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GUEST EDITORIAL
Year : 2020  |  Volume : 68  |  Issue : 12  |  Page : 2650-2651

Artificial intelligence in laser refractive surgery – Potential and promise!


Narayana Nethralaya Eye Institute, 121/C, Chord Road, Rajajinagar, Bangalore - 560 010, Karnataka, India

Date of Web Publication23-Nov-2020

Correspondence Address:
Chaitra Jayadev
Narayana Nethralaya Eye Institute, 121/C, Chord Road, Rajajinagar, Bangalore - 560 010, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/ijo.IJO_3304_20

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How to cite this article:
Jayadev C, Shetty R. Artificial intelligence in laser refractive surgery – Potential and promise!. Indian J Ophthalmol 2020;68:2650-1

How to cite this URL:
Jayadev C, Shetty R. Artificial intelligence in laser refractive surgery – Potential and promise!. Indian J Ophthalmol [serial online] 2020 [cited 2024 Mar 28];68:2650-1. Available from: https://journals.lww.com/ijo/pages/default.aspx/text.asp?2020/68/12/2650/301273



Laser refractive surgery is about precision. Whether it is screening or surgery, there is no scope for errors, owing to the age and profile of patients undergoing these procedures.[1],[2] A majority of them are for cosmesis, intolerance to contact lenses, or professional reasons, including sportsmen. Case selection and type of technique is, therefore, paramount to ensure optimal results. At the time of investigations and scans, there can be subjective variations in the interpretation of topographic maps and subtle differences between the devices used.[3] Some organizations screen potential employees or recruits for previous refractive surgery.[4] Evidence of these procedures in corneas donated to eye banks for transplantation of donor tissue may also be a requirement.[5] Hence, an objective assessment across demographic variations and instrumentation would add to the success of planning and outcome evaluation.

Artificial intelligence (AI), based on machine learning and deep learning, is gaining popularity and acceptance in medicine and healthcare with its ability to perform much better than human beings, especially in image recognition and analysis.[6] While AI in ophthalmology has been used for diabetic retinopathy, age-related macular degeneration, glaucoma and cataract, and its application in corneal conditions is being explored.[7],[8],[9],[10],[11],[12] Different machine learning algorithms have been used to identify eyes with preclinical or subclinical keratoconus.[13],[14],[15] Based on the experience from these algorithms, it is possible to screen for cornea ectasias with good accuracy before refractive surgery and to identify those in whom the surgery would be a contraindication.[16] All preoperative data from 10,561 eyes were combined to form a model to predict suitability for refractive surgery with an accuracy of 93.4%, including laser-assisted epithelial keratomileusis (LASIK) and small incision lenticular extraction (SMILE).[17]

Another possible avenue for AI includes predicting laser refractive surgery outcomes and enhancing the accuracy for SMILE outcomes, with the current algorithms' performance being comparable to that of an experienced surgeon for safety, efficacy, and predictability.[18],[19] It is now also possible to identify patients with a risk for post-surgical ectasia, especially post LASIK.[20] Based on the preoperative Pentacam data of 2980 stable LASIK eyes, a machine learning algorithm was able to detect 71 eyes with ectasia susceptibility and 182 eyes with clinical keratoconus.[21] Another model with the Orbscan could detect post LASIK ectasia with 93% sensitivity and 92% specificity.[22] With better technology and surgical options available, the number of patients opting for refractive error correction is increasing. They will eventually need to undergo cataract surgery and calculation of the corneal power is paramount for intraocular lens (IOL) selection. AI can be used to prevent erroneous calculations and predict accurate IOL powers to reduce the likelihood of residual refractive errors.[12]

In India and neighboring Asian countries, there is a growing prevalence of myopia with more patients seeking refractive error correction.[23] Automated and objective categorization of scan images to provide salient corneal information will go a long way to support clinical decision making and lead to better individual risk assessment and outcomes. With the ongoing pandemic, there is a potential for telemedicine and AI applications in inaccessible regions. Similar to those in other fields, challenges of AI in ophthalmology are validation and applicability of AI models, implementation barriers, standardization of data sets, and ethical issues. Some models are less predictable for eyes with high myopia (>−7D) and astigmatism (>2D).[24] Nonetheless, ophthalmology has the advantage of image-based diagnosis and AI can facilitate quantification of disease severity, surgical planning, and longitudinal monitoring of treatment response. AI, as an industry, is growing in leaps and bounds and will ultimately be a partner for an improved and accurate refractive surgical practice.[25]



 
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Kim TI, Alió Del Barrio JL, Wilkins M, Cochener B, Ang M. Refractive surgery. Lancet 2019;393:2085-98.  Back to cited text no. 1
    
2.
Ang M, Gatinel D, Reinstein DZ, Mertens E, Alió Del Barrio JL, Alió JL. Refractive surgery beyond 2020. Eye (Lond) 2020. doi: 10.1038/s41433-020-1096-5.  Back to cited text no. 2
    
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Schallhorn SC, Reid JL, Kaupp SE, Blanton CL, Zoback L, Goforth H, Jr., et al. Topographic detection of photorefractive keratectomy. Ophthalmology 1998;105:507-16.  Back to cited text no. 3
    
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Smolek MK and Klyce SD. Screening of prior refractive surgery by a wavelet-based neural network. J Cataract Refract Surg 2001;27:1926-31.  Back to cited text no. 4
    
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Lim-Bon-Siong R, Williams JM, Samapunphong S, Chuck RS, Pepose JS. Screening of myopic photorefractive keratectomy in eye bank eyes by computerized videokeratography. Arch Ophthalmol 1998;116:617-23.  Back to cited text no. 5
    
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Lu W, Tong Y, Yu Y, Xing Y, Chen C, Shen Y. Applications of Artificial Intelligence in Ophthalmology: General Overview. J Ophthalmol 2018;2018:5278196.  Back to cited text no. 6
    
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Wong TY, Bressler NM. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA 2016;316:2366-7.  Back to cited text no. 7
    
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Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and Validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 2016;316:2402-10.  Back to cited text no. 8
    
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Chakravarthy U, Goldenberg D, Young G, Havilio M, Rafaeli O, Benyamini G, et al. Automated identification of lesion activity in neovascular age-related macular degeneration. Ophthalmology 2016;123:1731-6.  Back to cited text no. 9
    
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Kim SJ, Cho KJ, Oh S. Development of machine learning models for diagnosis of glaucoma. PLoS One 2017;12:e0177726.  Back to cited text no. 10
    
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Gao X, Lin S, Wong TY. Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans Biomed Eng 2015;62:2693-701.  Back to cited text no. 11
    
12.
Koprowski R, Lanza M, Irregolare C. Corneal power evaluation after myopic corneal refractive surgery using artificial neural networks. Biomed Eng Online 2016;15:121.  Back to cited text no. 12
    
13.
Shi C, Wang M, Zhu T, Zhang Y, Ye Y, Jiang J et al. Machine learning helps improve diagnostic ability of subclinical keratoconus using Scheimpflug and OCT imaging modalities. Eye Vis (Lond) 2020;7:48.  Back to cited text no. 13
    
14.
Kovács I, Miháltz K, Kránitz K, Juhász É, Takács Á, Dienes L, et al. Accuracy of machine learning classifiers using bilateral data from a Scheimpflug camera for identifying eyes with preclinical signs of keratoconus. J Cataract Refract Surg 2016;42:275-83.  Back to cited text no. 14
    
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Cao K, Verspoor K, Sahebjada S, Baird PN. Evaluating the performance of various machine learning algorithms to detect subclinical keratoconus. Transl Vis Sci Technol 2020;9:24.  Back to cited text no. 15
    
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Ruiz Hidalgo I, Rodriguez P, Rozema JJ, S ND, Zakaria N, Tassignon MJ, et al. Evaluation of a machine-learning classifier for keratoconus detection based on scheimpflug tomography. Cornea 2016;35:827-32.  Back to cited text no. 16
    
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Yoo TK, Ryu IH, Lee G, Kim Y, Kim JK, Lee IS, et al. Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. NPJ Digit Med 2019;2:59.  Back to cited text no. 17
    
18.
Achiron A, Gur Z, Aviv U, Hilely A, Mimouni M, Karmona L, et al. Predicting refractive surgery outcome: Machine learning approach with big data. J Refract Surg 2017;33:592-7.  Back to cited text no. 18
    
19.
Cui T, Wang Y, Ji S, Li Y, Hao W, Zou H, et al. Applying machine learning techniques in nomogram prediction and analysis for SMILE treatment. Am J Ophthalmol 2020;210:71-7.  Back to cited text no. 19
    
20.
Ting DSJ, Foo VH, Yang LWY, Sia JT, Ang M, Lin H, et al. Artificial intelligence for anterior segment diseases: Emerging applications in ophthalmology. Br J Ophthalmol 2020. doi: 10.1136/bjophthalmol-2019-315651.  Back to cited text no. 20
    
21.
Lopes BT, Ramos IC, Salomão MQ, Guerra FP, Schallhorn SC, Schallhorn JM, et al. Enhanced tomographic assessment to detect corneal ectasia based on artificial intelligence. Am J Ophthalmol 2018;195:223-32.  Back to cited text no. 21
    
22.
Saad A, Gatinel D. Combining placido and corneal wavefront data for the detection of forme fruste keratoconus. J Refract Surg 2016;32:510-6.  Back to cited text no. 22
    
23.
Redd TK, Campbell JP, Chiang MF. Artificial intelligence for refractive surgery screening: Finding the balance between myopia and hype-ropia. JAMA Ophthalmol 2020;138:526-7.  Back to cited text no. 23
    
24.
Wu X, Liu L, Zhao L, Guo C, Li R, Wang T, et al. Application of artificial intelligence in anterior segment ophthalmic diseases: Diversity and standardization. Ann Transl Med 2020;8:714.  Back to cited text no. 24
    
25.
Siddiqui AA, Ladas JG, Lee JK. Artificial intelligence in cornea, refractive, and cataract surgery. Curr Opin Ophthalmol 2020;31:253-60.  Back to cited text no. 25
    

 
  Authors Top


Dr. Chaitra Jayadev, DOMS, FVR, FICO, PhD
Dr. Chaitra Jayadev is currently senior consultant at Narayana Nethralaya Eye Institute, Bangalore. In addition to her clinical role, she is actively involved in editorial management, scientific publications and translational research at her Institute and beyond. She has served the Indian Journal of Ophthalmology from 2006-17 in various capacities and is currently the Joint Secretary of AIOS; an Executive Committee Member of the Vitreo Retinal Surgeons of India Society; Treasurer of the Karnataka Ophthalmic Society; and an Executive Committee Member of the Bangalore Ophthalmic Society.
With a flair for research and scientific writing, Dr. Jayadev has over 100 PubMed indexed manuscripts. She has contributed to internationally renowned Myron Yanoff's Text Book on Advances in Ophthalmology. She completed her PhD from the Maastricht University, Netherlands on Pediatric Retinal Imaging. Dr. Chaitra did a program on Healthcare Leadership from the Harvard Medical School in 2019. She is an invited speaker at various national and international platforms and is the recipient of the prestigious JM Pahwa Award for the best paper at the annual conference of VRSI.



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