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Yoo TK, Kim D, Kim JS, Kim HS, Ryu IH, Lee IS, Kim JK, Na KH. Comparison of early visual outcomes after SMILE using VISUMAX 800 and VISUMAX 500 for myopia: a retrospective matched case-control study. Sci Rep 2024; 14:11989. [PMID: 38796537 PMCID: PMC11127987 DOI: 10.1038/s41598-024-62354-y] [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: 11/27/2023] [Accepted: 05/16/2024] [Indexed: 05/28/2024] Open
Abstract
VISUMAX 800 was introduced to improve the patient experience and clinical outcomes of small incision lenticule extraction (SMILE). This was a retrospective, matched, and case-control study (1:2) controlled for preoperative central corneal thickness and refractive error that compared early refractive and visual outcomes after SMILE using VISUMAX 800 and VISUMAX 500 to treat myopia. We included 50 eyes that underwent the VISUMAX 800 SMILE and 100 eyes that underwent the VISUMAX 500 SMILE. SMILE using VISUMAX 800 was performed using the CentraLign aid for vertex centration. Cyclotorsion was controlled by an OcuLign assistant in the VISUMAX 800 group after corneal marking. Corneal higher-order aberrations (HOAs) were evaluated using a Pentacam 1 month after surgery. No differences were observed in the pre- and post-operative refractive and visual outcomes at 1 day, 1 month, and 6 months after surgery. VISUMAX 800 induced less total HOAs than VISUMAX 500 (P = 0.036). No statistically significant differences were observed in the amounts of induced spherical aberrations or vertical and horizontal comas. No differences were observed in the 1 month and 6 months refractive and visual outcomes between two SMILE procedures, except for VISUMAX 800, which resulted in lower postoperative total HOAs than VISUMAX 500.
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Affiliation(s)
- Tae Keun Yoo
- Department of Ophthalmology, Hangil Eye Hospital, 35 Bupyeong-daero, Bupyeong-gu, Incheon, 21388, South Korea.
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea.
| | - Dongyoung Kim
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Jung Soo Kim
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Hee Sun Kim
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea
| | - Ik Hee Ryu
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - In Sik Lee
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea
| | - Jin Kuk Kim
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea
| | - Kun-Hoo Na
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea
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Salari F, Rafizadeh SM, Fakhredin H, Rajabi MT, Yaseri M, Hosseini F, Fekrazad R, Salari B. Prediction of substantial closed-globe injuries in orbital wall fractures. Int Ophthalmol 2024; 44:219. [PMID: 38713333 DOI: 10.1007/s10792-024-03113-w] [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: 11/23/2023] [Accepted: 03/24/2024] [Indexed: 05/08/2024]
Abstract
PURPOSE To determine risk factors for substantial closed-globe injuries in orbital fractures (SCGI) and to develop the best multivariate model for the prediction of SCGI. METHODS A retrospective study was performed on patients diagnosed with orbital fractures at Farabi Hospital between 2016 and 2022. Patients with a comprehensive ophthalmologic examination and orbital CT scan were included. Predictive signs or imaging findings for SCGI were identified by logistic regression (LR) analysis. Support vector machine (SVM), random forest regression (RFR), and extreme gradient boosting (XGBoost) were also trained using a fivefold cross-validation method. RESULTS A total of 415 eyes from 403 patients were included. Factors associated with an increased risk of SCGI were reduced uncorrected visual acuity (UCVA), increased difference between UCVA of the traumatic eye from the contralateral eye, older age, male sex, grade of periorbital soft tissue trauma, trauma in the occupational setting, conjunctival hemorrhage, extraocular movement restriction, number of fractured walls, presence of medial wall fracture, size of fracture, intraorbital emphysema and retrobulbar hemorrhage. The area under the curve of the receiver operating characteristic for LR, SVM, RFR, and XGBoost for the prediction of SCGI was 57.2%, 68.8%, 63.7%, and 73.1%, respectively. CONCLUSIONS Clinical and radiographic findings could be utilized to efficiently predict SCGI. XGBoost outperforms the logistic regression model in the prediction of SCGI and could be incorporated into clinical practice.
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Affiliation(s)
- Farhad Salari
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Qazvin Square, Tehran, Iran
| | - Seyed Mohsen Rafizadeh
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Qazvin Square, Tehran, Iran.
| | - Hanieh Fakhredin
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Qazvin Square, Tehran, Iran
| | - Mohammad Taher Rajabi
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Qazvin Square, Tehran, Iran
| | - Mehdi Yaseri
- Eye Research Center, Farabi Eye Hospital, Tehran University of Medical Sciences, Qazvin Square, Tehran, Iran
| | - Farhang Hosseini
- Department of Health Information Technology and Management, Shahid Beheshti University of Medical Sciences, Tehran, Iran
| | - Reza Fekrazad
- International Network for Photo Medicine and Photo Dynamic Therapy (INPMPDT), Universal Scientific Education and Research, Network (USERN), Tehran, Iran
| | - Behzad Salari
- Orthodontics Department, Faculty of Dentistry, Tehran Medical Sciences, Islamic Azad University, Shariati St, Tehran, Iran.
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Alnahedh TA, Taha M. Role of Machine Learning and Artificial Intelligence in the Diagnosis and Treatment of Refractive Errors for Enhanced Eye Care: A Systematic Review. Cureus 2024; 16:e57706. [PMID: 38711688 PMCID: PMC11071623 DOI: 10.7759/cureus.57706] [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] [Accepted: 04/04/2024] [Indexed: 05/08/2024] Open
Abstract
A significant contributor to blindness and visual impairment globally is uncorrected refractive error. To plan effective interventions, eye care professionals must promptly identify people at a high risk of acquiring myopia, and monitor disease progress. Artificial intelligence (AI) and machine learning (ML) have enormous potential to improve diagnosis and treatment. This systematic review explores the current state of ML and AI applications in the diagnoses and treatment of refractory errors in optometry. A systematic review and meta-analysis of studies evaluating the diagnostic performance of AI-based tools in PubMed was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. To find relevant studies on the use of ML or AI in the diagnosis or treatment of refractive errors in optometry, a thorough search was conducted in various electronic databases such as PubMed, Google Scholar, and Web of Science. The search was limited to studies published between January 2015 and December 2022. The search terms used were "refractive errors," "myopia," "optometry," "machine learning," "ophthalmology," and "artificial intelligence." A total of nine studies met the inclusion criteria and were included in the final analysis. ML is increasingly being utilized for automating clinical data processing as AI technology progresses, making the formerly labor-intensive work possible. AI models that primarily use a neural network demonstrated exceptional efficiency and performance in the analysis of vast medical data, rivaling board-certified, healthcare professionals. Several studies showed that ML models could support diagnosis and clinical decision-making. Moreover, an ML algorithm predicted future refraction values in patients with myopia. AI and ML models have great potential to improve the diagnosis and treatment of refractive errors in optometry.
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Affiliation(s)
- Taghreed A Alnahedh
- Optometry, King Abdullah International Medical Research Center (KAIMRC), National Guard Health Affairs, Riyadh, SAU
- Academic Affairs, King Saud Bin Abdulaziz University for Health Sciences College of Medicine, Riyadh, SAU
| | - Mohammed Taha
- Ophthalmology, King Saud Bin Abdulaziz University for Health Sciences College of Medicine, Riyadh, SAU
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Luft N, Mohr N, Spiegel E, Marchi H, Siedlecki J, Harrant L, Mayer WJ, Dirisamer M, Priglinger SG. Optimizing Refractive Outcomes of SMILE: Artificial Intelligence versus Conventional State-of-the-Art Nomograms. Curr Eye Res 2024; 49:252-259. [PMID: 38032001 DOI: 10.1080/02713683.2023.2282938] [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: 05/14/2023] [Accepted: 11/07/2023] [Indexed: 12/01/2023]
Abstract
PURPOSE AI (artificial intelligence)-based methodologies have become established tools for researchers and physicians in the entire field of ophthalmology. However, the potential of AI to optimize the refractive outcome of keratorefractive surgery by means of machine learning (ML)-based nomograms has not been exhausted yet. In this study, we wanted to comprehensively compare state-of-the-art conventional nomograms for Small-Incision-Lenticule-Extraction (SMILE) with a novel ML-based nomogram regarding both their spherical and astigmatic predictability. METHODS A total of 1,342 eyes were analyzed for creation of three different nomograms based on a linear model (LM), a generalized additive mixed model (GAMM) and an artificial-neuronal-network (ANN), respectively. A total of 16 patient- and treatment-related features were included. Each model was trained by 895 eyes and validated by the remaining 447 eyes. Predictability was assessed by the difference between attempted and achieved change in spherical equivalent (SE) and the difference between target induced astigmatism (TIA) and surgically induced astigmatism (SIA). The root mean squared error (RMSE) of each model was computed as a measure of overall model performance. RESULTS The RMSE of LM, GAMM and ANN were 0.355, 0.348 and 0.367 for the prediction of SE and 0.279, 0.278 and 0.290 for the astigmatic correction, respectively. By applying the created models, the theoretical yield of eyes within ±0.50 D of SE from target refraction improved from 82 to 83% (LM), 84% (GAMM) and 83% (ANN), respectively. Astigmatic outcomes showed an improvement of eyes within ±0.50 D from TIA from 90 to 93% (LM), 93% (GAMM) and 92% (ANN), respectively. Subjective manifest refraction was the single most influential covariate in all models. CONCLUSION Machine learning endorsed the validity of state-of-the-art linear and non-linear SMILE nomograms. However, improving the accuracy of subjective manifest refraction seems warranted for optimizing ±0.50 D SE predictability beyond an apparent methodological 90% limit.
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Affiliation(s)
- Nikolaus Luft
- Department of Ophthalmology University Hospital, LMU Munich, Munich, Germany
- SMILE Eyes Clinic, Linz, Austria
| | - Niklas Mohr
- Department of Ophthalmology University Hospital, LMU Munich, Munich, Germany
| | - Elmar Spiegel
- Core Facility Statistical Consulting, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
| | - Hannah Marchi
- Core Facility Statistical Consulting, Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH), Neuherberg, Germany
- Faculty of Business Administration and Economics, Bielefeld University, Bielefeld, Germany
| | - Jakob Siedlecki
- Department of Ophthalmology University Hospital, LMU Munich, Munich, Germany
| | - Lisa Harrant
- Department of Ophthalmology University Hospital, LMU Munich, Munich, Germany
| | - Wolfgang J Mayer
- Department of Ophthalmology University Hospital, LMU Munich, Munich, Germany
| | - Martin Dirisamer
- Department of Ophthalmology University Hospital, LMU Munich, Munich, Germany
- SMILE Eyes Clinic, Linz, Austria
| | - Siegfried G Priglinger
- Department of Ophthalmology University Hospital, LMU Munich, Munich, Germany
- SMILE Eyes Clinic, Linz, Austria
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Li J, Dai Y, Mu Z, Wang Z, Meng J, Meng T, Wang J. Choice of refractive surgery types for myopia assisted by machine learning based on doctors' surgical selection data. BMC Med Inform Decis Mak 2024; 24:41. [PMID: 38331788 PMCID: PMC10854042 DOI: 10.1186/s12911-024-02451-0] [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: 07/05/2023] [Accepted: 02/02/2024] [Indexed: 02/10/2024] Open
Abstract
In recent years, corneal refractive surgery has been widely used in clinics as an effective means to restore vision and improve the quality of life. When choosing myopia-refractive surgery, it is necessary to comprehensively consider the differences in equipment and technology as well as the specificity of individual patients, which heavily depend on the experience of ophthalmologists. In our study, we took advantage of machine learning to learn about the experience of ophthalmologists in decision-making and assist them in the choice of corneal refractive surgery in a new case. Our study was based on the clinical data of 7,081 patients who underwent corneal refractive surgery between 2000 and 2017 at the Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences. Due to the long data period, there were data losses and errors in this dataset. First, we cleaned the data and deleted the samples of key data loss. Then, patients were divided into three groups according to the type of surgery, after which we used SMOTE technology to eliminate imbalance between groups. Six statistical machine learning models, including NBM, RF, AdaBoost, XGBoost, BP neural network, and DBN were selected, and a ten-fold cross-validation and grid search were used to determine the optimal hyperparameters for better performance. When tested on the dataset, the multi-class RF model showed the best performance, with agreement with ophthalmologist decisions as high as 0.8775 and Macro F1 as high as 0.8019. Furthermore, the results of the feature importance analysis based on the SHAP technique were consistent with an ophthalmologist's practical experience. Our research will assist ophthalmologists in choosing appropriate types of refractive surgery and will have beneficial clinical effects.
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Affiliation(s)
- Jiajing Li
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China.
- Wangganzhicha Information Technology Inc., Nanjing, Jiangsu Province, China.
| | - Yuanyuan Dai
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China
| | - Zhicheng Mu
- School of Artificial Intelligence, China University of Mining and Technology (Beijing), Beijing, China
| | - Zhonghai Wang
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
- Key Laboratory of Ocular Fundus Diseases, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
| | - Juan Meng
- Community Health Service Center of Douhudi Town, Gongan County, Jingzhou, Hubei Province, China
| | - Tao Meng
- Wangganzhicha Information Technology Inc., Nanjing, Jiangsu Province, China
| | - Jimin Wang
- Department of Information Management, Peking University, Beijing, China
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Koo S, Kim WK, Park YK, Jun K, Kim D, Ryu IH, Kim JK, Yoo TK. Development of a Machine-Learning-Based Tool for Overnight Orthokeratology Lens Fitting. Transl Vis Sci Technol 2024; 13:17. [PMID: 38386347 PMCID: PMC10896231 DOI: 10.1167/tvst.13.2.17] [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: 06/10/2023] [Accepted: 01/15/2024] [Indexed: 02/23/2024] Open
Abstract
Purpose Orthokeratology (ortho-K) is widely used to control myopia. Overnight ortho-K lens fitting with the selection of appropriate parameters is an important technique for achieving successful reductions in myopic refractive error. In this study, we developed a machine-learning model that could select ortho-K lens parameters at an expert level. Methods Machine-learning models were established to predict the optimal ortho-K parameters, including toric lens option (toric or non-toric), overall diameter (OAD; 10.5 or 11.0 mm), base curve (BC), return zone depth (RZD), landing zone angle (LZA), and lens sagittal depth (LensSag). The analysis included 547 eyes of 297 Korean adolescents with myopia or astigmatism. The dataset was randomly divided into training (80%, n = 437 eyes) and validation (20%, n = 110 eyes) sets at the patient level. The model was trained based on clinical ortho-K lens fitting performed by highly experienced experts and ophthalmic measurements. Results The final machine-learning models showed accuracies of 92.7% and 86.4% for predicting the toric lens option and OAD, respectively. The mean absolute errors for the BC, RZD, LZA, and LensSag predictions were 0.052 mm, 2.727 µm, 0.118°, and 5.215 µm, respectively. The machine-learning model outperformed the manufacturer's conventional initial lens selector in predicting BC and RZD. Conclusions We developed an expert-level machine-learning-based model for determining comprehensive ortho-K lens parameters. We also created a web-based application. Translational Relevance This model may provide more accurate fitting parameters for lenses than those of conventional calculations, thus reducing the need to rely on trial and error.
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Affiliation(s)
| | - Wook Kyum Kim
- Contact Lens Clinic, B&VIIT Eye Center, Seoul, South Korea
| | - Yoo Kyung Park
- Contact Lens Clinic, B&VIIT Eye Center, Seoul, South Korea
| | - Kiwon Jun
- Myopia Research Lab, VISUWORKS, Seoul, South Korea
| | | | - Ik Hee Ryu
- Myopia Research Lab, VISUWORKS, Seoul, South Korea
- Department of Ophthalmology and Vision Science, B&VIIT Eye Center, Seoul, South Korea
| | - Jin Kuk Kim
- Myopia Research Lab, VISUWORKS, Seoul, South Korea
- Department of Ophthalmology and Vision Science, B&VIIT Eye Center, Seoul, South Korea
| | - Tae Keun Yoo
- Myopia Research Lab, VISUWORKS, Seoul, South Korea
- Department of Ophthalmology and Vision Science, B&VIIT Eye Center, Seoul, South Korea
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Di Y, Fang H, Luo Y, Li Y, Xu Y. Predicting Implantable Collamer Lens Vault Using Machine Learning Based on Various Preoperative Biometric Factors. Transl Vis Sci Technol 2024; 13:8. [PMID: 38224328 PMCID: PMC10793387 DOI: 10.1167/tvst.13.1.8] [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: 08/30/2023] [Accepted: 12/06/2023] [Indexed: 01/16/2024] Open
Abstract
Purpose To predict the vault size after Implantable Collamer Lens (ICL) V4c implantation using machine learning methods and to compare the predicted vault with the conventional manufacturer's nomogram. Methods This study included 707 patients (707 eyes) who underwent ICL V4c implantation at the Department of Ophthalmology, Peking Union Medical College Hospital, from September 2019 to January 2022. Random Forest Regression (RFR), XGBoost, and linear regression (LR) were used to predict the vault size 1 week after ICL V4c implantation. The mean absolute error (MAE), median absolute error (MedAE), root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and Bland-Altman plot were utilized to compare the prediction performance of these machine learning methods. Results The dataset was divided into a training set of 180 patients (180 eyes) and a test set of 527 patients (527 eyes). XGBoost had the lowest prediction error, with mean MAE, RMSE, and SMAPE values of 121.70 µm, 148.87 µm, and 19.13%, respectively. The Bland‒Altman plots of RFR and XGBoost showed better prediction consistency than LR. However, XGBoost showed narrower 95% limits of agreement (LoA) than RFR, ranging from -307.12 to 256.59 µm. Conclusions XGBoost demonstrated better predictive performance than RFR and LR, as it had the lowest prediction error and the narrowest 95% LoA. Machine learning may be applicable for vault prediction, and it might be helpful for reducing the complications and the secondary surgery rate. Translational Relevance Using the proposed machine learning model, surgeons can consider the postoperative vault to reduce the surgical complications.
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Affiliation(s)
- Yu Di
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Huihui Fang
- School of Future Technology, South China University of Technology, Guangzhou, China
- Pazhou Lab, Guangzhou, China
| | - Yan Luo
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Ying Li
- Department of Ophthalmology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China
| | - Yanwu Xu
- School of Future Technology, South China University of Technology, Guangzhou, China
- Pazhou Lab, Guangzhou, China
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Nuliqiman M, Xu M, Sun Y, Cao J, Chen P, Gao Q, Xu P, Ye J. Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations. Clin Ophthalmol 2023; 17:3499-3511. [PMID: 38026589 PMCID: PMC10674717 DOI: 10.2147/opth.s438127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Accepted: 11/09/2023] [Indexed: 12/01/2023] Open
Abstract
Artificial Intelligence (AI) has found rapidly growing applications in ophthalmology, achieving robust recognition and classification in most kind of ocular diseases. Ophthalmic surgery is one of the most delicate microsurgery, requiring high fineness and stability of surgeons. The massive demand of the AI assist ophthalmic surgery will constitute an important factor in boosting accelerate precision medicine. In clinical practice, it is instrumental to update and review the considerable evidence of the current AI technologies utilized in the investigation of ophthalmic surgery involved in both the progression and innovation of precision medicine. Bibliographic databases including PubMed and Google Scholar were searched using keywords such as "ophthalmic surgery", "surgical selection", "candidate screening", and "robot-assisted surgery" to find articles about AI technology published from 2018 to 2023. In addition to the Editorials and letters to the editor, all types of approaches are considered. In this paper, we will provide an up-to-date review of artificial intelligence in eye surgery, with a specific focus on its application to candidate screening, surgery selection, postoperative prediction, and real-time intraoperative guidance.
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Affiliation(s)
- Maimaiti Nuliqiman
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of China
| | - Mingyu Xu
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of China
| | - Yiming Sun
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of China
| | - Jing Cao
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of China
| | - Pengjie Chen
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of China
| | - Qi Gao
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of China
| | - Peifang Xu
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of China
| | - Juan Ye
- Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of China
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Du HQ, Dai Q, Zhang ZH, Wang CC, Zhai J, Yang WH, Zhu TP. Artificial intelligence-aided diagnosis and treatment in the field of optometry. Int J Ophthalmol 2023; 16:1406-1416. [PMID: 37724269 PMCID: PMC10475639 DOI: 10.18240/ijo.2023.09.06] [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: 04/11/2023] [Accepted: 06/14/2023] [Indexed: 09/20/2023] Open
Abstract
With the rapid development of computer technology, the application of artificial intelligence (AI) to ophthalmology has gained prominence in modern medicine. As modern optometry is closely related to ophthalmology, AI research on optometry has also increased. This review summarizes current AI research and technologies used for diagnosis in optometry, related to myopia, strabismus, amblyopia, optical glasses, contact lenses, and other aspects. The aim is to identify mature AI models that are suitable for research on optometry and potential algorithms that may be used in future clinical practice.
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Affiliation(s)
- Hua-Qing Du
- Zhejiang University, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310027, Zhejiang Province, China
| | - Qi Dai
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
| | - Zu-Hui Zhang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
| | - Chen-Chen Wang
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
| | - Jing Zhai
- National Clinical Research Center for Ocular Diseases, Eye Hospital, Wenzhou Medical University, Wenzhou 325027, Zhejiang Province, China
| | - Wei-Hua Yang
- Shenzhen Eye Institute, Shenzhen Eye Hospital, Jinan University, Shenzhen 518040, Guangdong Province, China
| | - Tie-Pei Zhu
- Eye Center, Second Affiliated Hospital of Zhejiang University, School of Medicine, Hangzhou 310002, Zhejiang Province, China
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Tan TF, Dai P, Zhang X, Jin L, Poh S, Hong D, Lim J, Lim G, Teo ZL, Liu N, Ting DSW. Explainable artificial intelligence in ophthalmology. Curr Opin Ophthalmol 2023; 34:422-430. [PMID: 37527200 DOI: 10.1097/icu.0000000000000983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/03/2023]
Abstract
PURPOSE OF REVIEW Despite the growing scope of artificial intelligence (AI) and deep learning (DL) applications in the field of ophthalmology, most have yet to reach clinical adoption. Beyond model performance metrics, there has been an increasing emphasis on the need for explainability of proposed DL models. RECENT FINDINGS Several explainable AI (XAI) methods have been proposed, and increasingly applied in ophthalmological DL applications, predominantly in medical imaging analysis tasks. SUMMARY We summarize an overview of the key concepts, and categorize some examples of commonly employed XAI methods. Specific to ophthalmology, we explore XAI from a clinical perspective, in enhancing end-user trust, assisting clinical management, and uncovering new insights. We finally discuss its limitations and future directions to strengthen XAI for application to clinical practice.
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Affiliation(s)
- Ting Fang Tan
- Artificial Intelligence and Digital Innovation Research Group
- Singapore National Eye Centre, Singapore General Hospital
| | - Peilun Dai
- Institute of High Performance Computing, A∗STAR
| | - Xiaoman Zhang
- Duke-National University of Singapore Medical School, Singapore
| | - Liyuan Jin
- Artificial Intelligence and Digital Innovation Research Group
- Duke-National University of Singapore Medical School, Singapore
| | - Stanley Poh
- Singapore National Eye Centre, Singapore General Hospital
| | - Dylan Hong
- Artificial Intelligence and Digital Innovation Research Group
| | - Joshua Lim
- Singapore National Eye Centre, Singapore General Hospital
| | - Gilbert Lim
- Artificial Intelligence and Digital Innovation Research Group
| | - Zhen Ling Teo
- Artificial Intelligence and Digital Innovation Research Group
- Singapore National Eye Centre, Singapore General Hospital
| | - Nan Liu
- Artificial Intelligence and Digital Innovation Research Group
- Duke-National University of Singapore Medical School, Singapore
| | - Daniel Shu Wei Ting
- Artificial Intelligence and Digital Innovation Research Group
- Singapore National Eye Centre, Singapore General Hospital
- Duke-National University of Singapore Medical School, Singapore
- Byers Eye Institute, Stanford University, Stanford, California, USA
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Li Y, Zhao H, Fan Y, Hu J, Li S, Wang K, Zhao M. A machine learning-based algorithm for estimating the original corneal curvature based on corneal topography after orthokeratology. Cont Lens Anterior Eye 2023; 46:101862. [PMID: 37208285 DOI: 10.1016/j.clae.2023.101862] [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: 10/07/2022] [Revised: 03/06/2023] [Accepted: 05/10/2023] [Indexed: 05/21/2023]
Abstract
OBJECTIVE To estimate the original corneal curvature after orthokeratology by applying a machine learning-based algorithm. METHODS A total of 497 right eyes of 497 patients undergoing overnight orthokeratology for myopia for more than 1 year were enrolled in this retrospective study. All patients were fitted with lenses from Paragon CRT. Corneal topography was obtained by a Sirius corneal topography system (CSO, Italy). Original flat K (K1) and original steep K (K2) were set as the targets of calculation. The importance of each variable was explored by Fisher's criterion. Two machine learning models were established to allow adaptation to more situations. Bagging Tree, Gaussian process, support vector machine (SVM), and decision tree were used for prediction. RESULTS K2 after one year of orthokeratology (K2after) was most important in the prediction of K1 and K2. Bagging Tree performed best in both models 1 and 2 for K1 prediction (R = 0.812, RMSE = 0.855 in model 1 and R = 0.812, RMSE = 0.858 in model 2) and K2 prediction (R = 0.831, RMSE = 0.898 in model 1 and R = 0.837, RMSE = 0.888 in model 2). In model 1, the difference was 0.006 ± 1.34 D (p = 0.93) between the predictive value of K1 and the true value of K1 (K1before) and was 0.005 ± 1.51 D(p = 0.94) between the predictive value of K2 and the true value of K2 (K2before). In model 2, the difference was -0.056 ± 1.75 D (p = 0.59) between the predictive value of K1 and K1before and was 0.017 ± 2.01 D(p = 0.88) between the predictive value of K2 and K2before. CONCLUSION Bagging Tree performed best in predicting K1 and K2. Machine learning can be applied to predict the corneal curvature for those who cannot provide the initial corneal parameters in the outpatient clinic, providing a relatively certain degree of reference for the refitting of the Ortho-k lenses.
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Affiliation(s)
- Yujing Li
- Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China; Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China; College of Optemetry, Peking University Health Science Centre, Beijing, China; Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Heng Zhao
- Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China; Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China; College of Optemetry, Peking University Health Science Centre, Beijing, China; Institute of Medical Technology, Peking University Health Science Centre, Beijing, China; Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Yuzhuo Fan
- Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China; Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China; College of Optemetry, Peking University Health Science Centre, Beijing, China; Institute of Medical Technology, Peking University Health Science Centre, Beijing, China; Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Jie Hu
- Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China; Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China; College of Optemetry, Peking University Health Science Centre, Beijing, China; Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Siying Li
- Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China; Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China; College of Optemetry, Peking University Health Science Centre, Beijing, China; Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
| | - Kai Wang
- Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China; Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China; College of Optemetry, Peking University Health Science Centre, Beijing, China; Institute of Medical Technology, Peking University Health Science Centre, Beijing, China; Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China.
| | - Mingwei Zhao
- Department of Ophthalmology & Clinical Centre of Optometry, Peking University People's Hospital, Beijing, China; Eye Disease and Optometry Institute, Peking University People's Hospital, Beijing, China; College of Optemetry, Peking University Health Science Centre, Beijing, China; Institute of Medical Technology, Peking University Health Science Centre, Beijing, China; Beijing Key Laboratory of the Diagnosis and Therapy of Retinal and Choroid Diseases, Beijing, China
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12
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Choi H, Kim T, Kim SJ, Sa BG, Ryu IH, Lee IS, Kim JK, Han E, Kim HK, Yoo TK. Predicting Postoperative Anterior Chamber Angle for Phakic Intraocular Lens Implantation Using Preoperative Anterior Segment Metrics. Transl Vis Sci Technol 2023; 12:10. [PMID: 36607625 PMCID: PMC9836008 DOI: 10.1167/tvst.12.1.10] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
Purpose The anterior chamber angle (ACA) is a critical factor in posterior chamber phakic intraocular lens (EVO Implantable Collamer Lens [ICL]) implantation. Herein, we predicted postoperative ACAs to select the optimal ICL size to reduce narrow ACA-related complications. Methods Regression models were constructed using pre-operative anterior segment optical coherence tomography metrics to predict postoperative ACAs, including trabecular-iris angles (TIAs) and scleral-spur angles (SSAs) at 500 µm and 750 µm from the scleral spur (TIA500, TIA750, SSA500, and SSA750). Data from three expert surgeons were assigned to the development (N = 430 eyes) and internal validation (N = 108 eyes) datasets. Additionally, data from a novice surgeon (N = 42 eyes) were used for external validation. Results Postoperative ACAs were highly predictable using the machine-learning (ML) technique (extreme gradient boosting regression [XGBoost]), with mean absolute errors (MAEs) of 4.42 degrees, 3.77 degrees, 5.25 degrees, and 4.30 degrees for TIA500, TIA750, SSA500, and SSA750, respectively, in internal validation. External validation also showed MAEs of 3.93 degrees, 3.86 degrees, 5.02 degrees, and 4.74 degrees for TIA500, TIA750, SSA500, and SSA750, respectively. Linear regression using the pre-operative anterior chamber depth, anterior chamber width, crystalline lens rise, TIA, and ICL size also exhibited good performance, with no significant difference compared with XGBoost in the validation sets. Conclusions We developed linear regression and ML models to predict postoperative ACAs for ICL surgery anterior segment metrics. These will prevent surgeons from overlooking the risks associated with the narrowing of the ACA. Translational Relevance Using the proposed algorithms, surgeons can consider the postoperative ACAs to increase surgical accuracy and safety.
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Affiliation(s)
- Hannuy Choi
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea
| | - Taein Kim
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Su Jeong Kim
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Beom Gi Sa
- Research and Development Department, VISUWORKS, Seoul, South Korea
| | - Ik Hee Ryu
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea,Research and Development Department, VISUWORKS, Seoul, South Korea
| | - In Sik Lee
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea
| | - Jin Kuk Kim
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea
| | - Eoksoo Han
- Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea
| | - Hong Kyu Kim
- Department of Ophthalmology, Dankook University Hospital, Dankook University College of Medicine, Cheonan, South Korea
| | - Tae Keun Yoo
- Department of Refractive Surgery, B&VIIT Eye Center, Seoul, South Korea,Research and Development Department, VISUWORKS, Seoul, South Korea
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Wang S, Ji Y, Bai W, Ji Y, Li J, Yao Y, Zhang Z, Jiang Q, Li K. Advances in artificial intelligence models and algorithms in the field of optometry. Front Cell Dev Biol 2023; 11:1170068. [PMID: 37187617 PMCID: PMC10175695 DOI: 10.3389/fcell.2023.1170068] [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: 02/27/2023] [Accepted: 04/17/2023] [Indexed: 05/17/2023] Open
Abstract
The rapid development of computer science over the past few decades has led to unprecedented progress in the field of artificial intelligence (AI). Its wide application in ophthalmology, especially image processing and data analysis, is particularly extensive and its performance excellent. In recent years, AI has been increasingly applied in optometry with remarkable results. This review is a summary of the application progress of different AI models and algorithms used in optometry (for problems such as myopia, strabismus, amblyopia, keratoconus, and intraocular lens) and includes a discussion of the limitations and challenges associated with its application in this field.
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Affiliation(s)
- Suyu Wang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yuke Ji
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Wen Bai
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yun Ji
- Affiliated Hospital of Shandong University of Traditional Chinese Medicine, Jinan, Shandong, China
| | - Jiajun Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Yujia Yao
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Ziran Zhang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
| | - Qin Jiang
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
| | - Keran Li
- Department of Ophthalmology, The Affiliated Eye Hospital of Nanjing Medical University, Nanjing, China
- The Fourth School of Clinical Medicine, Nanjing Medical University, Nanjing, China
- *Correspondence: Qin Jiang, ; Keran Li,
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Loh HW, Ooi CP, Seoni S, Barua PD, Molinari F, Acharya UR. Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022). COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 226:107161. [PMID: 36228495 DOI: 10.1016/j.cmpb.2022.107161] [Citation(s) in RCA: 84] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 09/16/2022] [Accepted: 09/25/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND AND OBJECTIVES Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare. Hence, explainable artificial intelligence (XAI) has been introduced as a technique that can provide confidence in the model's prediction by explaining how the prediction is derived, thereby encouraging the use of AI systems in healthcare. The primary goal of this review is to provide areas of healthcare that require more attention from the XAI research community. METHODS Multiple journal databases were thoroughly searched using PRISMA guidelines 2020. Studies that do not appear in Q1 journals, which are highly credible, were excluded. RESULTS In this review, we surveyed 99 Q1 articles covering the following XAI techniques: SHAP, LIME, GradCAM, LRP, Fuzzy classifier, EBM, CBR, rule-based systems, and others. CONCLUSION We discovered that detecting abnormalities in 1D biosignals and identifying key text in clinical notes are areas that require more attention from the XAI research community. We hope this is review will encourage the development of a holistic cloud system for a smart city.
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Affiliation(s)
- Hui Wen Loh
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore
| | - Silvia Seoni
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - Prabal Datta Barua
- Faculty of Engineering and Information Technology, University of Technology Sydney, Australia; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia
| | - Filippo Molinari
- Department of Electronics and Telecommunications, Biolab, Politecnico di Torino, Torino 10129, Italy
| | - U Rajendra Acharya
- School of Science and Technology, Singapore University of Social Sciences, Singapore; School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Australia; School of Engineering, Ngee Ann Polytechnic, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taiwan; Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan.
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Park D, Cho JM, Yang JW, Yang D, Kim M, Oh G, Kwon HD. Classification of expert-level therapeutic decisions for degenerative cervical myelopathy using ensemble machine learning algorithms. Front Surg 2022; 9:1010420. [PMID: 36147698 PMCID: PMC9485547 DOI: 10.3389/fsurg.2022.1010420] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2022] [Accepted: 08/19/2022] [Indexed: 11/28/2022] Open
Abstract
Background Therapeutic decisions for degenerative cervical myelopathy (DCM) are complex and should consider various factors. We aimed to develop machine learning (ML) models for classifying expert-level therapeutic decisions in patients with DCM. Methods This retrospective cross-sectional study included patients diagnosed with DCM, and the diagnosis of DCM was confirmed clinically and radiologically. The target outcomes were defined as conservative treatment, anterior surgical approaches (ASA), and posterior surgical approaches (PSA). We performed the following classifications using ML algorithms: multiclass, one-versus-rest, and one-versus-one. Two ensemble ML algorithms were used: random forest (RF) and extreme gradient boosting (XGB). The area under the receiver operating characteristic curve (AUC-ROC) was the primary metric. We also identified the variable importance for each classification. Results In total, 304 patients were included (109 conservative, 66 ASA, 125 PSA, and 4 combined surgeries). For multiclass classification, the AUC-ROC of RF and XGB models were 0.91 and 0.92, respectively. In addition, ML models showed AUC-ROC values of >0.9 for all types of binary classifications. Variable importance analysis revealed that the modified Japanese Orthopaedic Association score and central motor conduction time were the two most important variables for distinguishing between conservative and surgical treatments. When classifying ASA and PSA, the number of involved levels, age, and body mass index were important contributing factors. Conclusion ML-based classification of DCM therapeutic options is valid and feasible. This study can be a basis for establishing generalizable ML-based surgical decision models for DCM. Further studies are needed with a large multicenter database.
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Affiliation(s)
- Dougho Park
- Department of Rehabilitation Medicine, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Jae Man Cho
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Joong Won Yang
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Donghoon Yang
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Mansu Kim
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Gayeoul Oh
- Department of Radiology, Pohang Stroke and Spine Hospital, Pohang, South Korea
| | - Heum Dai Kwon
- Department of Neurosurgery, Pohang Stroke and Spine Hospital, Pohang, South Korea
- Correspondence: Heum Dai Kwon
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Shi Y, Zou Y, Liu J, Wang Y, Chen Y, Sun F, Yang Z, Cui G, Zhu X, Cui X, Liu F. Ultrasound-based radiomics XGBoost model to assess the risk of central cervical lymph node metastasis in patients with papillary thyroid carcinoma: Individual application of SHAP. Front Oncol 2022; 12:897596. [PMID: 36091102 PMCID: PMC9458917 DOI: 10.3389/fonc.2022.897596] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Accepted: 08/08/2022] [Indexed: 11/13/2022] Open
Abstract
ObjectivesA radiomics-based explainable eXtreme Gradient Boosting (XGBoost) model was developed to predict central cervical lymph node metastasis (CCLNM) in patients with papillary thyroid carcinoma (PTC), including positive and negative effects.MethodsA total of 587 PTC patients admitted at Binzhou Medical University Hospital from 2017 to 2021 were analyzed retrospectively. The patients were randomized into the training and test cohorts with an 8:2 ratio. Radiomics features were extracted from ultrasound images of the primary PTC lesions. The minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression were used to select CCLNM positively-related features and radiomics scores were constructed. Clinical features, ultrasound features, and radiomics score were screened out by the Boruta algorithm, and the XGBoost model was constructed from these characteristics. SHapley Additive exPlanations (SHAP) was used for individualized and visualized interpretation. SHAP addressed the cognitive opacity of machine learning models.ResultsEleven radiomics features were used to calculate the radiomics score. Five critical elements were used to build the XGBoost model: capsular invasion, radiomics score, diameter, age, and calcification. The area under the curve was 91.53% and 90.88% in the training and test cohorts, respectively. SHAP plots showed the influence of each parameter on the XGBoost model, including positive (i.e., capsular invasion, radiomics score, diameter, and calcification) and negative (i.e., age) impacts. The XGBoost model outperformed the radiologist, increasing the AUC by 44%.ConclusionsThe radiomics-based XGBoost model predicted CCLNM in PTC patients. Visual interpretation using SHAP made the model an effective tool for preoperative guidance of clinical procedures, including positive and negative impacts.
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Affiliation(s)
- Yan Shi
- Binzhou Medical University Hospital, Binzhou, China
| | - Ying Zou
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | - Jihua Liu
- First Teaching Hospital of Tianjin University of Traditional Chinese Medicine, Tianjin, China
- National Clinical Research Center for Chinese Medicine Acupuncture and Moxibustion, Tianjin, China
| | | | | | - Fang Sun
- Binzhou Medical University Hospital, Binzhou, China
| | - Zhi Yang
- Binzhou Medical University Hospital, Binzhou, China
| | - Guanghe Cui
- Binzhou Medical University Hospital, Binzhou, China
| | - Xijun Zhu
- Binzhou Medical University Hospital, Binzhou, China
| | - Xu Cui
- Binzhou Medical University Hospital, Binzhou, China
| | - Feifei Liu
- Binzhou Medical University Hospital, Binzhou, China
- Peking University People’s Hospital, Beijing, China
- *Correspondence: Feifei Liu,
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Ghazal TM, Hasan MK, Abdullah SNHS, Bakar KAA, Al Hamadi H. Private blockchain-based encryption framework using computational intelligence approach. EGYPTIAN INFORMATICS JOURNAL 2022. [DOI: 10.1016/j.eij.2022.06.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
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18
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Eldrandaly KA, Abdel-Basset M, Ibrahim M, Abdel-Aziz NM. Explainable and secure artificial intelligence: taxonomy, cases of study, learned lessons, challenges and future directions. ENTERP INF SYST-UK 2022. [DOI: 10.1080/17517575.2022.2098537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
Affiliation(s)
| | | | - Mahmoud Ibrahim
- Faculty of Computers and Informatics, Zagazig University, Zagazig, Egypt
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Kim J, Ryu IH, Kim JK, Lee IS, Kim HK, Han E, Yoo TK. Machine learning predicting myopic regression after corneal refractive surgery using preoperative data and fundus photography. Graefes Arch Clin Exp Ophthalmol 2022; 260:3701-3710. [PMID: 35748936 DOI: 10.1007/s00417-022-05738-y] [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: 01/05/2022] [Revised: 05/28/2022] [Accepted: 06/14/2022] [Indexed: 11/04/2022] Open
Abstract
PURPOSE Myopic regression after surgery is the most common long-term complication of refractive surgery, but it is difficult to identify myopic regression without long-term observation. This study aimed to develop machine learning models to identify high-risk patients for refractive regression based on preoperative data and fundus photography. METHODS This retrospective study assigned subjects to the training (n = 1606 eyes) and validation (n = 403 eyes) datasets with chronological data splitting. Machine learning models with ResNet50 (for image analysis) and XGBoost (for integration of all variables and fundus photography) were developed based on subjects who underwent corneal refractive surgery. The primary outcome was the predictive performance for the presence of myopic regression at 4 years of follow-up examination postoperatively. RESULTS By integrating all factors and fundus photography, the final combined machine learning model showed good performance to predict myopic regression of more than 0.5 D (area under the receiver operating characteristic curve [ROC-AUC], 0.753; 95% confidence interval [CI], 0.710-0.793). The performance of the final model was better than the single ResNet50 model only using fundus photography (ROC-AUC, 0.673; 95% CI, 0.627-0.716). The top-five most important input features were fundus photography, preoperative anterior chamber depth, planned ablation thickness, age, and preoperative central corneal thickness. CONCLUSION Our machine learning algorithm provides an efficient strategy to identify high-risk patients with myopic regression without additional labor, cost, and time. Surgeons might benefit from preoperative risk assessment of myopic regression, patient counseling before surgery, and surgical option decisions.
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Affiliation(s)
| | - Ik Hee Ryu
- B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea.,VISUWORKS, Seoul, South Korea
| | - Jin Kuk Kim
- B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea.,VISUWORKS, Seoul, South Korea
| | - In Sik Lee
- B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea
| | - Hong Kyu Kim
- Department of Ophthalmology, Dankook University Hospital, Dankook University College of Medicine, Cheonan, South Korea
| | - Eoksoo Han
- Electronics and Telecommunications Research Institute (ETRI), Daejeon, South Korea
| | - Tae Keun Yoo
- B&VIIT Eye Center, B2 GT Tower, 1317-23 Seocho-Dong, Seocho-Gu, Seoul, South Korea. .,VISUWORKS, Seoul, South Korea. .,Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, Cheongju, South Korea.
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Zhang C, Zhao J, Zhu Z, Li Y, Li K, Wang Y, Zheng Y. Applications of Artificial Intelligence in Myopia: Current and Future Directions. Front Med (Lausanne) 2022; 9:840498. [PMID: 35360739 PMCID: PMC8962670 DOI: 10.3389/fmed.2022.840498] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 12/17/2022] Open
Abstract
With the continuous development of computer technology, big data acquisition and imaging methods, the application of artificial intelligence (AI) in medical fields is expanding. The use of machine learning and deep learning in the diagnosis and treatment of ophthalmic diseases is becoming more widespread. As one of the main causes of visual impairment, myopia has a high global prevalence. Early screening or diagnosis of myopia, combined with other effective therapeutic interventions, is very important to maintain a patient's visual function and quality of life. Through the training of fundus photography, optical coherence tomography, and slit lamp images and through platforms provided by telemedicine, AI shows great application potential in the detection, diagnosis, progression prediction and treatment of myopia. In addition, AI models and wearable devices based on other forms of data also perform well in the behavioral intervention of myopia patients. Admittedly, there are still some challenges in the practical application of AI in myopia, such as the standardization of datasets; acceptance attitudes of users; and ethical, legal and regulatory issues. This paper reviews the clinical application status, potential challenges and future directions of AI in myopia and proposes that the establishment of an AI-integrated telemedicine platform will be a new direction for myopia management in the post-COVID-19 period.
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Zhang Y, Weng Y, Lund J. Applications of Explainable Artificial Intelligence in Diagnosis and Surgery. Diagnostics (Basel) 2022; 12:diagnostics12020237. [PMID: 35204328 PMCID: PMC8870992 DOI: 10.3390/diagnostics12020237] [Citation(s) in RCA: 48] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2021] [Revised: 01/11/2022] [Accepted: 01/13/2022] [Indexed: 02/06/2023] Open
Abstract
In recent years, artificial intelligence (AI) has shown great promise in medicine. However, explainability issues make AI applications in clinical usages difficult. Some research has been conducted into explainable artificial intelligence (XAI) to overcome the limitation of the black-box nature of AI methods. Compared with AI techniques such as deep learning, XAI can provide both decision-making and explanations of the model. In this review, we conducted a survey of the recent trends in medical diagnosis and surgical applications using XAI. We have searched articles published between 2019 and 2021 from PubMed, IEEE Xplore, Association for Computing Machinery, and Google Scholar. We included articles which met the selection criteria in the review and then extracted and analyzed relevant information from the studies. Additionally, we provide an experimental showcase on breast cancer diagnosis, and illustrate how XAI can be applied in medical XAI applications. Finally, we summarize the XAI methods utilized in the medical XAI applications, the challenges that the researchers have met, and discuss the future research directions. The survey result indicates that medical XAI is a promising research direction, and this study aims to serve as a reference to medical experts and AI scientists when designing medical XAI applications.
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Affiliation(s)
- Yiming Zhang
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China;
- School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK;
| | - Ying Weng
- School of Computer Science, Faculty of Science and Engineering, University of Nottingham Ningbo China, Ningbo 315100, China;
- Correspondence:
| | - Jonathan Lund
- School of Medicine, University of Nottingham, Nottingham NG7 2RD, UK;
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Veldhuis MS, Ariëns S, Ypma RJF, Abeel T, Benschop CCG. Explainable artificial intelligence in forensics: Realistic explanations for number of contributor predictions of DNA profiles. Forensic Sci Int Genet 2021; 56:102632. [PMID: 34839075 DOI: 10.1016/j.fsigen.2021.102632] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 11/18/2022]
Abstract
Machine learning obtains good accuracy in determining the number of contributors (NOC) in short tandem repeat (STR) mixture DNA profiles. However, the models used so far are not understandable to users as they only output a prediction without any reasoning for that conclusion. Therefore, we leverage techniques from the field of explainable artificial intelligence (XAI) to help users understand why specific predictions are made. Where previous attempts at explainability for NOC estimation have relied upon using simpler, more understandable models that achieve lower accuracy, we use techniques that can be applied to any machine learning model. Our explanations incorporate SHAP values and counterfactual examples for each prediction into a single visualization. Existing methods for generating counterfactuals focus on uncorrelated features. This makes them inappropriate for the highly correlated features derived from STR data for NOC estimation, as these techniques simulate combinations of features that could not have resulted from an STR profile. For this reason, we have constructed a new counterfactual method, Realistic Counterfactuals (ReCo), which generates realistic counterfactual explanations for correlated data. We show that ReCo outperforms state-of-the-art methods on traditional metrics, as well as on a novel realism score. A user evaluation of the visualization shows positive opinions of end-users, which is ultimately the most appropriate metric in assessing explanations for real-world settings.
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Affiliation(s)
- Marthe S Veldhuis
- Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands; Netherlands Forensic Institute, Division of Digital and Biometric Traces, Laan van Ypenburg 6, 2497GB The Hague, The Netherlands.
| | - Simone Ariëns
- Netherlands Forensic Institute, Division of Digital and Biometric Traces, Laan van Ypenburg 6, 2497GB The Hague, The Netherlands.
| | - Rolf J F Ypma
- Netherlands Forensic Institute, Division of Digital and Biometric Traces, Laan van Ypenburg 6, 2497GB The Hague, The Netherlands.
| | - Thomas Abeel
- Delft University of Technology, Mekelweg 5, 2628 CD Delft, The Netherlands.
| | - Corina C G Benschop
- Netherlands Forensic Institute, Division of Biological Traces, Laan van Ypenburg 6, 2497GB The Hague, The Netherlands.
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23
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Kucukseymen S, Arafati A, Al-Otaibi T, El-Rewaidy H, Fahmy AS, Ngo LH, Nezafat R. Noncontrast Cardiac Magnetic Resonance Imaging Predictors of Heart Failure Hospitalization in Heart Failure With Preserved Ejection Fraction. J Magn Reson Imaging 2021; 55:1812-1825. [PMID: 34559435 DOI: 10.1002/jmri.27932] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2021] [Revised: 09/11/2021] [Accepted: 09/13/2021] [Indexed: 12/28/2022] Open
Abstract
BACKGROUND Heart failure patients with preserved ejection fraction (HFpEF) are at increased risk of future hospitalization. Contrast agents are often contra-indicated in HFpEF patients due to the high prevalence of concomitant kidney disease. Therefore, the prognostic value of a noncontrast cardiac magnetic resonance imaging (MRI) for HF-hospitalization is important. PURPOSE To develop and test an explainable machine learning (ML) model to investigate incremental value of noncontrast cardiac MRI for predicting HF-hospitalization. STUDY TYPE Retrospective. POPULATION A total of 203 HFpEF patients (mean, 64 ± 12 years, 48% women) referred for cardiac MRI were randomly split into training validation (143 patients, ~70%) and test sets (60 patients, ~30%). FIELD STRENGTH A 1.5 T, balanced steady-state free precession (bSSFP) sequence. ASSESSMENT Two ML models were built based on the tree boosting technique and the eXtreme Gradient Boosting model (XGBoost): 1) basic clinical ML model using clinical and echocardiographic data and 2) cardiac MRI-based ML model that included noncontrast cardiac MRI markers in addition to the basic model. The primary end point was defined as HF-hospitalization. STATISTICAL TESTS ML tool was used for advanced statistics, and the Elastic Net method for feature selection. Area under the receiver operating characteristic (ROC) curve (AUC) was compared between models using DeLong's test. To gain insight into the ML model, the SHapley Additive exPlanations (SHAP) method was leveraged. A P-value <0.05 was considered statistically significant. RESULTS During follow-up (mean, 50 ± 39 months), 85 patients (42%) reached the end point. The cardiac MRI-based ML model using the XGBoost algorithm provided a significantly superior prediction of HF-hospitalization (AUC: 0.81) compared to the basic model (AUC: 0.64). The SHAP analysis revealed left atrium (LA) and right atrium (RV) strains as top imaging markers contributing to its performance with cutoff values of 17.5% and -15%, respectively. DATA CONCLUSIONS Using an ML model, RV and LA strains measured in noncontrast cardiac MRI provide incremental value in predicting future hospitalization in HFpEF. EVIDENCE LEVEL 3 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Selcuk Kucukseymen
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Arghavan Arafati
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Talal Al-Otaibi
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Hossam El-Rewaidy
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA.,Department of Computer Science, Technical University of Munich, Munich, Germany
| | - Ahmed S Fahmy
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Long H Ngo
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
| | - Reza Nezafat
- Department of Medicine, Cardiovascular Division, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, Massachusetts, USA
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24
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Shanthi S, Aruljyothi L, Balasundaram MB, Janakiraman A, Nirmaladevi K, Pyingkodi M. Artificial intelligence applications in different imaging modalities for corneal topography. Surv Ophthalmol 2021; 67:801-816. [PMID: 34450134 DOI: 10.1016/j.survophthal.2021.08.004] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2020] [Revised: 08/13/2021] [Accepted: 08/16/2021] [Indexed: 12/26/2022]
Abstract
Interpretation of topographical maps used to detect corneal ectasias requires a high level of expertise. Several artificial intelligence (AI) technologies have attempted to interpret topographic maps. The purpose of this study is to provide a review of AI algorithms in corneal topography from the perspectives of an eye care professional, a biomedical engineer, and a data scientist. A systematic literature review using Web of Science, Pubmed, and Google Scholar was performed from 2010 to 2020 on themes regarding imaging modalities, their parameters, purpose, and conclusions and their samples and performance related to AI in corneal topography. We provide a comprehensive summary of advances in corneal imaging and its applications in AI. Combined metrics from the Dual Scheimpflug and Placido device could be a good starting point to try AI models in corneal imaging systems. The range of area under the receiving operating curve for AI in keratoconus detection and classification was from 0.87 to 1, sensitivity was from 0.89 to 1, and specificity was from 0.82 to 1. A combination of different types of AI applications to corneal ectasia diagnosis is recommended.
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Affiliation(s)
- S Shanthi
- Kongu Engineering College, Erode, Tamil Nadu, India.
| | | | | | | | | | - M Pyingkodi
- Kongu Engineering College, Erode, Tamil Nadu, India
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25
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Rampat R, Deshmukh R, Chen X, Ting DSW, Said DG, Dua HS, Ting DSJ. Artificial Intelligence in Cornea, Refractive Surgery, and Cataract: Basic Principles, Clinical Applications, and Future Directions. Asia Pac J Ophthalmol (Phila) 2021; 10:268-281. [PMID: 34224467 PMCID: PMC7611495 DOI: 10.1097/apo.0000000000000394] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022] Open
Abstract
ABSTRACT Corneal diseases, uncorrected refractive errors, and cataract represent the major causes of blindness globally. The number of refractive surgeries, either cornea- or lens-based, is also on the rise as the demand for perfect vision continues to increase. With the recent advancement and potential promises of artificial intelligence (AI) technologies demonstrated in the realm of ophthalmology, particularly retinal diseases and glaucoma, AI researchers and clinicians are now channeling their focus toward the less explored ophthalmic areas related to the anterior segment of the eye. Conditions that rely on anterior segment imaging modalities, including slit-lamp photography, anterior segment optical coherence tomography, corneal tomography, in vivo confocal microscopy and/or optical biometers, are the most commonly explored areas. These include infectious keratitis, keratoconus, corneal grafts, ocular surface pathologies, preoperative screening before refractive surgery, intraocular lens calculation, and automated refraction, among others. In this review, we aimed to provide a comprehensive update on the utilization of AI in anterior segment diseases, with particular emphasis on the recent advancement in the past few years. In addition, we demystify some of the basic principles and terminologies related to AI, particularly machine learning and deep learning, to help improve the understanding, research and clinical implementation of these AI technologies among the ophthalmologists and vision scientists. As we march toward the era of digital health, guidelines such as CONSORT-AI, SPIRIT-AI, and STARD-AI will play crucial roles in guiding and standardizing the conduct and reporting of AI-related trials, ultimately promoting their potential for clinical translation.
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Affiliation(s)
| | - Rashmi Deshmukh
- Department of Ophthalmology, Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - Xin Chen
- School of Computer Science, University of Nottingham, Nottingham, UK
| | - Daniel S. W. Ting
- Duke-NUS Medical School, National University of Singapore, Singapore
- Singapore National Eye Centre / Singapore Eye Research Institute, Singapore
| | - Dalia G. Said
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
| | - Harminder S. Dua
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
| | - Darren S. J. Ting
- Singapore National Eye Centre / Singapore Eye Research Institute, Singapore
- Academic Ophthalmology, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Department of Ophthalmology, Queen’s Medical Centre, Nottingham, UK
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26
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Kang EM, Ryu IH, Lee G, Kim JK, Lee IS, Jeon GH, Song H, Kamiya K, Yoo TK. Development of a Web-Based Ensemble Machine Learning Application to Select the Optimal Size of Posterior Chamber Phakic Intraocular Lens. Transl Vis Sci Technol 2021; 10:5. [PMID: 34111253 PMCID: PMC8107636 DOI: 10.1167/tvst.10.6.5] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/01/2023] Open
Abstract
Purpose Selecting the optimal lens size by predicting the postoperative vault can reduce complications after implantation of an implantable collamer lens with a central-hole (ICL with KS-aquaport). We built a web-based machine learning application that incorporated clinical measurements to predict the postoperative ICL vault and select the optimal ICL size. Methods We applied the stacking ensemble technique based on eXtreme Gradient Boosting (XGBoost) and a light gradient boosting machine to pre-operative ocular data from two eye centers to predict the postoperative vault. We assigned the Korean patient data to a training (N = 2756 eyes) and internal validation (N = 693 eyes) datasets (prospective validation). Japanese patient data (N = 290 eyes) were used as an independent external dataset from different centers to validate the model. Results We developed an ensemble model that showed statistically better performance with a lower mean absolute error for ICL vault prediction (106.88 µm and 143.69 µm in the internal and external validation, respectively) than the other machine learning techniques and the classic ICL sizing methods did when applied to both validation datasets. Considering the lens size selection accuracy, our proposed method showed the best performance for both reference datasets (75.9% and 67.4% in the internal and external validation, respectively). Conclusions Applying the ensemble approach to a large dataset of patients who underwent ICL implantation resulted in a more accurate prediction of vault size and selection of the optimal ICL size. Translational Relevance We developed a web-based application for ICL sizing to facilitate the use of machine learning calculators for clinicians.
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Affiliation(s)
| | - 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
| | | | - Ga Hee Jeon
- B&VIIT Eye Center, Seoul, South Korea.,VISUWORKS, Seoul, South Korea
| | - Hojin Song
- B&VIIT Eye Center, Seoul, South Korea.,VISUWORKS, Seoul, South Korea
| | - Kazutaka Kamiya
- Visual Physiology, School of Allied Health Sciences, Kitasato University, Kanagawa, Japan
| | - Tae Keun Yoo
- B&VIIT Eye Center, Seoul, South Korea.,Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, Cheongju, South Korea
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27
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Rim TH, Lee AY, Ting DS, Teo KYC, Yang HS, Kim H, Lee G, Teo ZL, Teo Wei Jun A, Takahashi K, Yoo TK, Kim SE, Yanagi Y, Cheng CY, Kim SS, Wong TY, Cheung CMG. Computer-aided detection and abnormality score for the outer retinal layer in optical coherence tomography. Br J Ophthalmol 2021; 106:1301-1307. [PMID: 33875452 DOI: 10.1136/bjophthalmol-2020-317817] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2020] [Revised: 02/20/2021] [Accepted: 03/17/2021] [Indexed: 01/20/2023]
Abstract
BACKGROUND To develop computer-aided detection (CADe) of ORL abnormalities in the retinal pigmented epithelium, interdigitation zone and ellipsoid zone via optical coherence tomography (OCT). METHODS In this retrospective study, healthy participants with normal ORL, and patients with abnormality of ORL including choroidal neovascularisation (CNV) or retinitis pigmentosa (RP) were included. First, an automatic segmentation deep learning (DL) algorithm, CADe, was developed for the three outer retinal layers using 120 handcraft masks of ORL. This automatic segmentation algorithm generated 4000 segmentations, which included 2000 images with normal ORL and 2000 (1000 CNV and 1000 RP) images with focal or wide defects in ORL. Second, based on the automatically generated segmentation images, a binary classifier (normal vs abnormal) was developed. Results were evaluated by area under the receiver operating characteristic curve (AUC). RESULTS The DL algorithm achieved an AUC of 0.984 (95% CI 0.976 to 0.993) for individual image evaluation in the internal test set of 797 images. In addition, performance analysis of a publicly available external test set (n=968) had an AUC of 0.957 (95% CI 0.944 to 0.970) and a second clinical external test set (n=1124) had an AUC of 0.978 (95% CI 0.970 to 0.986). Moreover, the CADe highlighted well normal parts of ORL and omitted highlights in abnormal ORLs of CNV and RP. CONCLUSION The CADe can use OCT images to segment ORL and differentiate between normal ORL and abnormal ORL. The CADe classifier also performs visualisation and may aid future physician diagnosis and clinical applications.
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Affiliation(s)
- Tyler Hyungtaek Rim
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Aaron Yuntai Lee
- Department of Ophthalmology, University of Washington School of Medicine, Seattle, Washington, USA
| | - Daniel S Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Kelvin Yi Chong Teo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Hee Seung Yang
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | | | | | - Zhen Ling Teo
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Alvin Teo Wei Jun
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Kengo Takahashi
- Department of Ophthalmology, Asahikawa Medical University, Hokkaido, Japan
| | - Tea Keun Yoo
- Department of Ophthalmology, Aerospace Medical Center, Republic of Korea Air Force, Seoul, Korea (the Republic of)
| | - Sung Eun Kim
- Department of Ophthalmology, CHA Bundang Medical Center, CHA University, Seongnam, South Korea
| | - Yasuo Yanagi
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore.,Department of Ophthalmology, Asahikawa Medical University, Hokkaido, Japan
| | - Ching-Yu Cheng
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Sung Soo Kim
- Department of Ophthalmology, Institute of Vision Research, Severance Hospital, Yonsei University College of Medicine, Seoul, South Korea
| | - Tien Yin Wong
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore.,Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Chui Ming Gemmy Cheung
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore .,Ophthalmology and Visual Sciences Academic Clinical Program, Duke-NUS Medical School, Singapore
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28
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Yoo TK, Choi JY, Kim HK. Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification. Med Biol Eng Comput 2021; 59:401-415. [PMID: 33492598 PMCID: PMC7829497 DOI: 10.1007/s11517-021-02321-1] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Accepted: 01/15/2021] [Indexed: 01/16/2023]
Abstract
Deep learning (DL) has been successfully applied to the diagnosis of ophthalmic diseases. However, rare diseases are commonly neglected due to insufficient data. Here, we demonstrate that few-shot learning (FSL) using a generative adversarial network (GAN) can improve the applicability of DL in the optical coherence tomography (OCT) diagnosis of rare diseases. Four major classes with a large number of datasets and five rare disease classes with a few-shot dataset are included in this study. Before training the classifier, we constructed GAN models to generate pathological OCT images of each rare disease from normal OCT images. The Inception-v3 architecture was trained using an augmented training dataset, and the final model was validated using an independent test dataset. The synthetic images helped in the extraction of the characteristic features of each rare disease. The proposed DL model demonstrated a significant improvement in the accuracy of the OCT diagnosis of rare retinal diseases and outperformed the traditional DL models, Siamese network, and prototypical network. By increasing the accuracy of diagnosing rare retinal diseases through FSL, clinicians can avoid neglecting rare diseases with DL assistance, thereby reducing diagnosis delay and patient burden.
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Affiliation(s)
- Tae Keun Yoo
- Department of Ophthalmology, Medical Research Center, Aerospace Medical Center, Republic of Korea Air Force, 635 Danjae-ro, Sangdang-gu, Cheongju, South Korea.
| | - Joon Yul Choi
- Epilepsy Center, Neurological Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Hong Kyu Kim
- Department of Ophthalmology, Dankook University Hospital, Dankook University College of Medicine, Cheonan, South Korea
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Cabeza-Gil I, Ríos-Ruiz I, Calvo B. Customised Selection of the Haptic Design in C-Loop Intraocular Lenses Based on Deep Learning. Ann Biomed Eng 2020; 48:2988-3002. [DOI: 10.1007/s10439-020-02636-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Accepted: 09/22/2020] [Indexed: 12/28/2022]
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30
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Toward automated severe pharyngitis detection with smartphone camera using deep learning networks. Comput Biol Med 2020; 125:103980. [PMID: 32871294 PMCID: PMC7440230 DOI: 10.1016/j.compbiomed.2020.103980] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/19/2020] [Revised: 08/18/2020] [Accepted: 08/18/2020] [Indexed: 02/06/2023]
Abstract
PURPOSE Severe pharyngitis is frequently associated with inflammations caused by streptococcal pharyngitis, which can cause immune-mediated and post-infectious complications. The recent global pandemic of coronavirus disease (COVID-19) encourages the use of telemedicine for patients with respiratory symptoms. This study, therefore, purposes automated detection of severe pharyngitis using a deep learning framework with self-taken throat images. METHODS A dataset composed of two classes of 131 throat images with pharyngitis and 208 normal throat images was collected. Before the training classifier, we constructed a cycle consistency generative adversarial network (CycleGAN) to augment the training dataset. The ResNet50, Inception-v3, and MobileNet-v2 architectures were trained with transfer learning and validated using a randomly selected test dataset. The performance of the models was evaluated based on the accuracy and area under the receiver operating characteristic curve (ROC-AUC). RESULTS The CycleGAN-based synthetic images reflected the pragmatic characteristic features of pharyngitis. Using the synthetic throat images, the deep learning model demonstrated a significant improvement in the accuracy of the pharyngitis diagnosis. ResNet50 with GAN-based augmentation showed the best ROC-AUC of 0.988 for pharyngitis detection in the test dataset. In the 4-fold cross-validation using the ResNet50, the highest detection accuracy and ROC-AUC achieved were 95.3% and 0.992, respectively. CONCLUSION The deep learning model for smartphone-based pharyngitis screening allows fast identification of severe pharyngitis with a potential of the timely diagnosis of pharyngitis. In the recent pandemic of COVID-19, this framework will help patients with upper respiratory symptoms to improve convenience in diagnosis and reduce transmission.
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