Artificial intelligence and machine learning in ophthalmology: A review : Indian Journal of Ophthalmology

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Review Article

Artificial intelligence and machine learning in ophthalmology: A review

Srivastava, Ojas; Tennant, Matthew1; Grewal, Parampal1,2; Rubin, Uriel1; Seamone, Mark1,

Author Information
Indian Journal of Ophthalmology 71(1):p 11-17, January 2023. | DOI: 10.4103/ijo.IJO_1569_22
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Artificial intelligence (AI) refers to the use of computer algorithms to simulate human intelligence. The use of such intelligence has become common in the field of medicine.[1] AI allows the processing of patient information at greater levels of speed and accuracy than that of a single physician.

“Artificial intelligence” is an umbrella term for all computer software–based algorithms used to complete a given task. The two major focuses of AI in this study include machine learning (ML) and deep learning (DL). Machine learning is a subset of AI that encodes and modifies its parameters in response to data. The goal of ML is to use a computer-generated algorithm to make predictions and responses to data. Arthur Samuel, a pioneer of ML, used it to train a computer to play checkers. That program was eventually able to beat the checkers world champion.[2] Deep learning is a form of machine learning that focuses on using multiple algorithms to process input data and automatically identify patterns and structures within a given dataset. These multiple layers of algorithms form a neural network that aims to replicate the neural connectivity of the human brain. For example, a DL program can identify an image of a banana from other fruits after being trained via a set of neural networks that include images of bananas.

Ophthalmology lends itself well to AI integration as the field utilizes extensive digital imaging modalities and objective metrics such as foveal thickness or visual acuity. Electronic medical datasets combined with multi-modal digital images provide a large pool of ophthalmic patient information for analysis by AI. Moreover, the precision required when performing ophthalmic surgery may promote the application of robotic technologies to improve surgical outcomes and patient safety. Finally, the recent COVID-19 pandemic has created many hurdles for individuals seeking ophthalmic care.[345] Teleophthalmology solutions integrated with AI algorithms are able to reduce clinician review times by screening large numbers of batched image files for pathology.[6]

Herein, we provide an update on the current applications of AI in the field of ophthalmology and its various subspecialties.


Diabetic retinopathy

Screening for diabetic retinopathy (DR) is essential as it facilitates early detection and treatment, thereby preventing vision loss.[7] This is relevant in Canada as 3.7 million people have diabetic retinopathy; the incidence of DR is reported to be as high as 40% in at-risk populations, and a significant proportion of patients are not screened.[8910] DR is an optimally suited area for AI, which can help overcome screening barriers, improving access and preventing vision loss.

Early studies of AI and DR focused on lesion detection and have evolved classifying DR with a predominant focus on standard color fundus photography. In 2016, both Abràmoff et al.[11] and Gulshan et al.[12] reported algorithms using convolutional neural networks (CNN) that were able to detect referrable diabetic retinopathy (area under the curve [AUC] of 0.980 and 0.991, respectively). Subsequent studies used larger data sets demonstrated good detection of referrable diabetic retinopathy with AUCs of 0.97 and 0.94, respectively.[1314] Further studies have prospectively evaluated the performance of AI in detecting referrable DR. Heydon et al.[15] reported that EyeArt v2.1 had a 95.7% sensitivity for referrable DR.

In addition to standard fundus photography, AI detection of DR has been studied using optical coherence tomography (OCT) images, ultra-widefield (UWF) imaging, and even smartphone-captured retinal images. Intraretinal fluid identified by OCT can be identified accurately by CNN; for instance Lee et al.[16] used manually segmented macular OCT images to develop a CNN capable of detecting macular edema (with a cross-validation Dice coefficient of 0.911). UWF imaging allows visualization of up to 200° of the fundus, potentially catching additional diabetic-related peripheral disease. Nagasawa et al.[17] found high sensitivity (94.7%) and specificity (97.2%) of a CNN in detecting proliferative DR on UWF images. Similarly, Wang et al.[18] found high sensitivity (91.7%), though limited specificity (50.0%), for referrable DR using UWF images. Access and availability of imaging tools are challenges in effective DR screening. Natarajan et al.[19] reported a smartphone-based, offline AI system that had a high sensitivity for detecting referrable diabetic retinopathy.

There are a number of commercially available AI-developed DR screening platforms including IDx-DR (Iowa), which holds FDA approval, and EyeArt (California), which is designated as a European Union Class IIa medical device.[2021]

Age-related macular degeneration

Age-related macular degeneration (AMD) is a common cause of vision loss, with an estimated 196 million patients impacted globally.[22] Early detection and treatment of wet AMD can minimize vision loss. Given the burden of disease, AI could assist in mass screening of OCT and retinal photographs without in-person evaluations.

The research in this field started from ML with databases of under 1000 images to now over 490,000 images with high sensitivity and specificity rates.[14232425] Burlina et al.[24] used a database of over 130,000 images from 4613 patients to develop a DL algorithm for automated detection of AMD. Their DL system reported a 92% accuracy in identifying individuals with moderate and advanced AMD.[24] Similarly, a study by Vaghefi et al.[26] demonstrated that combining DL modalities in AMD—specifically fundus photographs, OCT, and OCT angiography scans—increased accuracy from 91% to 96% in detecting AMD compared to OCT alone.

Keenan et al.[27] recently published a paper on an AI algorithm that could accurately quantify volume of fluid in neovascular AMD patients. This has potential in monitoring response to treatment. Deep learning has also been used to quantify other key features associated with AMD including intraretinal fluid (IRF), subretinal fluid (SRF), pigment epithelial detachment (PED), ellipsoid zone loss, drusen, fibrosis, and subretinal hyperreflective material.[28] Similarly, Moraes et al.[29] published a paper on automated quantification of key features in AMD while Fu et al.[30] demonstrated that automatically captured quantitative parameters could predict visual change following treatment.

Additional applications in the retina

Moving beyond diagnosis of individual disease entities, De Fauw et al.[31] reported a deep learning architecture that identified referrable retinal disease via OCT images, achieving a performance comparable to retina subspecialists (AUC = 99.21). This system was able to identify neovascular AMD, geographic atrophy, drusen, macular edema, macular holes, central serous retinopathy, vitreomacular traction, and epiretinal membrane.

Deep learning is able to predict retinal function on microperimetry based on structural assessment of OCT in patients with Stargardt disease.[32] This may assist in assessing patients with inherited retinal disease while monitoring progression or treatment effect in clinical trials. Other AI systems are able to identify central serous retinopathy, pachychoroid vasculopathy, sickle cell disease, and macular telangiectasia.[33343536] Aside from ocular diagnosis, DL can also predict demographics including age, gender, and cardiovascular risk factors such as systolic blood pressure, smoking status, and major adverse cardiac events.[37]

Cornea and Anterior Segment

While AI has been heavily researched in the posterior segment, the application of AI in anterior segment disease and diagnostic research is now coming to the forefront of ophthalmology literature.


Using the Japan Ocular Allergy Society (JOAS) classification, Hiroki Masumoto trained a neural network to grade conjunctival hyperemia.[38] The system graded the severity of the hyperemia with a high degree of accuracy.

Trachoma is a blinding disease secondary to infection by ocular strains of Chlamydia trachomatis. Using eyelid images from a database of two clinical trials—the Niger arm of the Partnership for Rapid Elimination of Trachoma trial (PRET) and the Trachoma Amelioration in Northern Amhara (TANA) trial—machine learning was used to accurately classify trachomatis changes.[39]

Lacrimal apparatus

Lacrimal scintigraphy (LS) is an objective and reliable method of studying the lacrimal drainage system and tear flow. Park et al.[40] developed machine and deep learning algorithms using LS images to classify lacrimal duct pathology in patients with epiphora. The system showed accuracy comparable to a trained oculoplastic specialist.

Dry eye

Meibomian glands (MGs) are believed to play a critical role in ocular surface health. Dysfunction of MGs is the most frequent cause of dry eyes.[41] Meibography, or photo documentation of MGs of the eyelids with transillumination or infrared light, is a common test for the diagnosis, treatment, and management of MG dysfunction (MGD). Wang et al.[42] developed a DL approach to digitally segment MG atrophy and computing percent atrophy in meibography images, providing quantitative information of gland atrophy. The algorithm achieved a 95.6% meiboscore grading accuracy, outperforming the lead clinical investigator by 16.0% and the clinical team by 40.6%. The algorithm also achieved a 97.6% and 95.4% accuracy for eyelid and atrophy segmentations, respectively.

Stegman et al.[43] developed a ML segmentation algorithm to measure tear meniscus thickness via OCT to measure tear film quantity. The system showed reproducible results although the sample size was small.[43]


Keratoconus is a non-inflammatory corneal disorder characterized by stromal thinning and astigmatism. Kuo et al.[44] retrospectively collected corneal topographic results over time to develop a DL algorithm to detect keratoconus. The model had fair accuracy for keratoconus screening, and furthermore, it predicted subclinical keratoconus. The sensitivity and specificity of all CNN models were over 0.90, and the AUC reached 0.995 in one of the three tested models.[44]

Dos Santos et al.[45] designed and trained a neural network (CorneaNet) to segment cornea OCT images. The algorithm measured the thickness of the three main layers, namely the epithelium, Bowman’s layer, and the middle stroma, in patients with keratoconus and those with healthy eyes. All models revealed very similar performances when identifying keratoconus and had a validation accuracy ranging from 99.45% to 99.57%. Lavric et al.[46] devised the KeratoDetect, a neural network that achieved a high level of performance in detecting keratoconus from cornea topographies. With an accuracy of 99.33%, the author claimed that it could assist ophthalmologists in rapid screening of patients. Similarly, Kamiya et al.[47] evaluated the diagnostic accuracy of six colored anterior segment OCT maps: anterior elevation, anterior curvature, posterior elevation, posterior curvature, total refractive power, and pachymetry map. DL was able to identify and classify keratoconus eyes and stage of disease. Shi et al.[48] developed an automated classification system using ML and combining Scheimpflug and ultra-high resolution OCT. The system showed excellent performance (AUC = 0.93) in discriminating subclinical keratoconus from normal corneas. The author found that epithelial features assessed by OCT were the most important features when identifying keratoconus. Abdelmotaal et al.[49] was able to identify keratoconus and subclinical keratoconus via ML using color-coded corneal maps obtained by a Scheimpflug camera.


Glaucoma is the second most common cause of irreversible blindness worldwide.[50] Early detection of glaucoma has been shown to reduce vision loss.[51] Digital photography of the optic nerve is a common method to screen for glaucoma and is used effectively as part of many teleglaucoma programs.[52] Comprehensive evaluation of a glaucoma suspect might include spectral domain optical coherence tomography (SD-OCT), perimetry, tonometry, pachymetry, and gonioscopy. AI algorithms have been developed to identify optic nerve changes via optic disc photographs and SD-OCT and thereby predict glaucomatous field changes. Very little work has been done to date with DL for tonometry, pachymetry, and gonioscopy.[5354]

Glaucoma diagnosis and screening

Color fundus photography of the optic nerve is an inexpensive and available method to screen for glaucoma. ML has been utilized to improve identification of early glaucomatous changes to the optic nerve captured by photography. Computer segmentation of the optic nerve into disc, cup, and vessels to create a glaucoma score showed an AUC of 98.2% when compared to standard cup-to-disc ratio score (AUC 91.4%) on three glaucoma-related public datasets.[55] Once incorporated into teleglaucoma screening programs, such algorithms will automate detection of early glaucoma.

Simultaneous or near-simultaneous capture of optic nerve OCT scans at the time of color fundus photography has enabled the rapid development of highly accurate DL algorithms for identification of glaucomatous nerve damage. This method of training DL systems—first published by Medeiros et al.[56] in 2018 using high resolution digital images captured in the research setting—has removed the errors and biases associated with human grading of nerve damage. They showed that once trained with OCT data as truth, the DL system could discriminate glaucomatous optic nerves from healthy eyes with the area under the ROC curves of 0.944 (95% confidence interval [CI], 0.912–0.966) and 0.940 (95% CI, 0.902–0.966), respectfully (P = 0.724). This seminal work has been replicated and confirmed through publications by various groups around the world utilizing other fundus image repositories.[575859]

Machine-to-machine (M2M) deep learning algorithms trained with SD-OCT to assess monoscopic optic nerve photographs are able to identify glaucomatous optic nerve damage more accurately than glaucoma specialists.[60] This increase in accuracy suggests that DL systems will replace human review of disc photographs for glaucoma screening programs of the future.

It is possible to detect progression of glaucomatous nerve damage by fundus photography utilizing DL algorithms confirmed by OCT. Medeiros et al.[61] assessed temporally disparate disc photographs of 5529 patients over time. They utilized a DL CNN trained with OCT data from the same patients. The ROC curve area was 0.86 (95% CI, 0.83–0.88) when differentiating between progressors and non-progressors. Agitha et al.[62] showed a similar benefit of a DL model used on 1113 fundus images to achieve an accuracy of 94%, sensitivity of 85%, and specificity of 100% in the automatic diagnosis of glaucoma.

In the setting of glaucoma detection, OCT has been utilized primarily to provide an objective truth reference for glaucoma-related DL CNN training.[63] Recent work has shown that OCT DL algorithms are able to identify glaucomatous damage reliably by utilizing various datasets and various artificial intelligence algorithms.[6364] DL algorithms are also able to predict glaucomatous visual field with OCT nerve topography.[65] The sensitivity and specificity of ML classifiers to diagnose glaucoma can be improved by combining standard automated perimetry and OCT data when compared to OCT alone.[66]

Standard automated perimetry (SAP) is perhaps the most exciting area to assess with ML. The availability of long-term visual field test results, often over decades, in patients with and without glaucoma has provided extensive datasets for artificial intelligence researchers. Artificial intelligence is able to identify glaucoma four years in advance of diagnosis using original visual field data with good reliability.[67] Asaoka et al.[68] retrospectively assessed visual field data over 15 years in 51 patients with open-angle glaucoma and 87 healthy participants. Their deep feedforward neural network (FNN) showed an AUC of 92.6% (95% CI, 89.8%–95.4%) when identifying pre-perimetric glaucoma. Unsupervised ML classifiers showed a sensitivity of 82.8% and specificity of 93.1% in the identification of glaucomatous patterns by frequency doubling technology (FDT).[69] This suggests that machine learning could become an important adjunct where visual field testing is performed as part of any glaucoma screening program.

Glaucoma progression

Machine classifier algorithms are able to identify glaucoma progression by visual fields. Progression of patterns (POP) is a variational machine learning classifier that was able to identify more eyes with progression of glaucomatous optic neuropathy in glaucoma suspects and glaucoma than were identified by guided progression analysis (GPA).[70] Deep learning is also able to forecast future Humphrey visual fields in patients with glaucoma. Wen et al.[71] was able to predict the development of future visual field changes up to five years in the future using deep learning networks.


Retinopathy of prematurity

Retinopathy of prematurity (ROP) is a vasoproliferative retinal disease that is a leading cause of childhood blindness.[72] The Early Treatment for Retinopathy of Prematurity (ETROP) study has shown that screening and early intervention is critical for improving visual outcomes.[73] Improved survival of extremely premature infants has increased the prevalence of ROP, particularly in developing nations. AI can play a vital role in assisting with ROP diagnosis, thereby improving treatment outcomes. In a study by Brown et al.,[74] researchers showed that a DL algorithm trained with wide-field retinal photographs outperformed 6 out of 8 ROP experts on an independent data set of 100 images in diagnosing ROP. The algorithm was trained with a database of 5511 fundus images and demonstrated a 93% sensitivity and 94% specificity in determining ROP severity. Tong et al.[75] developed a neural network that was trained for ROP identification with 36,000 fundus images. This system achieved an accuracy of 0.903 for ROP severity classification and demonstrated comparable to or better diagnostic ability when compared to retina subspecialist. Other research have shown similar success with ROP severity classification and deep learning.[76777879] Integration of AI into an ROP screening program will likely occur in the near future.

Congenital cataracts

Pediatric cataract is one of the leading causes of juvenile blindness, with an estimated prevalence of 4.24 per 10,000 live births.

Congenital cataract guardian (CC-Guardian) is an AI agent that incorporates individualized prediction and scheduling, and intelligent telehealth follow-up computing for congenital cataracts. The system exhibits high sensitivity and specificity and has been integrated to a web-based smartphone app.[80] The intelligent agent consists of three functional modules: (i) a prediction module that identifies potential high-risk congenital cataract patients who are likely to suffer complications, (ii) a dispatching module that schedules individual follow-up based on the prediction results, and (iii) a telehealth module that makes intervention decisions in each follow-up examination. All the records were derived from routine examinations at the Childhood Cataract Program of the Chinese Ministry of Health.


In Korea, Chun et al. assessed a DL system to predict the range of refractive error in children using a smartphone photorefraction image to screen for amblyopia and compared it to a cycloplegic refraction. The DL tool showed an accuracy of 81.6%.[81]


The development and modern usage of AI in research has become a breakthrough for optimization and efficiency. With the growth of electronic medical records, healthcare providers and hospitals are able to accumulate a wealth of patient information. A common barrier to sifting through this information is the time required to appropriately review each individual item. With the advent of AI, after developing a computer-generated algorithm or suitably training an automated system to batch patient information, data collection can be completed in a fraction of the time that it would take to be done manually. Ophthalmology is a medical specialty that is conducive to retrieving these large amounts of data due to its rapid access of ophthalmic imaging and objective markers (e.g., visual acuity, intraocular pressure [IOP], retinal thickness, etc.). The Intelligent Research in Sight (IRIS) Registry is one of the largest clinical datasets that includes data about demographics, disease conditions, and visit rates in ophthalmology.[82] The Smart Eye Database stores electronic medical records of ophthalmology patients which are stratified by eye conditions.[83] Datasets such as IRIS and the Smart Eye Database allow us to appreciate subtle correlations, conduct multicenter studies, incorporate multimodal analyses, identify novel imaging patterns, and increase the power in studies, all of which may not be possible with smaller sets of data.[84] As described by Joshi et al.,[85] this large collection of medical information, or “big data,” serves as a perfect substrate for AI, ML, and DL to develop and run algorithms at a scale that would never have been possible before.


Ophthalmology is a specialty well-suited for AI integration. The extensive use of multi-modal digital imaging and diagnostic tests captured over time in all ophthalmology subspecialties provide a treasure trove of opportunities for machine learning that are now being realized. Artificial intelligence and machine learning solutions have begun the evolution from research setting to a clinical tool that will be invaluable for ophthalmologists in all clinical settings.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


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AI; anterior segment; cornea; ophthalmology; retina; pediatrics

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