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Cao D, Hu M, Zhi D, Liang J, Tan Q, Lei Q, Li M, Cheng H, Wang L, Dai W. Systematic evaluation of machine learning-enhanced trifocal IOL power selection for axial myopia cataract patients. Comput Biol Med 2024; 173:108245. [PMID: 38531253 DOI: 10.1016/j.compbiomed.2024.108245] [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: 10/15/2023] [Revised: 03/03/2024] [Accepted: 03/04/2024] [Indexed: 03/28/2024]
Abstract
PURPOSE This study aimed to evaluate and optimize intraocular lens (IOL) power selection for cataract patients with high axial myopia receiving trifocal IOLs. DESIGN A multi-center, retrospective observational case series was conducted. Patients having an axial length ≥26 mm and undergoing cataract surgery with trifocal IOL implanted were studied. METHODS Preoperative biometric and postoperative outcome data from 139 eyes were collected to train and test various machine learning (ML) models (support vector machine, linear regression, and stacking regressor) using five-fold cross-validation. The models' performance was further validated externally using data from 48 eyes enrolled from other hospitals. Performance of seven IOL calculation formulas (BUII, Kane, EVO, K6, DGS, Holladay I, and SRK/T) were examined with and without ML models. RESULTS The results of cross-validation revealed improvements across all IOL calculation formulas, especially for K6 and Holladay I. The model increased the percentage of eyes with a prediction error (PE) within ±0.50 D from 71.94% to 79.14% for K6, and from 35.25% to 51.80% for Holladay I. In external validation involving 48 patients from other centers, six out of seven formulas demonstrated a reduction in the mean absolute error (MAE). K6's PE within ±0.50 D improved from 62.50% to 77.08%, and Holladay I from 16.67% to 58.33%. CONCLUSIONS In this study, we conducted a comprehensive evaluation of seven IOL power calculation formulas in high axial myopia cases and explored the effectiveness of the Stacking Regressor model in augmenting their accuracy. Of these formulas, K6 and Holladay I exhibited the most significant improvements, suggesting that integrating ML may have varying levels of effectiveness across different formulas but holds substantial promise in improving the predictability of IOL power calculations in patients with long eyes.
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Affiliation(s)
- Danmin Cao
- Aier Institute of Digital Ophthalmology & Visual Science, Changsha Aier Eye Hospital, Changsha, China; Department of Ophthalmology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China; Aier Eye Hospital of Wuhan University, Wuhan, China
| | - Min Hu
- Aier Institute of Digital Ophthalmology & Visual Science, Changsha Aier Eye Hospital, Changsha, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
| | - Danlin Zhi
- The First Affiliated Hospital of University of South China, Hengyang, China
| | - Jianheng Liang
- Guangzhou Aier Eye Hospital, Jinan University, Guangzhou, China
| | - Qian Tan
- Aier Institute of Digital Ophthalmology & Visual Science, Changsha Aier Eye Hospital, Changsha, China
| | - Qiong Lei
- Aier Eye Hospital of Wuhan University, Wuhan, China
| | - Maoyan Li
- Aier Institute of Digital Ophthalmology & Visual Science, Changsha Aier Eye Hospital, Changsha, China
| | - Hao Cheng
- Department of Ophthalmology, The First Affiliated Hospital of Guangzhou Medical University, Guangzhou, China.
| | - Li Wang
- Cullen Eye Institute, Department of Ophthalmology, Baylor College of Medicine, Houston, TX, USA
| | - Weiwei Dai
- Aier Institute of Digital Ophthalmology & Visual Science, Changsha Aier Eye Hospital, Changsha, China.
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Savini G, Hoffer KJ, Kohnen T. IOL power formula classifications. J Cataract Refract Surg 2024; 50:105-107. [PMID: 38259130 DOI: 10.1097/j.jcrs.0000000000001378] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Affiliation(s)
- Giacomo Savini
- From the IRCCS Bietti Foundation, Rome, Italy (Savini); St. Mary's Eye Center, Santa Monica, California (Hoffer); Stein Eye Institute, Los Angeles, California (Hoffer); Department of Ophthalmology, Goethe-University, Frankfurt am Main, Germany (Kohnen)
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3
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Wawer Matos PA, Reimer RP, Rokohl AC, Caldeira L, Heindl LM, Große Hokamp N. Artificial Intelligence in Ophthalmology - Status Quo and Future Perspectives. Semin Ophthalmol 2023; 38:226-237. [PMID: 36356300 DOI: 10.1080/08820538.2022.2139625] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Artificial intelligence (AI) is an emerging technology in healthcare and holds the potential to disrupt many arms in medical care. In particular, disciplines using medical imaging modalities, including e.g. radiology but ophthalmology as well, are already confronted with a wide variety of AI implications. In ophthalmologic research, AI has demonstrated promising results limited to specific diseases and imaging tools, respectively. Yet, implementation of AI in clinical routine is not widely spread due to availability, heterogeneity in imaging techniques and AI methods. In order to describe the status quo, this narrational review provides a brief introduction to AI ("what the ophthalmologist needs to know"), followed by an overview of different AI-based applications in ophthalmology and a discussion on future challenges.Abbreviations: Age-related macular degeneration, AMD; Artificial intelligence, AI; Anterior segment OCT, AS-OCT; Coronary artery calcium score, CACS; Convolutional neural network, CNN; Deep convolutional neural network, DCNN; Diabetic retinopathy, DR; Machine learning, ML; Optical coherence tomography, OCT; Retinopathy of prematurity, ROP; Support vector machine, SVM; Thyroid-associated ophthalmopathy, TAO.
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Affiliation(s)
| | - Robert P Reimer
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Alexander C Rokohl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Liliana Caldeira
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
| | - Ludwig M Heindl
- Department of Ophthalmology, University Hospital of Cologne, Köln, Germany
| | - Nils Große Hokamp
- Department of Diagnostic and Interventional Radiology, University Hospital of Cologne, Köln, Germany
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4
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Kothari SS, Reddy JC. Recent developments in the intraocular lens formulae: An update. Semin Ophthalmol 2023; 38:143-150. [PMID: 35776680 DOI: 10.1080/08820538.2022.2094712] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
BACKGROUND The precision of refractive outcomes after uneventful cataract surgery largely depends on the biometry and intraocular lens (IOL) formula used for selecting the IOL. To improve the accuracy of post-op refractive outcomes, several new IOL power calculation formulae have come up. This review would aim to summarise the differences among the new formulae in their performance among normal and variable ocular biometry conditions like short and long axial lengths. METHODS A literature review was performed by searching the PubMed and Cochrane databases from 2016 to 2021, identified 483 articles, of which 51 were included in the review. RESULTS We identified 15 new IOL formulas (including updates on older formulas) of which, only 8 newer formulas (BUII, Hill-RBF 2.0, Kane, Pearl DGS, LSF AI, Naesar 2, EVO 2.0 and VRF) met the eligibility criteria. They were compared according to the reported median absolute error, mean absolute error and percentage of eyes within 0.5D. CONCLUSION The Kane formula and Barrett Universal-II formula performed better than other formulas over the entire axial length (AL) spectrum. In the long eye (AL > 26.0 mm) sub-group, the Kane formula was the most accurate, while in the short eye (AL < 22.0 mm) sub-group, both Kane and EVO 2.0 formulas fared better than other formulas.
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Affiliation(s)
- Sarthak S Kothari
- Academy of Eye Care Education, L V Prasad Eye Institute, Hyderabad, India.,Cataract & Refractive Surgery Services, L V Prasad Eye Institute, Hyderabad, India
| | - Jagadesh C Reddy
- Cataract & Refractive Surgery Services, L V Prasad Eye Institute, Hyderabad, India
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Ting DSJ, Deshmukh R, Ting DSW, Ang M. Big data in corneal diseases and cataract: Current applications and future directions. Front Big Data 2023; 6:1017420. [PMID: 36818823 PMCID: PMC9929069 DOI: 10.3389/fdata.2023.1017420] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2022] [Accepted: 01/16/2023] [Indexed: 02/04/2023] Open
Abstract
The accelerated growth in electronic health records (EHR), Internet-of-Things, mHealth, telemedicine, and artificial intelligence (AI) in the recent years have significantly fuelled the interest and development in big data research. Big data refer to complex datasets that are characterized by the attributes of "5 Vs"-variety, volume, velocity, veracity, and value. Big data analytics research has so far benefitted many fields of medicine, including ophthalmology. The availability of these big data not only allow for comprehensive and timely examinations of the epidemiology, trends, characteristics, outcomes, and prognostic factors of many diseases, but also enable the development of highly accurate AI algorithms in diagnosing a wide range of medical diseases as well as discovering new patterns or associations of diseases that are previously unknown to clinicians and researchers. Within the field of ophthalmology, there is a rapidly expanding pool of large clinical registries, epidemiological studies, omics studies, and biobanks through which big data can be accessed. National corneal transplant registries, genome-wide association studies, national cataract databases, and large ophthalmology-related EHR-based registries (e.g., AAO IRIS Registry) are some of the key resources. In this review, we aim to provide a succinct overview of the availability and clinical applicability of big data in ophthalmology, particularly from the perspective of corneal diseases and cataract, the synergistic potential of big data, AI technologies, internet of things, mHealth, and wearable smart devices, and the potential barriers for realizing the clinical and research potential of big data in this field.
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Affiliation(s)
- Darren S. J. Ting
- Academic Unit of Ophthalmology, Institute of Inflammation and Ageing, University of Birmingham, Birmingham, United Kingdom,Birmingham and Midland Eye Centre, Birmingham, United Kingdom,Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, United Kingdom,*Correspondence: Darren S. J. Ting ✉
| | - Rashmi Deshmukh
- Department of Cornea and Refractive Surgery, LV Prasad Eye Institute, Hyderabad, India
| | - Daniel S. W. Ting
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
| | - Marcus Ang
- Singapore National Eye Centre, Singapore Eye Research Institute, Singapore, Singapore,Department of Ophthalmology and Visual Sciences, Duke-National University of Singapore (NUS) Medical School, Singapore, Singapore
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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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Topical NSAIDs and Oral Acetazolamide for Macular Edema after Uncomplicated Phacoemulsification: Outcome and Predictors of Non-Response. J Clin Med 2022; 11:jcm11195537. [PMID: 36233408 PMCID: PMC9572828 DOI: 10.3390/jcm11195537] [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: 08/02/2022] [Revised: 09/14/2022] [Accepted: 09/19/2022] [Indexed: 11/24/2022] Open
Abstract
Purpose: To investigate the effectiveness of nonsteroidal anti-inflammatory eye drops (NSAIDs) combined with oral acetazolamide for postoperative macular edema (PME) after uncomplicated phacoemulsification (PE) and identify predictors of non-response. Methods: We analyzed data of uncomplicated PE and identified eyes with PME. First-line therapy included topical NSAIDs combined with oral acetazolamide. In the case of non-response, triamcinolone was administered subtenonally. Outcome measures included best-corrected visual acuity (BCVA) and central macular thickness (CMT). Results: 94 eyes out of 9750 uncomplicated PE developed PME, of which 60 eyes were included. Follow-ups occurred 6.4 ± 1.8, 12.5 ± 3.7 and 18.6 ± 6.0 weeks after diagnosis. BCVA and CMT improved significantly in all follow-ups. In total, 40 eyes showed a response to first-line therapy at the first follow-up (G1). The remaining 20 eyes showed no response and required subtenon triamcinolone (G2), of which 11 eyes showed complete regression at the second follow-up and 4 eyes at third follow-up. A further 5 eyes showed no response and required intravitreal injection. Multivariate linear regression model showed that Diabetes mellitus (DM) and increased cumulative dissipated energy (CDE) are predictors of non-response. Conclusion: Topical NSAIDs with acetazolamide resulted in complete regression of PME in 67% of all cases. DM and increased CDE might be considered as predictors of non-response to this treatment.
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Gutierrez L, Lim JS, Foo LL, Ng WY, Yip M, Lim GYS, Wong MHY, Fong A, Rosman M, Mehta JS, Lin H, Ting DSJ, Ting DSW. Application of artificial intelligence in cataract management: current and future directions. EYE AND VISION (LONDON, ENGLAND) 2022; 9:3. [PMID: 34996524 PMCID: PMC8739505 DOI: 10.1186/s40662-021-00273-z] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/05/2021] [Accepted: 12/07/2021] [Indexed: 02/10/2023]
Abstract
The rise of artificial intelligence (AI) has brought breakthroughs in many areas of medicine. In ophthalmology, AI has delivered robust results in the screening and detection of diabetic retinopathy, age-related macular degeneration, glaucoma, and retinopathy of prematurity. Cataract management is another field that can benefit from greater AI application. Cataract is the leading cause of reversible visual impairment with a rising global clinical burden. Improved diagnosis, monitoring, and surgical management are necessary to address this challenge. In addition, patients in large developing countries often suffer from limited access to tertiary care, a problem further exacerbated by the ongoing COVID-19 pandemic. AI on the other hand, can help transform cataract management by improving automation, efficacy and overcoming geographical barriers. First, AI can be applied as a telediagnostic platform to screen and diagnose patients with cataract using slit-lamp and fundus photographs. This utilizes a deep-learning, convolutional neural network (CNN) to detect and classify referable cataracts appropriately. Second, some of the latest intraocular lens formulas have used AI to enhance prediction accuracy, achieving superior postoperative refractive results compared to traditional formulas. Third, AI can be used to augment cataract surgical skill training by identifying different phases of cataract surgery on video and to optimize operating theater workflows by accurately predicting the duration of surgical procedures. Fourth, some AI CNN models are able to effectively predict the progression of posterior capsule opacification and eventual need for YAG laser capsulotomy. These advances in AI could transform cataract management and enable delivery of efficient ophthalmic services. The key challenges include ethical management of data, ensuring data security and privacy, demonstrating clinically acceptable performance, improving the generalizability of AI models across heterogeneous populations, and improving the trust of end-users.
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Affiliation(s)
| | - Jane Sujuan Lim
- Singapore Eye Research Institute, Singapore, Singapore.,Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Li Lian Foo
- Singapore Eye Research Institute, Singapore, Singapore.,Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Wei Yan Ng
- Singapore Eye Research Institute, Singapore, Singapore.,Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Michelle Yip
- Singapore Eye Research Institute, Singapore, Singapore.,Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | | | - Melissa Hsing Yi Wong
- Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Allan Fong
- Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Mohamad Rosman
- Singapore Eye Research Institute, Singapore, Singapore.,Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Jodhbir Singth Mehta
- Singapore Eye Research Institute, Singapore, Singapore.,Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore
| | - Haotian Lin
- Zhongshan Ophthalmic Center, Sun Yet Sen University, Guangzhou, China
| | - Darren Shu Jeng Ting
- Academic Ophthalmology, School of Medicine, University of Nottingham, Nottingham, UK
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore, Singapore. .,Singapore National Eye Center, 11 Third Hospital Avenue, Singapore, 168751, Singapore.
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Mori Y, Yamauchi T, Tokuda S, Minami K, Tabuchi H, Miyata K. Machine learning adaptation of intraocular lens power calculation for a patient group. EYE AND VISION 2021; 8:42. [PMID: 34775991 PMCID: PMC8591948 DOI: 10.1186/s40662-021-00265-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/23/2021] [Accepted: 10/21/2021] [Indexed: 11/10/2022]
Abstract
Abstract
Background
To examine the effectiveness of the use of machine learning for adapting an intraocular lens (IOL) power calculation for a patient group.
Methods
In this retrospective study, the clinical records of 1,611 eyes of 1,169 Japanese patients who received a single model of monofocal IOL (SN60WF, Alcon) at Miyata Eye Hospital were reviewed and analyzed. Using biometric metrics and postoperative refractions of 1211 eyes of 769 patients, constants of the SRK/T and Haigis formulas were optimized. The SRK/T formula was adapted using a support vector regressor. Prediction errors in the use of adapted formulas as well as the SRK/T, Haigis, Hill-RBF and Barrett Universal II formulas were evaluated with data from 395 eyes of 395 distinct patients. Mean prediction errors, median absolute errors, and percentages of eyes within ± 0.25 D, ± 0.50 D, and ± 1.00 D, and over + 0.50 D of errors were compared among formulas.
Results
The mean prediction errors in the use of the SRT/K and adapted formulas were smaller than the use of other formulas (P < 0.001). In the absolute errors, the Hill-RBF and adapted methods were better than others. The performance of the Barrett Universal II was not better than the others for the patient group. There were the least eyes with hyperopic refractive errors (16.5%) in the use of the adapted formula.
Conclusions
Adapting IOL power calculations using machine learning technology with data from a particular patient group was effective and promising.
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Tognetto D, Giglio R, Vinciguerra AL, Milan S, Rejdak R, Rejdak M, Zaluska-Ogryzek K, Zweifel S, Toro MD. Artificial intelligence applications and cataract management: A systematic review. Surv Ophthalmol 2021; 67:817-829. [PMID: 34606818 DOI: 10.1016/j.survophthal.2021.09.004] [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: 05/09/2021] [Revised: 09/27/2021] [Accepted: 09/27/2021] [Indexed: 11/26/2022]
Abstract
Artificial intelligence (AI)-based applications exhibit the potential to improve the quality and efficiency of patient care in different fields, including cataract management. A systematic review of the different applications of AI-based software on all aspects of a cataract patient's management, from diagnosis to follow-up, was carried out in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. All selected articles were analyzed to assess the level of evidence according to the Oxford Centre for Evidence-Based Medicine 2011 guidelines, and the quality of evidence according to the Grading of Recommendations Assessment, Development and Evaluation system. Of the articles analyzed, 49 met the inclusion criteria. No data synthesis was possible for the heterogeneity of available data and the design of the available studies. The AI-driven diagnosis seemed to be comparable and, in selected cases, to even exceed the accuracy of experienced clinicians in classifying disease, supporting the operating room scheduling, and intraoperative and postoperative management of complications. Considering the heterogeneity of data analyzed, however, further randomized controlled trials to assess the efficacy and safety of AI application in the management of cataract should be highly warranted.
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Affiliation(s)
- Daniele Tognetto
- Eye Clinic, Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Rosa Giglio
- Eye Clinic, Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy.
| | - Alex Lucia Vinciguerra
- Eye Clinic, Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Serena Milan
- Eye Clinic, Department of Medicine, Surgery and Health Sciences, University of Trieste, Trieste, Italy
| | - Robert Rejdak
- Chair and Department of General and Pediatric Ophthalmology, Medical University of Lublin, Lublin, Poland
| | | | | | | | - Mario Damiano Toro
- Department of Ophthalmology, University of Zurich, Zurich; Department of Medical Sciences, Collegium Medicum, Cardinal Stefan Wyszyński University, Warsaw, Poland
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Abstract
PURPOSE OF REVIEW Artificial intelligence and deep learning have become important tools in extracting data from ophthalmic surgery to evaluate, teach, and aid the surgeon in all phases of surgical management. The purpose of this review is to highlight the ever-increasing intersection of computer vision, machine learning, and ophthalmic microsurgery. RECENT FINDINGS Deep learning algorithms are being applied to help evaluate and teach surgical trainees. Artificial intelligence tools are improving real-time surgical instrument tracking, phase segmentation, as well as enhancing the safety of robotic-assisted vitreoretinal surgery. SUMMARY Similar to strides appreciated in ophthalmic medical disease, artificial intelligence will continue to become an important part of surgical management of ocular conditions. Machine learning applications will help push the boundaries of what surgeons can accomplish to improve patient outcomes.
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Affiliation(s)
- Kapil Mishra
- Department of Ophthalmology, Byers Eye Institute at Stanford, Stanford University School of Medicine, Palo Alto, California, USA
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