements. Results For SS annotation, our algorithm achieved a Euclidean distance of 124.7 μm, ICC ≥ 0.95, and a 3.3% error rate. For structure segmentation, we obtained Dice similarity coefficient ≥ 0.91 for cornea, iris, and AC segmentation. For angle width measurements, ≥ 95% of data points were within the 95% limits-of-agreement in Bland-Altman plot with insignificant systematic bias (all P > 0.12). The ICC ranged from 0.71-0.87 for angle width measurements, 0.54 for IT750, 0.83-0.85 for other iris measurements, and 0.89-0.99 for AC measurements. Using the same SS coordinates from a human expert, measurements obtained from our algorithm were generally less variable than measurements obtained from a semiautomated angle assessment program. Conclusion We developed a deep learning algorithm that could automate SS annotation and structure segmentation in ASOCT scans like human experts, in both open-angle and angle-closure eyes. This algorithm reduces the time needed and subjectivity in obtaining ASOCT measurements. Financial Disclosures The author(s) have no proprietary or commercial interest in any materials discussed in this article.
Collapse
Affiliation(s)
- Zhi Da Soh
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Mingrui Tan
- Institute of High Performance Computing, Agency for Science, Technology and Research, Singapore
| | - Monisha Esther Nongpiur
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Marco Yu
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Chaoxu Qian
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Yih Chung Tham
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Victor Koh
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Tin Aung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - 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
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
- Ophthalmology & Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
- Centre for Innovation and Precision Eye Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| |
Collapse
|
12
|
Jiang W, Yan Y, Cheng S, Wan S, Huang L, Zheng H, Tian M, Zhu J, Pan Y, Li J, Huang L, Wu L, Gao Y, Mao J, Cong Y, Wang Y, Deng Q, Shi X, Yang Z, Liu S, Zheng B, Yang Y. Deep Learning-Based Model for Automatic Assessment of Anterior Angle Chamber in Ultrasound Biomicroscopy. ULTRASOUND IN MEDICINE & BIOLOGY 2023; 49:2497-2509. [PMID: 37730479 DOI: 10.1016/j.ultrasmedbio.2023.08.013] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/26/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 09/22/2023]
Abstract
OBJECTIVE The goal of the work described here was to develop and assess a deep learning-based model that could automatically segment anterior chamber angle (ACA) tissues; classify iris curvature (I-Curv), iris root insertion (IRI), and angle closure (AC); automatically locate scleral spur; and measure ACA parameters in ultrasound biomicroscopy (UBM) images. METHODS A total of 11,006 UBM images were obtained from 1538 patients with primary angle-closure glaucoma who were admitted to the Eye Center of Renmin Hospital of Wuhan University (Wuhan, China) to develop an imaging database. The UNet++ network was used to segment ACA tissues automatically. In addition, two support vector machine (SVM) algorithms were developed to classify I-Curv and AC, and a logistic regression (LR) algorithm was developed to classify IRI. Meanwhile, an algorithm was developed to automatically locate the scleral spur and measure ACA parameters. An external data set of 1,658 images from Huangshi Aier Eye Hospital was used to evaluate the performance of the model under different conditions. An additional 439 images were collected to compare the performance of the model with experts. RESULTS The model achieved accuracies of 95.2%, 88.9% and 85.6% in classification of AC, I-Curv and IRI, respectively. Compared with ophthalmologists, the model achieved an accuracy of 0.765 in classifying AC, I-Curv and IRI, indicating that its high accuracy was as high as that of the ophthalmologists (p > 0.05). The average relative errors (AREs) of ACA parameters were smaller than 15% in the internal data sets. Intraclass correlation coefficients (ICCs) of all the angle-related parameters were greater than 0.911. ICC values of all iris thickness parameters were greater than 0.884. The accurate measurement of ACA parameters partly depended on accurate localization of the scleral spur (p < 0.001). CONCLUSION The model could effectively and accurately evaluate the ACA automatically based on fully automated analysis of UBM images, and it can potentially be a promising tool to assist ophthalmologists. The present study suggested that the deep learning model can be extensively applied to the evaluation of ACA and AC-related biometric risk factors, and it may broaden the application of UBM imaging in the clinical research of primary angle-closure glaucoma.
Collapse
Affiliation(s)
- Weiyan Jiang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yulin Yan
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Simin Cheng
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Shanshan Wan
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Linying Huang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Hongmei Zheng
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Miao Tian
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Jian Zhu
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yumiao Pan
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Jia Li
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Li Huang
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Lianlian Wu
- Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yuelan Gao
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Jiewen Mao
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yuyu Cong
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Yujin Wang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Qian Deng
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Xiaoshuo Shi
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Zixian Yang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Siqi Liu
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China
| | - Biqing Zheng
- School of Resources and Environmental Sciences of Wuhan University, Wuhan, Hubei Province, China
| | - Yanning Yang
- Eye Center, Renmin Hospital of Wuhan University, Wuhan, Hubei Province, China.
| |
Collapse
|
13
|
Gao Z, Pan X, Shao J, Jiang X, Su Z, Jin K, Ye J. Automatic interpretation and clinical evaluation for fundus fluorescein angiography images of diabetic retinopathy patients by deep learning. Br J Ophthalmol 2023; 107:1852-1858. [PMID: 36171054 DOI: 10.1136/bjo-2022-321472] [Citation(s) in RCA: 33] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 09/04/2022] [Indexed: 11/03/2022]
Abstract
BACKGROUND/AIMS Fundus fluorescein angiography (FFA) is an important technique to evaluate diabetic retinopathy (DR) and other retinal diseases. The interpretation of FFA images is complex and time-consuming, and the ability of diagnosis is uneven among different ophthalmologists. The aim of the study is to develop a clinically usable multilevel classification deep learning model for FFA images, including prediagnosis assessment and lesion classification. METHODS A total of 15 599 FFA images of 1558 eyes from 845 patients diagnosed with DR were collected and annotated. Three convolutional neural network (CNN) models were trained to generate the label of image quality, location, laterality of eye, phase and five lesions. Performance of the models was evaluated by accuracy, F-1 score, the area under the curve and human-machine comparison. The images with false positive and false negative results were analysed in detail. RESULTS Compared with LeNet-5 and VGG16, ResNet18 got the best result, achieving an accuracy of 80.79%-93.34% for prediagnosis assessment and an accuracy of 63.67%-88.88% for lesion detection. The human-machine comparison showed that the CNN had similar accuracy with junior ophthalmologists. The false positive and false negative analysis indicated a direction of improvement. CONCLUSION This is the first study to do automated standardised labelling on FFA images. Our model is able to be applied in clinical practice, and will make great contributions to the development of intelligent diagnosis of FFA images.
Collapse
Affiliation(s)
- Zhiyuan Gao
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Xiangji Pan
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Ji Shao
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Xiaoyu Jiang
- College of Control Science and Engineering, Zhejiang University, Hangzhou, Zhejiang, China
| | - Zhaoan Su
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Kai Jin
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| | - Juan Ye
- Department of Ophthalmology, Zhejiang University School of Medicine Second Affiliated Hospital, Hangzhou, Zhejiang, China
| |
Collapse
|
14
|
Keller MJ, Gast TJ, King BJ. Advancements in high-resolution imaging of the iridocorneal angle. FRONTIERS IN OPHTHALMOLOGY 2023; 3:1229670. [PMID: 38983074 PMCID: PMC11182319 DOI: 10.3389/fopht.2023.1229670] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 07/31/2023] [Indexed: 07/11/2024]
Abstract
High-resolution imaging methods of the iridocorneal angle (ICA) will lead to enhanced understanding of aqueous humor outflow mechanisms and a characterization of the trabecular meshwork (TM) morphology at the cellular level will help to better understand glaucoma mechanics (e.g., cellular level biomechanics of the particulate glaucomas). This information will translate into immense clinical value, leading to more informed and customized treatment selection, and improved monitoring of procedural interventions that lower intraocular pressure (IOP). Given ICA anatomy, imaging modalities that yield intrinsic optical sectioning or 3D imaging capability will be useful to aid in the visualization of TM layers. This minireview examines advancements in imaging the ICA in high-resolution.
Collapse
Affiliation(s)
- Matthew J Keller
- School of Optometry, Indiana University, Bloomington, IN, United States
| | - Thomas J Gast
- School of Optometry, Indiana University, Bloomington, IN, United States
| | - Brett J King
- School of Optometry, Indiana University, Bloomington, IN, United States
| |
Collapse
|
15
|
MVGL-Net: A generalizable multi-view convolutional network for anterior segment OCT. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2023.104778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2023]
|
16
|
Eslami Y, Mousavi Kouzahkanan Z, Farzinvash Z, Safizadeh M, Zarei R, Fakhraie G, Vahedian Z, Mahmoudi T, Fadakar K, Beikmarzehei A, Tabatabaei SM. Deep Learning-Based Classification of Subtypes of Primary Angle-Closure Disease With Anterior Segment Optical Coherence Tomography. J Glaucoma 2023; 32:540-547. [PMID: 36897658 DOI: 10.1097/ijg.0000000000002194] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2022] [Accepted: 02/08/2023] [Indexed: 03/11/2023]
Abstract
PRCIS 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.
Collapse
Affiliation(s)
- Yadollah Eslami
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Zahra Farzinvash
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Mona Safizadeh
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Reza Zarei
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Ghasem Fakhraie
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Zakieh Vahedian
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | - Tahereh Mahmoudi
- Department of Medical Physics and Biomedical Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran
| | - Kaveh Fadakar
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| | | | - Seyed Mehdi Tabatabaei
- Glaucoma Service, Farabi Eye Hospital, Tehran University of Medical Sciences, Tehran, Iran
| |
Collapse
|
17
|
Zhang L, Tang L, Xia M, Cao G. The application of artificial intelligence in glaucoma diagnosis and prediction. Front Cell Dev Biol 2023; 11:1173094. [PMID: 37215077 PMCID: PMC10192631 DOI: 10.3389/fcell.2023.1173094] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2023] [Accepted: 04/24/2023] [Indexed: 05/24/2023] Open
Abstract
Artificial intelligence is a multidisciplinary and collaborative science, the ability of deep learning for image feature extraction and processing gives it a unique advantage in dealing with problems in ophthalmology. The deep learning system can assist ophthalmologists in diagnosing characteristic fundus lesions in glaucoma, such as retinal nerve fiber layer defects, optic nerve head damage, optic disc hemorrhage, etc. Early detection of these lesions can help delay structural damage, protect visual function, and reduce visual field damage. The development of deep learning led to the emergence of deep convolutional neural networks, which are pushing the integration of artificial intelligence with testing devices such as visual field meters, fundus imaging and optical coherence tomography to drive more rapid advances in clinical glaucoma diagnosis and prediction techniques. This article details advances in artificial intelligence combined with visual field, fundus photography, and optical coherence tomography in the field of glaucoma diagnosis and prediction, some of which are familiar and some not widely known. Then it further explores the challenges at this stage and the prospects for future clinical applications. In the future, the deep cooperation between artificial intelligence and medical technology will make the datasets and clinical application rules more standardized, and glaucoma diagnosis and prediction tools will be simplified in a single direction, which will benefit multiple ethnic groups.
Collapse
Affiliation(s)
- Linyu Zhang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Li Tang
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
| | - Min Xia
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Guofan Cao
- The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| |
Collapse
|
18
|
Zhou S, Pardeshi AA, Burkemper B, Apolo G, Cho A, Jiang X, Torres M, McKean-Cowdin R, Varma R, Xu BY. Refractive Error and Anterior Chamber Depth as Risk Factors in Primary Angle Closure Disease: The Chinese American Eye Study. J Glaucoma 2023; 32:257-264. [PMID: 36847699 PMCID: PMC10065888 DOI: 10.1097/ijg.0000000000002154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Accepted: 10/06/2022] [Indexed: 03/01/2023]
Abstract
PRCIS The risk of primary angle closure disease (PACD) rises rapidly with greater hyperopia while remaining relatively low for all degrees of myopia. Refractive error (RE) is useful for angle closure risk stratification in the absence of biometric data. PURPOSE To assess the role of RE and anterior chamber depth (ACD) as risk factors in PACD. METHODS Chinese American Eye Study participants received complete eye examinations including refraction, gonioscopy, amplitude-scan biometry, and anterior segment ocular coherence tomography imaging. PACD included primary angle closure suspect (≥3 quadrants of angle closure on gonioscopy) and primary angle closure/primary angle closure glaucoma (peripheral anterior synechiae or intraocular pressure >21 mm Hg). Logistic regression models were developed to assess associations between PACD and RE and/or ACD adjusted for sex and age. Locally weighted scatterplot smoothing curves were plotted to assess continuous relationships between variables. RESULTS Three thousand nine hundred seventy eyes (3403 open angle and 567 PACD) were included. The risk of PACD increased with greater hyperopia [odds ratio (OR) = 1.41 per diopter (D); P < 0.001] and shallower ACD (OR = 1.75 per 0.1 mm; P < 0.001). Hyperopia (≥ + 0.5 D; OR = 5.03) and emmetropia (-0.5 D to +0.5 D; OR = 2.78) conferred a significantly higher risk of PACD compared with myopia (≤0.5 D). ACD (standardized regression coefficient = -0.54) was a 2.5-fold stronger predictor of PACD risk compared with RE (standardized regression coefficient = 0.22) when both variables were included in one multivariable model. The sensitivity and specificity of a 2.6 mm ACD cutoff for PACD were 77.5% and 83.2% and of a +2.0 D RE cutoff were 22.3% and 89.1%. CONCLUSION The risk of PACD rises rapidly with greater hyperopia while remaining relatively low for all degrees of myopia. Although RE is a weaker predictor of PACD than ACD, it remains a useful metric to identify patients who would benefit from gonioscopy in the absence of biometric data.
Collapse
Affiliation(s)
- Sarah Zhou
- Keck School of Medicine at the University of Southern California
| | | | | | - Galo Apolo
- Department of Ophthalmology, Roski Eye Institute
| | - Austin Cho
- Keck School of Medicine at the University of Southern California
| | - Xuejuan Jiang
- Department of Ophthalmology, Roski Eye Institute
- Department of Population and Public Health Sciences, Keck School of Medicine at the University of Southern California
| | - Mina Torres
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA
| | - Roberta McKean-Cowdin
- Keck School of Medicine at the University of Southern California
- Department of Population and Public Health Sciences, Keck School of Medicine at the University of Southern California
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, CA
| | | |
Collapse
|
19
|
Randhawa J, Chiang M, Porporato N, Pardeshi AA, Dredge J, Apolo Aroca G, Tun TA, Quah JH, Tan M, Higashita R, Aung T, Varma R, Xu BY. Generalisability and performance of an OCT-based deep learning classifier for community-based and hospital-based detection of gonioscopic angle closure. Br J Ophthalmol 2023; 107:511-517. [PMID: 34670749 PMCID: PMC9018872 DOI: 10.1136/bjophthalmol-2021-319470] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Accepted: 10/02/2021] [Indexed: 11/04/2022]
Abstract
PURPOSE To assess the generalisability and performance of a deep learning classifier for automated detection of gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images. METHODS A convolutional neural network (CNN) model developed using data from the Chinese American Eye Study (CHES) was used to detect gonioscopic angle closure in AS-OCT images with reference gonioscopy grades provided by trained ophthalmologists. Independent test data were derived from the population-based CHES, a community-based clinic in Singapore, and a hospital-based clinic at the University of Southern California (USC). Classifier performance was evaluated with receiver operating characteristic curve and area under the receiver operating characteristic curve (AUC) metrics. Interexaminer agreement between the classifier and two human examiners at USC was calculated using Cohen's kappa coefficients. RESULTS The classifier was tested using 640 images (311 open and 329 closed) from 127 Chinese Americans, 10 165 images (9595 open and 570 closed) from 1318 predominantly Chinese Singaporeans and 300 images (234 open and 66 closed) from 40 multiethnic USC patients. The classifier achieved similar performance in the CHES (AUC=0.917), Singapore (AUC=0.894) and USC (AUC=0.922) cohorts. Standardising the distribution of gonioscopy grades across cohorts produced similar AUC metrics (range 0.890-0.932). The agreement between the CNN classifier and two human examiners (Ҡ=0.700 and 0.704) approximated interexaminer agreement (Ҡ=0.693) in the USC cohort. CONCLUSION An OCT-based deep learning classifier demonstrated consistent performance detecting gonioscopic angle closure across three independent patient populations. This automated method could aid ophthalmologists in the assessment of angle status in diverse patient populations.
Collapse
Affiliation(s)
- Jasmeen Randhawa
- Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Michael Chiang
- Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Natalia Porporato
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | - Anmol A Pardeshi
- Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Justin Dredge
- Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Galo Apolo Aroca
- Roski Eye Institute, Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| | - Tin A Tun
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
| | | | - Marcus Tan
- Ophthalmology, National University of Singapore, Singapore
| | | | - Tin Aung
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore
- Ophthalmology, National University of Singapore, Singapore
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, California, USA
| | - Benjamin Y Xu
- Keck School of Medicine of the University of Southern California, Los Angeles, California, USA
| |
Collapse
|
20
|
Xu Z, Xu J, Shi C, Xu W, Jin X, Han W, Jin K, Grzybowski A, Yao K. Artificial Intelligence for Anterior Segment Diseases: A Review of Potential Developments and Clinical Applications. Ophthalmol Ther 2023; 12:1439-1455. [PMID: 36884203 PMCID: PMC10164195 DOI: 10.1007/s40123-023-00690-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 02/13/2023] [Indexed: 03/09/2023] Open
Abstract
Artificial intelligence (AI) technology is promising in the field of healthcare. With the developments of big data and image-based analysis, AI shows potential value in ophthalmology applications. Recently, machine learning and deep learning algorithms have made significant progress. Emerging evidence has demonstrated the capability of AI in the diagnosis and management of anterior segment diseases. In this review, we provide an overview of AI applications and potential future applications in anterior segment diseases, focusing on cornea, refractive surgery, cataract, anterior chamber angle detection, and refractive error prediction.
Collapse
Affiliation(s)
- Zhe Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Jia Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Ce Shi
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Wen Xu
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Xiuming Jin
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Wei Han
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Kai Jin
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China
| | - Andrzej Grzybowski
- Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland.
- Institute for Research in Ophthalmology, Foundation for Ophthalmology Development, Poznan, Poland.
| | - Ke Yao
- Eye Center of the Second Affiliated Hospital, School of Medicine, Zhejiang University, No. 88 Jiefang Road, Hangzhou, 310009, Zhejiang, China.
| |
Collapse
|
21
|
Shen A, Chiang M, Pardeshi AA, McKean-Cowdin R, Varma R, Xu BY. Anterior segment biometric measurements explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure. Br J Ophthalmol 2023; 107:349-354. [PMID: 34615666 PMCID: PMC8983788 DOI: 10.1136/bjophthalmol-2021-319058] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Accepted: 09/18/2021] [Indexed: 11/04/2022]
Abstract
BACKGROUND/AIMS To identify biometric parameters that explain misclassifications by a deep learning classifier for detecting gonioscopic angle closure in anterior segment optical coherence tomography (AS-OCT) images. METHODS Chinese American Eye Study (CHES) participants underwent gonioscopy and AS-OCT of each angle quadrant. A subset of CHES AS-OCT images were analysed using a deep learning classifier to detect positive angle closure based on manual gonioscopy by a reference human examiner. Parameter measurements were compared between four prediction classes: true positives (TPs), true negatives (TNs), false positives (FPs) and false negatives (FN). Logistic regression models were developed to differentiate between true and false predictions. Performance was assessed using area under the receiver operating curve (AUC) and classifier accuracy metrics. RESULTS 584 images from 127 participants were analysed, yielding 271 TPs, 224 TNs, 77 FPs and 12 FNs. Parameter measurements differed (p<0.001) between prediction classes among anterior segment parameters, including iris curvature (IC) and lens vault (LV), and angle parameters, including angle opening distance (AOD). FP resembled TP more than FN and TN in terms of anterior segment parameters (steeper IC and higher LV), but resembled TN more than TP and FN in terms of angle parameters (wider AOD). Models for detecting FP (AUC=0.752) and FN (AUC=0.838) improved classifier accuracy from 84.8% to 89.0%. CONCLUSIONS Misclassifications by an OCT-based deep learning classifier for detecting gonioscopic angle closure are explained by disagreement between anterior segment and angle parameters. This finding could be used to improve classifier performance and highlights differences between gonioscopic and AS-OCT definitions of angle closure.
Collapse
Affiliation(s)
- Alice Shen
- Department of Ophthalmology, USC Keck School of Medicine, Los Angeles, California, USA
| | - Michael Chiang
- Department of Ophthalmology, USC Keck School of Medicine, Los Angeles, California, USA
| | - Anmol A Pardeshi
- Department of Ophthalmology, USC Keck School of Medicine, Los Angeles, California, USA
| | - Roberta McKean-Cowdin
- Department of Preventive Medicine, USC Keck School of Medicine, Los Angeles, California, USA
| | - Rohit Varma
- Southern California Eye Institute, CHA Hollywood Presbyterian Medical Center, Los Angeles, California, USA
| | - Benjamin Y Xu
- Department of Ophthalmology, USC Keck School of Medicine, Los Angeles, California, USA
| |
Collapse
|
22
|
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.
Collapse
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
| |
Collapse
|
23
|
Precision Medicine in Glaucoma: Artificial Intelligence, Biomarkers, Genetics and Redox State. Int J Mol Sci 2023; 24:ijms24032814. [PMID: 36769127 PMCID: PMC9917798 DOI: 10.3390/ijms24032814] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/07/2023] [Accepted: 01/18/2023] [Indexed: 02/05/2023] Open
Abstract
Glaucoma is a multifactorial neurodegenerative illness requiring early diagnosis and strict monitoring of the disease progression. Current exams for diagnosis and prognosis are based on clinical examination, intraocular pressure (IOP) measurements, visual field tests, and optical coherence tomography (OCT). In this scenario, there is a critical unmet demand for glaucoma-related biomarkers to enhance clinical testing for early diagnosis and tracking of the disease's development. The introduction of validated biomarkers would allow for prompt intervention in the clinic to help with prognosis prediction and treatment response monitoring. This review aims to report the latest acquisitions on biomarkers in glaucoma, from imaging analysis to genetics and metabolic markers.
Collapse
|
24
|
Chen D, Ran Ran A, Fang Tan T, Ramachandran R, Li F, Cheung CY, Yousefi S, Tham CCY, Ting DSW, Zhang X, Al-Aswad LA. Applications of Artificial Intelligence and Deep Learning in Glaucoma. Asia Pac J Ophthalmol (Phila) 2023; 12:80-93. [PMID: 36706335 DOI: 10.1097/apo.0000000000000596] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 12/06/2022] [Indexed: 01/28/2023] Open
Abstract
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.
Collapse
Affiliation(s)
- Dinah Chen
- Department of Ophthalmology, NYU Langone Health, New York City, NY
- Genentech Inc, South San Francisco, CA
| | - An Ran Ran
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Ting Fang Tan
- Singapore Eye Research Institute, Singapore
- Singapore National Eye Center, Singapore
| | | | - Fei Li
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | - Carol Y Cheung
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Siamak Yousefi
- Department of Ophthalmology, The University of Tennessee Health Science Center, Memphis, TN
| | - Clement C Y Tham
- Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Hong Kong, China
- Lam Kin Chung, Jet King-Shing Ho Glaucoma Treatment And Research Centre, The Chinese University of Hong Kong, Hong Kong, China
| | - Daniel S W Ting
- Singapore Eye Research Institute, Singapore
- Singapore National Eye Center, Singapore
- Duke-NUS Medical School, National University of Singapore, Singapore
| | - Xiulan Zhang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
- Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangdong Provincial Clinical Research Center for Ocular Diseases, Guangzhou, China
| | | |
Collapse
|
25
|
Garcia-Marin YF, Alonso-Caneiro D, Fisher D, Vincent SJ, Collins MJ. Patch-based CNN for corneal segmentation of AS-OCT images: Effect of the number of classes and image quality upon performance. Comput Biol Med 2023; 152:106342. [PMID: 36481759 DOI: 10.1016/j.compbiomed.2022.106342] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2022] [Revised: 10/24/2022] [Accepted: 11/16/2022] [Indexed: 12/03/2022]
Abstract
Anterior segment optical coherence tomography (AS-OCT) is a fundamental ophthalmic imaging technique. AS-OCT images can be examined by experts and segmented to provide quantitative metrics that inform clinical decision making. Manual segmentation of these images is time-consuming and subjective, encouraging software developers in the field to automate segmentation procedures. Traditional programing segmentation approaches are being replaced by deep learning methods, which have shown state-of-the-art performance in AS-OCT image analysis. In this study, a method based on patch-based convolutional neural networks (CNN) was used to segment the three main boundaries of the cornea: the epithelium, Bowman's layer, and the endothelium. To assess the effect of the number of classes on performance, the model was designed as a patch-based boundary classifier using 4 and 8 classes. The effect of image quality was also assessed using different data distributions during the training process. While the Dice coefficient and probability revealed greater precision for the 8 class models, the boundary error metric indicated comparable performance. Additionally, for 8 class models, the image quality test had only a small negative effect on performance, which may be an indication of the robustness of the model and could also suggest that the data augmentation methods did not show significant improvement. These findings contribute to the development of automatic segmentation techniques for AS-OCT images, since patch-based methods have been largely unexplored in favor of other deep learning techniques. The overall performance of the proposed method is comparable to other well-established segmentation methods.
Collapse
Affiliation(s)
- Yoel F Garcia-Marin
- Queensland University of Technology (QUT), Contact Lens and Visual Optics Laboratory, Centre for Vision and Eye Research, School of Optometry and Vision Science, Kelvin Grove, Qld, 4059, Australia.
| | - David Alonso-Caneiro
- Queensland University of Technology (QUT), Contact Lens and Visual Optics Laboratory, Centre for Vision and Eye Research, School of Optometry and Vision Science, Kelvin Grove, Qld, 4059, Australia
| | - Damien Fisher
- Queensland University of Technology (QUT), Contact Lens and Visual Optics Laboratory, Centre for Vision and Eye Research, School of Optometry and Vision Science, Kelvin Grove, Qld, 4059, Australia
| | - Stephen J Vincent
- Queensland University of Technology (QUT), Contact Lens and Visual Optics Laboratory, Centre for Vision and Eye Research, School of Optometry and Vision Science, Kelvin Grove, Qld, 4059, Australia
| | - Michael J Collins
- Queensland University of Technology (QUT), Contact Lens and Visual Optics Laboratory, Centre for Vision and Eye Research, School of Optometry and Vision Science, Kelvin Grove, Qld, 4059, Australia
| |
Collapse
|
26
|
Anterior Segment Parameter Changes after Cataract Surgery in Open-Angle and Angle-Closure Eyes: A Prospective Study. J Clin Med 2022; 12:jcm12010327. [PMID: 36615126 PMCID: PMC9820897 DOI: 10.3390/jcm12010327] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2022] [Revised: 12/26/2022] [Accepted: 12/28/2022] [Indexed: 01/03/2023] Open
Abstract
Background: To investigate the anterior segment parameters before and after cataract surgery in open-angle eyes and different subtypes of primary angle-closure glaucoma (PACG) eyes and to further explore the potential relationship between the anterior rotation of the ciliary process and crystalline lens. Methods: An observational, prospective study was performed on 66 patients who had cataract surgery including 22 chronic PACG patients, 22 acute PACG patients, and 22 open-angle cataract patients. Anterior segment parameters including the trabecular-ciliary process distance, ciliary process area, trabecular-ciliary angle (TCA), maximum ciliary body thickness (CBTmax), and so on, were measured using ultrasound biomicroscopy preoperatively and 3 months postoperatively. Results: After the surgery, there were significant increases in TCA (p < 0.001) and CBTmax (p < 0.05) in all three groups, while there was no significant change in the trabecular-ciliary process distance (p > 0.05) in all three groups. No significant difference in the changes of ciliary process area, TCA, and CBTmax (p > 0.05) pre- and postoperatively among the three groups were identified. Conclusions: Extractions of crystalline lenses played similar roles in terms of decreasing the anterior rotation of ciliary processes in open-angle eyes and angle-closure eyes. A natural anatomical abnormality may be a more important factor in the anterior rotation of ciliary processes in PACG patients.
Collapse
|
27
|
Straßer T, Wagner S. Performance of the Deep Neural Network Ciloctunet, Integrated with Open-Source Software for Ciliary Muscle Segmentation in Anterior Segment OCT Images, Is on Par with Experienced Examiners. Diagnostics (Basel) 2022; 12:diagnostics12123055. [PMID: 36553062 PMCID: PMC9777151 DOI: 10.3390/diagnostics12123055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2022] [Revised: 11/24/2022] [Accepted: 11/27/2022] [Indexed: 12/12/2022] Open
Abstract
Anterior segment optical coherence tomography (AS-OCT), being non-invasive and well-tolerated, is the method of choice for an in vivo investigation of ciliary muscle morphology and function. The analysis requires the segmentation of the ciliary muscle, which is, when performed manually, both time-consuming and prone to examiner bias. Here, we present a convolutional neural network trained for the automatic segmentation of the ciliary muscle in AS-OCT images. Ciloctunet is based on the Freiburg U-net and was trained and validated using 1244 manually segmented OCT images from two previous studies. An accuracy of 97.5% for the validation dataset was achieved. Ciloctunet's performance was evaluated by replicating the findings of a third study with 180 images as the test data. The replication demonstrated that Ciloctunet performed on par with two experienced examiners. The intersection-over-union index (0.84) of the ciliary muscle thickness profiles between Ciloctunet and an experienced examiner was the same as between the two examiners. The mean absolute error between the ciliary muscle thickness profiles of Ciloctunet and the two examiners (35.16 µm and 45.86 µm) was comparable to the one between the examiners (34.99 µm). A statistically significant effect of the segmentation type on the derived biometric parameters was found for the ciliary muscle area but not for the selective thickness reading ("perpendicular axis"). Both the inter-rater and the intra-rater reliability of Ciloctunet were good to excellent. Ciloctunet avoids time-consuming manual segmentation, thus enabling the analysis of large numbers of images of ample study cohorts while avoiding possible examiner biases. Ciloctunet is available as open-source.
Collapse
Affiliation(s)
- Torsten Straßer
- Institute for Ophthalmic Research, University of Tuebingen, 72076 Tuebingen, Germany
- University Eye Hospital Tuebingen, 72076 Tuebingen, Germany
- Correspondence:
| | - Sandra Wagner
- Institute for Ophthalmic Research, University of Tuebingen, 72076 Tuebingen, Germany
| |
Collapse
|
28
|
Jin K, Ye J. Artificial intelligence and deep learning in ophthalmology: Current status and future perspectives. ADVANCES IN OPHTHALMOLOGY PRACTICE AND RESEARCH 2022; 2:100078. [PMID: 37846285 PMCID: PMC10577833 DOI: 10.1016/j.aopr.2022.100078] [Citation(s) in RCA: 28] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/01/2022] [Accepted: 08/18/2022] [Indexed: 10/18/2023]
Abstract
Background The ophthalmology field was among the first to adopt artificial intelligence (AI) in medicine. The availability of digitized ocular images and substantial data have made deep learning (DL) a popular topic. Main text At the moment, AI in ophthalmology is mostly used to improve disease diagnosis and assist decision-making aiming at ophthalmic diseases like diabetic retinopathy (DR), glaucoma, age-related macular degeneration (AMD), cataract and other anterior segment diseases. However, most of the AI systems developed to date are still in the experimental stages, with only a few having achieved clinical applications. There are a number of reasons for this phenomenon, including security, privacy, poor pervasiveness, trust and explainability concerns. Conclusions This review summarizes AI applications in ophthalmology, highlighting significant clinical considerations for adopting AI techniques and discussing the potential challenges and future directions.
Collapse
Affiliation(s)
- Kai Jin
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| | - Juan Ye
- Department of Ophthalmology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, China
| |
Collapse
|
29
|
Thompson AC, Falconi A, Sappington RM. Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging. FRONTIERS IN OPHTHALMOLOGY 2022; 2:937205. [PMID: 38983522 PMCID: PMC11182271 DOI: 10.3389/fopht.2022.937205] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/22/2022] [Indexed: 07/11/2024]
Abstract
Glaucoma is a leading cause of progressive blindness and visual impairment worldwide. Microstructural evidence of glaucomatous damage to the optic nerve head and associated tissues can be visualized using optical coherence tomography (OCT). In recent years, development of novel deep learning (DL) algorithms has led to innovative advances and improvements in automated detection of glaucomatous damage and progression on OCT imaging. DL algorithms have also been trained utilizing OCT data to improve detection of glaucomatous damage on fundus photography, thus improving the potential utility of color photos which can be more easily collected in a wider range of clinical and screening settings. This review highlights ten years of contributions to glaucoma detection through advances in deep learning models trained utilizing OCT structural data and posits future directions for translation of these discoveries into the field of aging and the basic sciences.
Collapse
Affiliation(s)
- Atalie C. Thompson
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston Salem, NC, United States
- Department of Internal Medicine, Gerontology, and Geriatric Medicine, Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Aurelio Falconi
- Wake Forest School of Medicine, Winston Salem, NC, United States
| | - Rebecca M. Sappington
- Department of Surgical Ophthalmology, Wake Forest School of Medicine, Winston Salem, NC, United States
- Department of Neurobiology and Anatomy, Wake Forest School of Medicine, Winston Salem, NC, United States
| |
Collapse
|
30
|
Al-Aswad LA, Ramachandran R, Schuman JS, Medeiros F, Eydelman MB. Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression. Ophthalmol Glaucoma 2022; 5:e16-e25. [PMID: 35218987 PMCID: PMC9399304 DOI: 10.1016/j.ogla.2022.02.010] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2022] [Revised: 02/14/2022] [Accepted: 02/17/2022] [Indexed: 12/15/2022]
Abstract
On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmic conditions. In a session entitled "Artificial Intelligence for Glaucoma," a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities, including fundus photography, OCT imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations of and challenges to its clinical application. The panelists' discussion addressed AI and health equities from clinical, societal, and regulatory perspectives.
Collapse
Affiliation(s)
- Lama A Al-Aswad
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Population Health, NYU Langone Health, NYU Grossman School of Medicine, New York, New York.
| | - Rithambara Ramachandran
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York
| | - Joel S Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York; Department of Biomedical Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Department of Electrical and Computer Engineering, New York University Tandon School of Engineering, Brooklyn, New York; Center for Neural Science, NYU, New York, New York; Neuroscience Institute, NYU Langone Health, New York, New York
| | - Felipe Medeiros
- Department of Ophthalmology, Duke University School of Medicine, Durham, North Carolina; Department of Electrical and Computer Engineering, Pratt School of Engineering, Duke University, Durham, North Carolina
| | | |
Collapse
|
31
|
Garcia Marin YF, Alonso-Caneiro D, Vincent SJ, Collins MJ. Anterior segment optical coherence tomography (AS-OCT) image analysis methods and applications: A systematic review. Comput Biol Med 2022; 146:105471. [DOI: 10.1016/j.compbiomed.2022.105471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 03/27/2022] [Accepted: 03/28/2022] [Indexed: 11/03/2022]
|
32
|
Yoo TK, Ryu IH, Kim JK, Lee IS, Kim HK. A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 219:106735. [PMID: 35305492 DOI: 10.1016/j.cmpb.2022.106735] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Revised: 02/15/2022] [Accepted: 03/04/2022] [Indexed: 06/14/2023]
Abstract
BACKGROUND AND OBJECTIVES Patients with angle-closure glaucoma (ACG) are asymptomatic until they experience a painful attack. Shallow anterior chamber depth (ACD) is considered a significant risk factor for ACG. We propose a deep learning approach to detect shallow ACD using fundus photographs and to identify the hidden features of shallow ACD. METHODS This retrospective study assigned healthy subjects to the training (n = 1188 eyes) and test (n = 594) datasets (prospective validation design). We used a deep learning approach to estimate ACD and build a classification model to identify eyes with a shallow ACD. The proposed method, including subtraction of the input and output images of CycleGAN and a thresholding algorithm, was adopted to visualize the characteristic features of fundus photographs with a shallow ACD. RESULTS The deep learning model integrating fundus photographs and clinical variables achieved areas under the receiver operating characteristic curve of 0.978 (95% confidence interval [CI], 0.963-0.988) for an ACD ≤ 2.60 mm and 0.895 (95% CI, 0.868-0.919) for an ACD ≤ 2.80 mm, and outperformed the regression model using only clinical variables. However, the difference between shallow and deep ACD classes on fundus photographs was difficult to be detected with the naked eye. We were unable to identify the features of shallow ACD using the Grad-CAM. The CycleGAN-based feature images showed that area around the macula and optic disk significantly contributed to the classification of fundus photographs with a shallow ACD. CONCLUSIONS We demonstrated the feasibility of a novel deep learning model to detect a shallow ACD as a screening tool for ACG using fundus photographs. The CycleGAN-based feature map showed the hidden characteristic features of shallow ACD that were previously undetectable by conventional techniques and ophthalmologists. This framework will facilitate the early detection of shallow ACD to prevent overlooking the risks associated with ACG.
Collapse
Affiliation(s)
- Tae Keun Yoo
- B&VIIT Eye Center, Seoul, South Korea; Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, Cheongju, South Korea.
| | - Ik Hee Ryu
- B&VIIT Eye Center, Seoul, South Korea; VISUWORKS, Seoul, South Korea
| | - Jin Kuk Kim
- B&VIIT Eye Center, Seoul, South Korea; VISUWORKS, Seoul, South Korea
| | | | - Hong Kyu Kim
- Department of Ophthalmology, Dankook University Hospital, Dankook University College of Medicine, Cheonan, South Korea
| |
Collapse
|
33
|
Kim HE, Cosa-Linan A, Santhanam N, Jannesari M, Maros ME, Ganslandt T. Transfer learning for medical image classification: a literature review. BMC Med Imaging 2022; 22:69. [PMID: 35418051 PMCID: PMC9007400 DOI: 10.1186/s12880-022-00793-7] [Citation(s) in RCA: 113] [Impact Index Per Article: 56.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 03/30/2022] [Indexed: 02/07/2023] Open
Abstract
BACKGROUND Transfer learning (TL) with convolutional neural networks aims to improve performances on a new task by leveraging the knowledge of similar tasks learned in advance. It has made a major contribution to medical image analysis as it overcomes the data scarcity problem as well as it saves time and hardware resources. However, transfer learning has been arbitrarily configured in the majority of studies. This review paper attempts to provide guidance for selecting a model and TL approaches for the medical image classification task. METHODS 425 peer-reviewed articles were retrieved from two databases, PubMed and Web of Science, published in English, up until December 31, 2020. Articles were assessed by two independent reviewers, with the aid of a third reviewer in the case of discrepancies. We followed the PRISMA guidelines for the paper selection and 121 studies were regarded as eligible for the scope of this review. We investigated articles focused on selecting backbone models and TL approaches including feature extractor, feature extractor hybrid, fine-tuning and fine-tuning from scratch. RESULTS The majority of studies (n = 57) empirically evaluated multiple models followed by deep models (n = 33) and shallow (n = 24) models. Inception, one of the deep models, was the most employed in literature (n = 26). With respect to the TL, the majority of studies (n = 46) empirically benchmarked multiple approaches to identify the optimal configuration. The rest of the studies applied only a single approach for which feature extractor (n = 38) and fine-tuning from scratch (n = 27) were the two most favored approaches. Only a few studies applied feature extractor hybrid (n = 7) and fine-tuning (n = 3) with pretrained models. CONCLUSION The investigated studies demonstrated the efficacy of transfer learning despite the data scarcity. We encourage data scientists and practitioners to use deep models (e.g. ResNet or Inception) as feature extractors, which can save computational costs and time without degrading the predictive power.
Collapse
Affiliation(s)
- Hee E Kim
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany.
| | - Alejandro Cosa-Linan
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Nandhini Santhanam
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Mahboubeh Jannesari
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Mate E Maros
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
| | - Thomas Ganslandt
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68167, Mannheim, Germany
- Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 15, 91058, Erlangen, Germany
| |
Collapse
|
34
|
Singh LK, Garg H, Khanna M. Performance evaluation of various deep learning based models for effective glaucoma evaluation using optical coherence tomography images. MULTIMEDIA TOOLS AND APPLICATIONS 2022; 81:27737-27781. [PMID: 35368855 PMCID: PMC8962290 DOI: 10.1007/s11042-022-12826-y] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 02/20/2022] [Accepted: 03/09/2022] [Indexed: 06/14/2023]
Abstract
Glaucoma is the dominant reason for irreversible blindness worldwide, and its best remedy is early and timely detection. Optical coherence tomography has come to be the most commonly used imaging modality in detecting glaucomatous damage in recent years. Deep Learning using Optical Coherence Tomography Modality helps in predicting glaucoma more accurately and less tediously. This experimental study aims to perform glaucoma prediction using eight different ImageNet models from Optical Coherence Tomography of Glaucoma. A thorough investigation is performed to evaluate these models' performances on various efficiency metrics, which will help discover the best performing model. Every net is tested on three different optimizers, namely Adam, Root Mean Squared Propagation, and Stochastic Gradient Descent, to find the best relevant results. An attempt has been made to improvise the performance of models using transfer learning and fine-tuning. The work presented in this study was initially trained and tested on a private database that consists of 4220 images (2110 normal optical coherence tomography and 2110 glaucoma optical coherence tomography). Based on the results, the four best-performing models are shortlisted. Later, these models are tested on the well-recognized standard public Mendeley dataset. Experimental results illustrate that VGG16 using the Root Mean Squared Propagation Optimizer attains auspicious performance with 95.68% accuracy. The proposed work concludes that different ImageNet models are a good alternative as a computer-based automatic glaucoma screening system. This fully automated system has a lot of potential to tell the difference between normal Optical Coherence Tomography and glaucomatous Optical Coherence Tomography automatically. The proposed system helps in efficiently detecting this retinal infection in suspected patients for better diagnosis to avoid vision loss and also decreases senior ophthalmologists' (experts) precious time and involvement.
Collapse
Affiliation(s)
- Law Kumar Singh
- Department of Computer Science and Engineering, Sharda University , Greater Noida, India
- Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, India
| | - Hitendra Garg
- Department of Computer Engineering and Applications, GLA University, Mathura, India
| | - Munish Khanna
- Department of Computer Science and Engineering, Hindustan College of Science and Technology, Mathura, India
| |
Collapse
|
35
|
Yang Y, Wu Y, Guo C, Han Y, Deng M, Lin H, Yu M. Diagnostic Performance of Deep Learning Classifiers in Measuring Peripheral Anterior Synechia Based on Swept Source Optical Coherence Tomography Images. Front Med (Lausanne) 2022; 8:775711. [PMID: 35155465 PMCID: PMC8825342 DOI: 10.3389/fmed.2021.775711] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2021] [Accepted: 12/27/2021] [Indexed: 01/16/2023] Open
Abstract
Purpose To develop deep learning classifiers and evaluate their diagnostic performance in detecting the static gonioscopic angle closure and peripheral anterior synechia (PAS) based on swept source optical coherence tomography (SS-OCT) images. Materials and Methods Subjects were recruited from the Glaucoma Service at Zhongshan Ophthalmic Center of Sun Yat-sun University, Guangzhou, China. Each subject underwent a complete ocular examination, such as gonioscopy and SS-OCT imaging. Two deep learning classifiers, using convolutional neural networks (CNNs), were developed to diagnose the static gonioscopic angle closure and to differentiate appositional from synechial angle closure based on SS-OCT images. Area under the receiver operating characteristic (ROC) curve (AUC) was used as outcome measure to evaluate the diagnostic performance of two deep learning systems. Results A total of 439 eyes of 278 Chinese patients, which contained 175 eyes of positive PAS, were recruited to develop diagnostic models. For the diagnosis of static gonioscopic angle closure, the first deep learning classifier achieved an AUC of 0.963 (95% CI, 0.954–0.972) with a sensitivity of 0.929 and a specificity of 0.877. The AUC of the second deep learning classifier distinguishing appositional from synechial angle closure was 0.873 (95% CI, 0.864–0.882) with a sensitivity of 0.846 and a specificity of 0.764. Conclusion Deep learning systems based on SS-OCT images showed good diagnostic performance for gonioscopic angle closure and moderate performance in the detection of PAS.
Collapse
Affiliation(s)
- Yangfan Yang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Yanyan Wu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Chong Guo
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Ying Han
- Department of Ophthalmology, University of California, San Francisco, San Francisco, CA, United States
| | - Mingjie Deng
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Haotian Lin
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| | - Minbin Yu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, China
| |
Collapse
|
36
|
Hao J, Li F, Hao H, Fu H, Xu Y, Higashita R, Zhang X, Liu J, Zhao Y. Hybrid Variation-Aware Network for Angle-Closure Assessment in AS-OCT. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:254-265. [PMID: 34487491 DOI: 10.1109/tmi.2021.3110602] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Automatic angle-closure assessment in Anterior Segment OCT (AS-OCT) images is an important task for the screening and diagnosis of glaucoma, and the most recent computer-aided models focus on a binary classification of anterior chamber angles (ACA) in AS-OCT, i.e., open-angle and angle-closure. In order to assist clinicians who seek better to understand the development of the spectrum of glaucoma types, a more discriminating three-class classification scheme was suggested, i.e., the classification of ACA was expended to include open-, appositional- and synechial angles. However, appositional and synechial angles display similar appearances in an AS-OCT image, which makes classification models struggle to differentiate angle-closure subtypes based on static AS-OCT images. In order to tackle this issue, we propose a 2D-3D Hybrid Variation-aware Network (HV-Net) for open-appositional-synechial ACA classification from AS-OCT imagery. Specifically, taking into account clinical priors, we first reconstruct the 3D iris surface from an AS-OCT sequence, and obtain the geometrical characteristics necessary to provide global shape information. 2D AS-OCT slices and 3D iris representations are then fed into our HV-Net to extract cross-sectional appearance features and iris morphological features, respectively. To achieve similar results to those of dynamic gonioscopy examination, which is the current gold standard for diagnostic angle assessment, the paired AS-OCT images acquired in dark and light illumination conditions are used to obtain an accurate characterization of configurational changes in ACAs and iris shapes, using a Variation-aware Block. In addition, an annealing loss function was introduced to optimize our model, so as to encourage the sub-networks to map the inputs into the more conducive spaces to extract dark-to-light variation representations, while retaining the discriminative power of the learned features. The proposed model is evaluated across 1584 paired AS-OCT samples, and it has demonstrated its superiority in classifying open-, appositional- and synechial angles.
Collapse
|
37
|
Bunod R, Augstburger E, Brasnu E, Labbe A, Baudouin C. [Artificial intelligence and glaucoma: A literature review]. J Fr Ophtalmol 2022; 45:216-232. [PMID: 34991909 DOI: 10.1016/j.jfo.2021.11.002] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2021] [Accepted: 11/18/2021] [Indexed: 11/26/2022]
Abstract
In recent years, research in artificial intelligence (AI) has experienced an unprecedented surge in the field of ophthalmology, in particular glaucoma. The diagnosis and follow-up of glaucoma is complex and relies on a body of clinical evidence and ancillary tests. This large amount of information from structural and functional testing of the optic nerve and macula makes glaucoma a particularly appropriate field for the application of AI. In this paper, we will review work using AI in the field of glaucoma, whether for screening, diagnosis or detection of progression. Many AI strategies have shown promising results for glaucoma detection using fundus photography, optical coherence tomography, or automated perimetry. The combination of these imaging modalities increases the performance of AI algorithms, with results comparable to those of humans. We will discuss potential applications as well as obstacles and limitations to the deployment and validation of such models. While there is no doubt that AI has the potential to revolutionize glaucoma management and screening, research in the coming years will need to address unavoidable questions regarding the clinical significance of such results and the explicability of the predictions.
Collapse
Affiliation(s)
- R Bunod
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France.
| | - E Augstburger
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France
| | - E Brasnu
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France
| | - A Labbe
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
| | - C Baudouin
- Service d'ophtalmologie 3, IHU FOReSIGHT, centre hospitalier national des Quinze-Vingts, 28, rue de Charenton, 75012 Paris, France; CHNO des Quinze-Vingts, IHU FOReSIGHT, INSERM-DGOS CIC 1423, 17, rue Moreau, 75012 Paris, France; Sorbonne universités, INSERM, CNRS, institut de la Vision, 17, rue Moreau, 75012 Paris, France; Service d'ophtalmologie, hôpital Ambroise-Paré, AP-HP, université de Paris Saclay, 9, avenue Charles-de-Gaulle, 92100 Boulogne-Billancourt, France
| |
Collapse
|
38
|
Wang Z, Keane PA, Chiang M, Cheung CY, Wong TY, Ting DSW. Artificial Intelligence and Deep Learning in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
39
|
Shigueoka LS, Jammal AA, Medeiros FA, Costa VP. Artificial Intelligence in Ophthalmology. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_201] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
40
|
Updates in deep learning research in ophthalmology. Clin Sci (Lond) 2021; 135:2357-2376. [PMID: 34661658 DOI: 10.1042/cs20210207] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2021] [Revised: 09/14/2021] [Accepted: 09/29/2021] [Indexed: 12/13/2022]
Abstract
Ophthalmology has been one of the early adopters of artificial intelligence (AI) within the medical field. Deep learning (DL), in particular, has garnered significant attention due to the availability of large amounts of data and digitized ocular images. Currently, AI in Ophthalmology is mainly focused on improving disease classification and supporting decision-making when treating ophthalmic diseases such as diabetic retinopathy, age-related macular degeneration (AMD), glaucoma and retinopathy of prematurity (ROP). However, most of the DL systems (DLSs) developed thus far remain in the research stage and only a handful are able to achieve clinical translation. This phenomenon is due to a combination of factors including concerns over security and privacy, poor generalizability, trust and explainability issues, unfavorable end-user perceptions and uncertain economic value. Overcoming this challenge would require a combination approach. Firstly, emerging techniques such as federated learning (FL), generative adversarial networks (GANs), autonomous AI and blockchain will be playing an increasingly critical role to enhance privacy, collaboration and DLS performance. Next, compliance to reporting and regulatory guidelines, such as CONSORT-AI and STARD-AI, will be required to in order to improve transparency, minimize abuse and ensure reproducibility. Thirdly, frameworks will be required to obtain patient consent, perform ethical assessment and evaluate end-user perception. Lastly, proper health economic assessment (HEA) must be performed to provide financial visibility during the early phases of DLS development. This is necessary to manage resources prudently and guide the development of DLS.
Collapse
|
41
|
Digital Gonioscopy Based on Three-dimensional Anterior-Segment OCT: An International Multicenter Study. Ophthalmology 2021; 129:45-53. [PMID: 34619247 DOI: 10.1016/j.ophtha.2021.09.018] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2021] [Revised: 09/18/2021] [Accepted: 09/28/2021] [Indexed: 11/23/2022] Open
Abstract
PURPOSE To develop and evaluate the performance of a 3-dimensional (3D) deep-learning-based automated digital gonioscopy system (DGS) in detecting 2 major characteristics in eyes with suspected primary angle-closure glaucoma (PACG): (1) narrow iridocorneal angles (static gonioscopy, Task I) and (2) peripheral anterior synechiae (PAS) (dynamic gonioscopy, Task II) on OCT scans. DESIGN International, cross-sectional, multicenter study. PARTICIPANTS A total of 1.112 million images of 8694 volume scans (2294 patients) from 3 centers were included in this study (Task I, training/internal validation/external testing: 4515, 1101, and 2222 volume scans, respectively; Task II, training/internal validation/external testing: 378, 376, and 102 volume scans, respectively). METHODS For Task I, a narrow angle was defined as an eye in which the posterior pigmented trabecular meshwork was not visible in more than 180° without indentation in the primary position captured in the dark room from the scans. For Task II, PAS was defined as the adhesion of the iris to the trabecular meshwork. The diagnostic performance of the 3D DGS was evaluated in both tasks with gonioscopic records as reference. MAIN OUTCOME MEASURES The area under the curve (AUC), sensitivity, and specificity of the 3D DGS were calculated. RESULTS In Task I, 29.4% of patients had a narrow angle. The AUC, sensitivity, and specificity of 3D DGS on the external testing datasets were 0.943 (0.933-0.953), 0.867 (0.838-0.895), and 0.878 (0.859-0.896), respectively. For Task II, 13.8% of patients had PAS. The AUC, sensitivity, and specificity of 3D DGS were 0.902 (0.818-0.985), 0.900 (0.714-1.000), and 0.890 (0.841-0.938), respectively, on the external testing set at quadrant level following normal clinical practice; and 0.885 (0.836-0.933), 0.912 (0.816-1.000), and 0.700 (0.660-0.741), respectively, on the external testing set at clock-hour level. CONCLUSIONS The 3D DGS is effective in detecting eyes with suspected PACG. It has the potential to be used widely in the primary eye care community for screening of subjects at high risk of developing PACG.
Collapse
|
42
|
Abstract
Early detection and monitoring are critical to the diagnosis and management of glaucoma, a progressive optic neuropathy that causes irreversible blindness. Optical coherence tomography (OCT) has become a commonly utilized imaging modality that aids in the detection and monitoring of structural glaucomatous damage. Since its inception in 1991, OCT has progressed through multiple iterations, from time-domain OCT, to spectral-domain OCT, to swept-source OCT, all of which have progressively improved the resolution and speed of scans. Even newer technological advancements and OCT applications, such as adaptive optics, visible-light OCT, and OCT-angiography, have enriched the use of OCT in the evaluation of glaucoma. This article reviews current commercial and state-of-the-art OCT technologies and analytic techniques in the context of their utility for glaucoma diagnosis and management, as well as promising future directions.
Collapse
Affiliation(s)
- Alexi Geevarghese
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA;
| | - Gadi Wollstein
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA;
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, New York 11201, USA
- Center for Neural Science, NYU College of Arts and Sciences, New York, NY 10003, USA
| | - Hiroshi Ishikawa
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA;
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, New York 11201, USA
| | - Joel S Schuman
- Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA;
- Department of Biomedical Engineering, NYU Tandon School of Engineering, Brooklyn, New York 11201, USA
- Center for Neural Science, NYU College of Arts and Sciences, New York, NY 10003, USA
- Department of Physiology and Neuroscience, NYU Langone Health, NYU Grossman School of Medicine, New York, NY 10016, USA
| |
Collapse
|
43
|
Xiang Y, Zeng C, Liu B, Tan W, Wu J, Hu X, Han Y, Luo Q, Gong J, Liu J, Li Y. Deep Learning-Enabled Identification of Autoimmune Encephalitis on 3D Multi-Sequence MRI. J Magn Reson Imaging 2021; 55:1082-1092. [PMID: 34478565 DOI: 10.1002/jmri.27909] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 08/21/2021] [Accepted: 08/23/2021] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Autoimmune encephalitis (AE) is a noninfectious emergency with severe clinical attacks. It is difficult for the earlier diagnosis of acute AE due to the lack of antibody detection resources. PURPOSE To construct a deep learning (DL) algorithm using multi-sequence magnetic resonance imaging (MRI) for the identification of acute AE. STUDY TYPE Retrospective. POPULATION One hundred and sixty AE patients (90 women; median age 36), 177 herpes simplex virus encephalitis (HSVE) (89 women; median age 39), and 184 healthy controls (HC) (95 women; median age 39) were included. Fifty-two patients from another site were enrolled for external validation. FIELD STRENGTH/SEQUENCE 3.0 T; fast spin-echo (T1 WI, T2 WI, fluid attenuated inversion recovery imaging) and spin-echo echo-planar diffusion weighted imaging. ASSESSMENT Five DL models based on individual or combined four MRI sequences to classify the datasets as AE, HSVE, or HC. Reader experiment was further carried out by radiologists. STATISTICAL TESTS The discriminative performance of different models was assessed using the area under the receiver operating characteristic curve (AUC). The optimal threshold cut-off was identified when sensitivity and specificity were maximized (sensitivity + specificity - 1) in the validation set. Classification performance using confusion matrices was reported to evaluate the diagnostic value of the models and the radiologists' assessments before being assessed by the paired t-test (P < 0.05 was considered significant). RESULTS In the internal test set, the fusion model achieved the significantly greatest diagnostic performance than single-sequence DL models with AUCs of 0.828, 0.884, and 0.899 for AE, HSVE, and HC, respectively. The model demonstrated a consistently high performance in the external validation set with AUCs of 0.831 (AE), 0.882 (HSVE), and 0.892 (HC). The fusion model also demonstrated significantly higher performance than all radiologists in identifying AE (accuracy between the fuse model vs. average radiologist: 83% vs. 72%). DATA CONCLUSION The proposed DL algorithm derived from multi-sequence MRI provided desirable identification and classification of acute AE. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY: Stage 2.
Collapse
Affiliation(s)
- Yayun Xiang
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Chun Zeng
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | | | | | | | - Xiaofei Hu
- Department of Radiology, The Southwest Hospital of AMU, Chongqing, China
| | - Yongliang Han
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Qi Luo
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junwei Gong
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Junhang Liu
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| | - Yongmei Li
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China
| |
Collapse
|
44
|
Wang W, Wang L, Wang X, Zhou S, Lin S, Yang J. A Deep Learning System for Automatic Assessment of Anterior Chamber Angle in Ultrasound Biomicroscopy Images. Transl Vis Sci Technol 2021; 10:21. [PMID: 34570190 PMCID: PMC8479575 DOI: 10.1167/tvst.10.11.21] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/03/2022] Open
Abstract
Purpose To develop and assess a deep learning system that automatically detects angle closure and quantitatively measures angle parameters from ultrasound biomicroscopy (UBM) images using a deep learning algorithm. Methods A total of 3788 UBM images (2146 open angle and 1642 angle closure) from 1483 patients were collected. We developed a convolutional neural network (CNN) based on the InceptionV3 network for automatic classification of angle closure and open angle. For nonclosed images, we developed a CNN based on the EfficienttNetB3 network for the automatic localization of the scleral spur and the angle recess; then, the Unet network was used to segment the anterior chamber angle (ACA) tissue automatically. Based on the results of the latter two processes, we developed an algorithm to automatically measure the trabecular-iris angle (TIA500 and TIA750), angle-opening distance (AOD500 and AOD750), and angle recess area (ARA500 and ARA750) for quantitative evaluation of angle width. Results Using manual labeling as the reference standard, the ACA classification network's accuracy reached 98.18%, and the sensitivity and specificity for angle closure reached 98.74% and 97.44%, respectively. The deep learning system realized the automatic measurement of the angle parameters, and the mean of differences was generally small between automatic measurement and manual measurement. The coefficients of variation of TIA500, TIA750, AOD500, AOD750, ARA500, and ARA750 measured by the deep learning system were 5.77%, 4.67%, 10.76%, 7.71%, 16.77%, and 12.70%, respectively. The within-subject standard deviations of TIA500, TIA750, AOD500, AOD750, ARA500, and ARA750 were 5.77 degrees, 4.56 degrees, 155.92 µm, 147.51 µm, 0.10 mm2, and 0.12 mm2, respectively. The intraclass correlation coefficients of all the angle parameters were greater than 0.935. Conclusions The deep learning system can effectively and accurately evaluate the ACA automatically based on fully automated analysis of a UBM image. Translational Relevance The present work suggests that the deep learning system described here could automatically detect angle closure and quantitatively measure angle parameters from UBM images and enhancing the intelligent diagnosis and management of primary angle-closure glaucoma.
Collapse
Affiliation(s)
- Wensai Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Lingxiao Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Xiaochun Wang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Sheng Zhou
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| | - Song Lin
- Eye Institute and School of Optometry, Tianjin Medical University Eye Hospital, Tianjin, China
| | - Jun Yang
- Institute of Biomedical Engineering, Chinese Academy of Medical Sciences and Peking Union Medical College, Tianjin, China
| |
Collapse
|
45
|
García G, Del Amor R, Colomer A, Verdú-Monedero R, Morales-Sánchez J, Naranjo V. Circumpapillary OCT-focused hybrid learning for glaucoma grading using tailored prototypical neural networks. Artif Intell Med 2021; 118:102132. [PMID: 34412848 DOI: 10.1016/j.artmed.2021.102132] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/22/2022]
Abstract
Glaucoma is one of the leading causes of blindness worldwide and Optical Coherence Tomography (OCT) is the quintessential imaging technique for its detection. Unlike most of the state-of-the-art studies focused on glaucoma detection, in this paper, we propose, for the first time, a novel framework for glaucoma grading using raw circumpapillary B-scans. In particular, we set out a new OCT-based hybrid network which combines hand-driven and deep learning algorithms. An OCT-specific descriptor is proposed to extract hand-crafted features related to the retinal nerve fibre layer (RNFL). In parallel, an innovative CNN is developed using skip-connections to include tailored residual and attention modules to refine the automatic features of the latent space. The proposed architecture is used as a backbone to conduct a novel few-shot learning based on static and dynamic prototypical networks. The k-shot paradigm is redefined giving rise to a supervised end-to-end system which provides substantial improvements discriminating between healthy, early and advanced glaucoma samples. The training and evaluation processes of the dynamic prototypical network are addressed from two fused databases acquired via Heidelberg Spectralis system. Validation and testing results reach a categorical accuracy of 0.9459 and 0.8788 for glaucoma grading, respectively. Besides, the high performance reported by the proposed model for glaucoma detection deserves a special mention. The findings from the class activation maps are directly in line with the clinicians' opinion since the heatmaps pointed out the RNFL as the most relevant structure for glaucoma diagnosis.
Collapse
Affiliation(s)
- Gabriel García
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain.
| | - Rocío Del Amor
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Adrián Colomer
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| | - Rafael Verdú-Monedero
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Juan Morales-Sánchez
- Departamento de Tecnologías de la Información y las Comunicaciones, Universidad Politécnica de Cartagena, 30202 Cartagena, Spain
| | - Valery Naranjo
- Instituto de Investigación e Innovación en Bioingeniería, Universitat Politècnica de València, 46022 Valencia, Spain
| |
Collapse
|
46
|
Wong SH, Tsai JC. Telehealth and Screening Strategies in the Diagnosis and Management of Glaucoma. J Clin Med 2021; 10:3452. [PMID: 34441748 PMCID: PMC8396962 DOI: 10.3390/jcm10163452] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2021] [Revised: 07/31/2021] [Accepted: 08/02/2021] [Indexed: 11/16/2022] Open
Abstract
Telehealth has become a viable option for glaucoma screening and glaucoma monitoring due to advances in technology. The ability to measure intraocular pressure without an anesthetic and to take optic nerve photographs without pharmacologic pupillary dilation using portable equipment have allowed glaucoma screening programs to generate enough data for assessment. At home, patients can perform visual acuity testing, web-based visual field testing, rebound tonometry, and video visits with the physician to monitor for glaucomatous progression. Artificial intelligence will enhance the accuracy of data interpretation and inspire confidence in popularizing telehealth for glaucoma.
Collapse
Affiliation(s)
- Sze H. Wong
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York Eye and Ear Infirmary of Mount Sinai, New York, NY 10003, USA;
| | | |
Collapse
|
47
|
Zheng C, Koh V, Bian F, Li L, Xie X, Wang Z, Yang J, Chew PTK, Zhang M. Semi-supervised generative adversarial networks for closed-angle detection on anterior segment optical coherence tomography images: an empirical study with a small training dataset. ANNALS OF TRANSLATIONAL MEDICINE 2021; 9:1073. [PMID: 34422985 PMCID: PMC8339863 DOI: 10.21037/atm-20-7436] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/12/2020] [Accepted: 03/17/2021] [Indexed: 02/05/2023]
Abstract
BACKGROUND Semi-supervised learning algorithms can leverage an unlabeled dataset when labeling is limited or expensive to obtain. In the current study, we developed and evaluated a semi-supervised generative adversarial networks (GANs) model that detects closed-angle on anterior segment optical coherence tomography (AS-OCT) images using a small labeled dataset. METHODS In this cross-sectional study, a semi-supervised GANs model was developed for automatic closed-angle detection training on a small labeled and large unsupervised training dataset collected from the Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong (JSIEC). The closed-angle was defined as iris-trabecular contact beyond the scleral spur in AS-OCT images. We further developed two supervised deep learning (DL) models training on the same supervised dataset and the whole dataset separately. The semi-supervised GANs model and supervised DL models' performance were compared on two independent testing datasets from JSIEC (515 images) and the Department of Ophthalmology (84 images), National University Health System, respectively. The diagnostic performance was assessed by evaluation matrices, including the accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). RESULTS For closed-angle detection using clinician grading of AS-OCT imaging as the reference standard, the semi-supervised GANs model showed comparable performance, with AUCs of 0.97 (95% CI, 0.96-0.99) and 0.98 (95% CI, 0.94-1.00), compared with the supervised DL model (using the whole dataset) [AUCs of 0.97 (95% CI, 0.96-0.99), and 0.97 (95% CI, 0.94-1.00)]. When training on the same small supervised dataset, the semi-supervised GANs achieved performance at least as well as, if not better than, the supervised DL model [AUCs of 0.90 (95% CI: 0.84-0.96), and 0.92 (95% CI, 0.86-0.97)]. CONCLUSIONS The semi-supervised GANs method achieves diagnostic performance at least as good as a supervised DL model when trained on small labeled datasets. Further development of semi-supervised learning methods could be useful within clinical and research settings. TRIAL REGISTRATION NUMBER ChiCTR2000037892.
Collapse
Affiliation(s)
- Ce Zheng
- Department of Ophthalmology, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China, Shanghai, China
| | - Victor Koh
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Fang Bian
- Department of Ophthalmology, Deyang People’s Hospital, Deyang, China
| | - Luo Li
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| | - Xiaolin Xie
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| | - Zilei Wang
- Shanghai Children’s Hospital, Shanghai, China
| | - Jianlong Yang
- Ningbo Institute of Industrial Technology, Chinese Academy of Sciences, Ningbo, China
| | - Paul Tec Kuan Chew
- Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Mingzhi Zhang
- Joint Shantou International Eye Center of Shantou University and the Chinese University of Hong Kong, Shantou University Medical College, Shantou, China
| |
Collapse
|
48
|
Zhou Q, Wang J, Guo J, Huang Z, Ding M, Yuchi M, Zhang X. Anterior chamber angle classification in anterior segment optical coherence tomography images using hybrid attention based pyramidal convolutional network. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102686] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
|
49
|
Li W, Chen Q, Jiang C, Shi G, Deng G, Sun X. Automatic Anterior Chamber Angle Classification Using Deep Learning System and Anterior Segment Optical Coherence Tomography Images. Transl Vis Sci Technol 2021; 10:19. [PMID: 34111263 PMCID: PMC8142723 DOI: 10.1167/tvst.10.6.19] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2023] Open
Abstract
Purpose The purpose of this study was to develop a software package for the automatic classification of anterior chamber angle using anterior segment optical coherence tomography (AS-OCT). Methods AS-OCT images were collected from subjects with open, narrow, and closure anterior chamber angles, which were graded based on ultrasound biomicroscopy (UBM) results. The Inception version 3 network and the transfer learning technique were applied in the design of an algorithm for anterior chamber angle classification. The classification performance was evaluated by fivefold cross-validation and on an independent test dataset. Results The proposed algorithm reached a sensitivity of 0.999 and specificity of 1.000 in the judgment of closed and nonclosed angles. The overall classification of the proposed method in open angle, narrow angle, and angle-closure classifications reached a sensitivity of 0.989 and specificity of 0.995. Additionally, the sensitivity and specificity reached 1.000 and 1.000 for angle-closure, 0.983 and 0.993 for narrow angle, and 0.985 and 0.991 for open angle. Conclusions The experimental results showed that the proposed method can achieve a high accuracy of anterior chamber angle classification using AS-OCT images, and could be of value in future practice. Translational Relevance The proposed deep learning-based method that automate the classification of anterior chamber angle can facilitate clinical assessment of glaucoma.
Collapse
Affiliation(s)
- Wanyue Li
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Qian Chen
- Eye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.,Key NHC Key Laboratory of Myopia (Fudan University); Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Chunhui Jiang
- Eye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.,Key NHC Key Laboratory of Myopia (Fudan University); Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| | - Guohua Shi
- Jiangsu Key Laboratory of Medical Optics, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai, China
| | - Guohua Deng
- Department of Ophthalmology, the Third People's Hospital of Changzhou, Changzhou, Jiangsu, China
| | - Xinghuai Sun
- Eye Institute and Department of Ophthalmology, Eye and ENT Hospital, Fudan University, Shanghai, China.,Key NHC Key Laboratory of Myopia (Fudan University); Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, China
| |
Collapse
|
50
|
Machine Learning-Guided Prediction of Central Anterior Chamber Depth Using Slit Lamp Images from a Portable Smartphone Device. BIOSENSORS-BASEL 2021; 11:bios11060182. [PMID: 34198935 PMCID: PMC8227501 DOI: 10.3390/bios11060182] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/31/2021] [Revised: 05/31/2021] [Accepted: 06/02/2021] [Indexed: 11/26/2022]
Abstract
There is currently no objective portable screening modality for narrow angles in the community. In this prospective, single-centre image validation study, we used machine learning on slit lamp images taken with a portable smartphone device (MIDAS) to predict the central anterior chamber depth (ACD) of phakic patients with undilated pupils. Patients 60 years or older with no history of laser or intraocular surgery were recruited. Slit lamp images were taken with MIDAS, followed by anterior segment optical coherence tomography (ASOCT; Casia SS-1000, Tomey, Nagoya, Japan). After manual annotation of the anatomical landmarks of the slit lamp photos, machine learning was applied after image processing and feature extraction to predict the ACD. These values were then compared with those acquired from the ASOCT. Sixty-six eyes (right = 39, 59.1%) were included for analysis. The predicted ACD values formed a strong positive correlation with the measured ACD values from ASOCT (R2 = 0.91 for training data and R2 = 0.73 for test data). This study suggests the possibility of estimating central ACD using slit lamp images taken from portable devices.
Collapse
|