The document outlines the role of artificial intelligence (AI) in ophthalmology, detailing its history, definitions, and applications in various eye-related conditions and surgeries. It distinguishes between weak and strong AI, discusses various AI techniques such as machine learning in areas like diabetic retinopathy screening and cataract surgery, and highlights recent advancements and FDA-approved AI algorithms for eye health. The document emphasizes the potential of AI to enhance diagnostic accuracy and streamline ophthalmic practices.

1. ARTIFICIAL
INTELLIGENCE IN
OPHTHALMOLOGY
PRESENTER: DR. HARSHIKA
1

2. STAGES OF INDUSTRIAL REVOLUTION
2

3. WHAT IS ARTIFICIAL
INTELLIGENCE?
• Artificial intelligence (AI) - Technology that enables computers and machines to
simulate human intelligence and problem-solving capabilities.
• Examples in daily life-
3
Taxi booking app
Voice assistance
Entertainment streaming app
Image recognition through google lens
Navigation and Travel

4. HISTORY OF AI
• Term “artificial intelligence” (AI): coined on August 31, 1955
• John McCarthy, Marvin L. Minsky, Nathaniel Rochester, and Claude E. Shannon
submitted “A Proposal for the Dartmouth Summer Research Project on Artificial
Intelligence.” *
• First scientific step towards intelligent machines: Alan Turing (1950) - famous Turing
test
• This involves an interview with open-ended questions to determine whether the
intelligence of the interviewee is human or artificial. If this distinction can no
longer be made, within certain predefined margins, true machine intelligence
has been accomplished.
Benet D, Pellicer-Valero OJ. Artificial intelligence: the unstoppable revolution in ophthalmology. Surv Ophthalmol. 2022 Jan-Feb;67(1):252-270.
doi: 10.1016/j.survophthal.2021.03.003. Epub 2021 Mar 16. PMID: 33741420
Mitchell M. Artificial intelligence: a guide for thinking humans. Penguin UK; 2019*
Topol E. Deep medicine: how artificial intelligence can make healthcare human again. New York: Basic Books; 2019
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: Past, present and future. Stroke Vasc Neurol 2017;2;2:230-43**
4

5. 5
Tong et al; Application of machine learning in ophthalmic imaging modalities; Eye and Vision;
2020

6. TYPES OF AI: WEAK AI VS STRONG AI
• Weak/ narrow Al: Trained and focused to perform specific tasks. Most of the Al that
surrounds us today are weak AI.
Examples: Apple's Siri, Amazon's Alexa
• Strong Al/ Artificial super intelligence (ASD): Is a theoretical form of Al where a
machine would have an intelligence equal to humans. Strong Al is still entirely
theoretical with no practical examples in use today.
Examples: Science fiction, such as HAL, the superhuman and rogue computer
assistant in 2001: A Space Odyssey.
6

7. ARTIFICIAL INTELLIGENCE TECHNIQUES
Tong et al; Application of machine learning in ophthalmic imaging modalities; Eye and Vision; 2020
7

8. AI IN MEDICINE
Detection of lung cancer
Lymph node metastases secondary to breast cancer
Real-time detection of colonoscopic polyps and adenoma
Detection of cardiovascular risk factor
8

9. APPLICATIONS OF AI IN OPHTHALMOLOGY
• ANTERIOR SEGMENT :
• Keratoconus and other Anterior segment disorders
• Amblyopia,squint surgeries
• Refractive surgeries
• Cataract surgery and IOL power calculations
• POSTERIOR SEGMENT:
• Diabetic Retinopathy screening
• Age-Related Macular Degeneration
• Glaucoma screening and diagnosis
• Retinopathy of prematurity screening
9

10. #APPLICATIONS OF AI IN ANTERIOR
SEGMENT DISEASES
Wu X, Liu L, Zhao L, Guo C, Li R, Wang T, Yang X, et al. Application of artificial intelligence in anterior segment ophthalmic diseases: diversity and standardization. Ann Transl
Med 2020;8:714
10

11. #AI IN KERATOCONUS SCREENING
Preclinical detection of keratoconus-
• Bilateral data were effective in
discriminating clinically normal fellow
eyes of patients with keratoconus from
normal eyes.
• Scheimpflug camera was used.
• The most sensitive parameters to date
for early KC identification -
combination of videokeratography,
wavefront analysis, and optical
coherence tomography.
.
11

12. #AI IN DETECTION OF AMBLYOPIA
Euvision Tab Stereovision tests (ETS) - Detecting
amblyopia, amblyogenic and non-amblyogenic
conditions in children.
Do not rely on measuring stereoacuity
Can be either static or dynamic
Can include uncorrelated noise
Use artificial intelligence technology
12
1st – universally visible
2nd – visible with red green glass

13. #AI IN SQUINT SURGERY
Strabismus affects about 4% of the population.
Surgical strategies are complex, demanding both theoretical knowledge and experience from the
surgeon.
Support Vector Regression (SVR) can help the physician with decision related to horizontal
strabismus surgeries.
The results are promising and prove the feasibility of the use of Support Vector Regression
Limitation of SVR- Not used in vertical and oblique muscle surgery
13

14. #AI IN REFRACTIVE SURGERY
• Refractive surgery has undergone rapid advancements in the last decades with
good visual effects and long-term safety.
• Several refractive surgery types available both in the cornea and lens.
• The following surgeries with aid of laser are in vogue:
• 1)PRK, 2)LASIK 3)SMILE (Small incision lenticule extraction)
• more clinical data related to the surgery are being generated and more accuracy for
the preoperative assessment and screening are required.
• Therefore, artificial intelligence assisting in diagnosis and surgery procedures may
be needed.
Kim TI, Alio Del Barrio JL, Wilkins M, Cochener B, Ang M. Refractive surgery. Lancet. 2019;393(10185):2085–98. https://doi.org/10.1016/ S0140-6736( 18)33209-4.
14

15. DEEP LEARNING IN PRE-OP
ASSESSMENT IN REFRACTIVE
SURGERY
A. Grzybowski (ed.), Artificial Intelligence in Ophthalmology, © Springer Nature Switzerland AG 2021 https://doi.org/10.1007/978-3-030-78601-
4_17
15

16. Yoo TK, Ryu IH, Lee G, et al. Adopting machine learning to automatically identify candidate patients for corneal refractive surgery. NPJ Digit Med
2019;2:59.
16

17. #AI BASED IOL
• SAV-IOL (Swiss Advanced Vision) - Mimicking the human lens, the autofocus
system sends signals that trigger micro-pumps to alter the curvature of the optical
membrane through liquid displacement - in real time; in fact, the process occurs at
0.2 seconds, a speed equivalent to the accommodation of a healthy human eye.
17

18. ALGORITHM FOR IOL POWER CALCULATION
• Ladas Super Formula - Combinations of existing formulas (Hoffer Q.
Holladay-1, Holladay-l with Koch adjust - ment, Haigis, and SRK/T formulas)
and plotted into a 5-D surface
• Hill-Radial Basis Function (RBF) method - Based on Haag-Streit LENSTAR
optical biometer
• Kane formula- Cloud-based formula
Hill W. Hill-RBF Formula 3.0 [Internet]. Hill-RBF Calculator Version 3.0. https://rbfcalculator.com/. Accessed 3 Feb 2021.
Ladas JG, Siddiqui AA, Devgan U, Jun AS. A 3-D “Super Surface” combining modern intraocular lens formulas to generate a “Super Formula” and maximize accuracy.
JAMA Ophthalmol. 2015;133(12):1431–6.
Gatinel D, Debellemanière G, Saad A, Dubois M, Rampat R. Determining the theoretical effective lens position of thick intraocular lenses for machine learning based IOL
power calculation and simulation. Transl Vis Sci Technol. 2021.
18

19. BREAK THE LIMITATIONS
19
New Al-derived formula may be
explored to improve the
accuracy of IOL power
estimation in post refractive
surgery patient.
Though storage of large data is
a big challange always,
hospital's electronic medical
records coupled with seamless
connection to cloud storage
may offer new opportunities for
development of algorithms.

20. FUTURE OF AI IN CATARACT SCREENING
• Hand-held retinal cameras,or slit lamp adapters attached to smartphones, can
potentially provide better outreach for cataract screening,especially in rural or less-
resourced areas.
• DR screening programs with automated cataract assessment based on retinal
photos will reduce the additional cost.
• Recent work has demonstrated Al's capability in recognizing different phases of
cataract surgery. This system may be particularly useful when training
ophthalmology residents.
20

21. #CATARACT
• Cataract is the leading cause of visual impairment worldwide, accounting for 62.5
million cases of visual impairment and blindness globally
Screening:
• Cataracts are clinically diagnosed using slit-lamp biomicroscopy, and graded based
on established clinical scales such as the Lens Opacities Classification System III
Limitations:
• Manual" process requires clinical expertise
• Shortage of trained ophthalmologists
• Grading results are compromised by inter-examiner variability
21

22. ALGORITHM BASED ON SLIT LAMP
PHOTO
Active Shape Model (ASM)-
• Identify the location of the
crystalline lens and its
nucleus on 5820 slit lamp
photographs
• The ASM achieved 95%
success rate in correctly
identifying the location of
lens.
Cloud based AI- CC-Cruise-
• Diagnose and grade
paediatric cataracts
22

23. 23

24. ALGORITHM BASED ON SLIT LAMP PHOTO
Residual neural network (Res Net) - 3-step sequential Al algorithm for the diagnosis
and referral of cataracts
• Al system - first differentiate slit lamp photographs between mydriatic and
nonmydriatic images, and between optical section and diffuse slit-lamp illumination.
• The images would be categorized as either normal (ie, no cataract), cataractous, or
postoperative IOL
• Type and severity of the cataract/posterior capsular opacification -evaluated based
on the Lens Opacities Classification System Il scale
24

25. ALGORITHIM BASED ON FUNDUS
PHOTOGRAPHY
ResNet-18 and ResNet-50 - visibility" of fundus images were used to denote four
classes of cataract severity (noncataract, mild, moderate, and severe cataract)
25

26. WHAT TO LOOK FOR IN SELECTING ALGORITM
• Number of Images in the training set
• How was the ground truth ascertained
• Whether the algorithm was trained on mydriatic or non mydriatic image
• Whether the algorithm was trained on single field or multiple field (field-
investigations)
26
Selecting algorithm depends on circumstances of intended
screening
Diabetic Retinopathy is the main specific complication which affects 1/3rd of the
diabetic population and is a sight-threatening condition.*
#AI IN DIABETIC RETINOPATHY

27. FINAL OUTCOME IN AI BASED SYSTEM
• Referable DR,
• Non-referable DR,
• Ungradable image.
• Hospital referral (Referable DR +
Ungradable image)
• Vision threatning DR
Challenges
• Regional variations in outcomes
• Intergrader variations
• Agreement between human graders
and the algorithm
27

28. ESTABLISHMENT OF AI
ALGORITHM
28
Abstraction of the proposed algorithmic pipeline
Fundus heatmap overlaid on a fundus image

29. COMMERCIAL PRODUCT FOR DR
SCREENING
29

30. #AI IN DIABETIC RETINOPATHY
• In April 2018, the US Food and Drug Administration (FDA) approved an AI algorithm, developed by
IDx, used with Topcon Fundus camera (Topcon Medical) for DR identification.
• a study was done on 900-subjects in a primary-care setting (10 primary care sites) with automated
image analysis.
• Two 45-degree digital images per eye (one centered on the macula, one centered on the optic nerve)
were obtained and analyzed.
• These images were compared with the stereo, widefield fundus imaging interpreted by the
Wisconsin Fundus Photograph Reading Centre (FPRC)
Saeedi P, Petersohn I, Salpea P, Malanda B, Karuranga S, Unwin N, Colagiuri S, Guariguata L, Motala AA, Ogurtsova K, Shaw JE, Bright D, Williams R; IDF Diabetes Atlas
Committee. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes
Atlas, 9th edition. Diabetes Res Clin Pract. 2019 Nov;157:107843. doi: 10.1016/j.diabres.2019.107843. Epub 2019 Sep 10. PMID: 31518657.*
US Food and Drug Administration. FDA permits marketing of artificial intelligence-based device to detect certain diabetes-related eye problems. Available from:
https://www.fda.gov/NewsEvents/ Newsroom/PressAnnouncements/ucm604357.htm. Published April 11, 2018. [Last accessed on 2018 Aug 12].
30

31. AI IN DIABETIC RETINOPATHY
• By autonomous comparison software provides one of the two results:*
• (1) If more than mild DR detected, refer to an eyecare professional (ECP);
• (2) If the results are negative for more than mild DR, rescreen in 12 months.
• Based on the analysis a new entity called more than minimal DR (mtmDR) was
defined- the presence of ETDRS level 35 or higher (microaneurysms plus hard
exudates, cotton wool spots, and/or mild retinal hemorrhages) and/or DME in at
least one eye**
• Sensitivity and specificity of the technology was 87.4% and 89.5% respectively for
detecting more than mild DR
• Anti-VEGF outcome prediction and dose optimization in DME.
Abramoff M. Artificial intelligence for automated detection of diabetic retinopathy in primary care. Paper presented at: Macular Society; February 22, 2018; Beverly Hills,
CA. Available from: http:// webeye.ophth.uiowa.edu/abramoff/MDA MacSocAbst 2018 02 22. Pdf [Internet]. [Last accessed on 2019 Mar 26].*
Pros and Cons of Using an AI-Based Diagnosis for Diabetic Retinopathy: Page 4 of 5 N.d. Optometry Times. Available from:
http://www.optometrytimes.com/article/pros-and-cons-using-ai-based-diagnosis-diabetic-retinopathy. [Last accessed on 2018 Oct 29**
31

32. IDX-DR ANALYSIS REPORT 32

33. 33

34. EyeArt Retmarker
SENSITIVITY 94.7% for any retinopathy,
93.8% for referable retinopathy
(human graded as either
ungradable, maculopathy,
preproliferative, or
proliferative), 99.6% for
proliferative retinopathy
73.0% for any retinopathy,
85.0% for referable retinopathy,
97.9% for proliferative
retinopathy.
SPECIFICITY 20% for any DR 52.3% for any DR
Tufail A, Kapetanakis VV, Salas-Vega S, Egan C, Rudisill C, Owen CG, et al. An observational study to assess if automated diabetic retinopathy image
assessment software can replace one or more steps of manual imaging grading and to determine their cost-effectiveness. Health Technol Assess.
2016;20(92):1–72
34

35. MEDIOS-AI FOR DR ON SMART
PHONE
• A study looking into 900 adult subjects with diabetes in India, where five retinal specialists graded
images taken with the Remidio mobile camera for any DR or rDR.
• This was later compared to the Medios AI software running offline on an Iphone 6
• Medios AI achieved good results with sensitivity and specificity pair for any DR of 83.3% and 95.5%
and for rDR 93% and 92.5%
Sosale B, Aravind SR, Murthy H, Narayana S, Sharma U, SGV G, et al. Simple, mobile-based artificial intelligence algorithm in the detection of diabetic
retinopathy (SMART) study. BMJ Open Diabetes Res Amp Care. 2020;8(1):e000892
35

36. PUPIL: ARE THEY DIFFERENT IN DIABETIC
RETINOPATHY
• Baseline pupil diameter (BPD) decreased with increasing
severity of diabetic retinopathy.
• Mean velocity of pupillary constriction (VPC) decreased
progressively with increasing severity of retinopathy.
• Mean velocity of pupil re dilatation (VPD) as compared to the
control group was significantly reduced in the no DR (p -
0.03), mild NPDR (p - 0.038), moderate NPDR (p - 0.05),
PDR group (p -0.02).
• Pupillary dynamics are abnormal in early stages of diabetic
retinopathy and progress with increasing retinopathy severity.
36

37. PREDICTING DIABETIC NEUROPATHY
FROM RETINAL IMAGES
• Retinal sensitivity was checked with
microperimeter
• Alterations in Retinal Function seen in DN
• Increase in foveal thickness
• Reduced RNFL thickness
• Retinal images from people with diabetes can be
used to identify individuals with DN.
• Simultaneous screening of DN and DR: new
possibilities in preventing microvascular
complications of Diabetes
37

38. PREDICTING RESPONSE (GOOD/POOR)
BASED ON INFLAMMATORY MARKERS
• Uveitis macular edema: Annotation of inflammatory markers
• Deep learning algorithm to detect these markers
• Use this algorithm to differentiate b/w DME with UME.
• Use the algorithm to see a difference in responder & no responder to the treatment
38

39. WHEN NOT TO USE
AI
PATIENTS
WITH KNOWN
RETINOPATHY
PRIOR
RETINOPATHY
TREATMENT
SYMPTOMS
OF VISION
IMPAIRMENT
39

40. FLUID INTELLIGENCE APP
40

41. #OCULAR ULTRASOUND BASED AI
41
Heat maps highlighting regions of abnormalities detected using the DLA

42. #AI IN AGE-RELATED MACULAR
DEGENERATION (ARMD)
• Age-related macular degeneration (AMD) accounts for approximately 9% of global blindness and is
the leading cause of visual loss in developed countries.*
• The number of people with AMD worldwide is projected to be 196 million in 2020, rising
substantially to 288 million in 2040.**
• There are two forms of late AMD:
• 1)neovascular AMD
• 2)atrophic AMD, defined by geographic atrophy (GA)
Congdon N, O’Colmain B, Klaver CCW, et al. Causes and prevalence of visual impairment among adults in the United States. Arch Ophthalmol Chic Ill
1960. 2004;122(4):477–85. https://doi.org/10.1001/ archopht.122.4.477.
Wong WL, Su X, Li X, et al. Global prevalence of age-related macular degeneration and disease burden projection for 2020 and 2040: a systematic review and
meta-analysis. Lancet Glob Health. 2014;2(2):e106– 16. https://doi.org/10.1016/S2214-109X( 13)70145-1.
42

43. ESTABLISHMENT OF AI TOOL TO SCREEN
ARMD
ОСТ-
• Huge number of macular OCT's that are
routinely done around the world
• Macular OCTs have dense three dimensional
structural information that is usually
consistently captured.
• OCTs provide structural detail that is not easily
visible using conventional imaging techniques
43
Examples of identification of
pathology by deep learning
algorithm

44. DIAGNOSIS OF ARMD WITH
AI
44
(A) interrupted outer retina (pink)
(B) Interrupted RPE (lake blue)
(C)absence of outer retina (yellow)
(D) absence of RPE (dark blue)
(E) hypertransmission «250um (red)
(F) hypertransmission z250um (green)
Wei, W., Southern, J., Zhu, K. et al. Deep learning to detect macular atrophy in wet age-related
macular degeneration using optical coherence tomography. Sci Rep 13, 8296 (2023).
https://doi.org/10.1038/s41598-023-35414-y

45. DEEP LEARNING NETWORK FOR ARMD
45

46. COMMERCIALLY AVAILABLE DL SYSTEM
USED IN ARMD
• AlexNet
• Google Net
• VGG
• Inception-V5
• ResNet
• Inception-ResNet-V2
46

47. #AI IN GLAUCOMA DIAGNOSIS
• Glaucoma is a leading cause of irreversible blindness, with a global prevalence of 3.5% and
a global burden of 76 million affected people in 2020.
• Early detection and treatment can preserve vision in affected individuals.
• However, glaucoma is asymptomatic in early stages, as visual fields are not affected until
20–50% of corresponding retinal ganglion cells are lost.
Tham YC, Li X, Wong TY, Quigley HA, Aung T, Cheng CY. Global prevalence of glaucoma and projections of glaucoma burden through 2040: a systematic review
and meta-analysis. Ophthalmology. 2014;121:2081–90.
Harwerth RS, Carter-Dawson L, Shen F, Smith EL 3rd, Crawford ML. Ganglion cell losses underlying visual field defects from experimental glaucoma. Invest
Ophthalmol Vis Sci. 1999; 40:2242–50.
Harwerth RS, Carter-Dawson L, Smith EL 3rd, Barnes G, Holt WF, Crawford ML. Neural losses correlated with visual losses in clinical perimetry. Invest
Ophthalmol Vis Sci. 2004;45:3152–60.
47

48. DEEP LEARNING AND
DETECTION OF THE
GLAUCOMATOUS DISC
• Assessment of optic nerve head (ONH) integrity is the foundation for detecting
glaucomatous damage.
• PROCEDURE:
Eileen L. Mayro et al; The impact of artificial intelligence in the diagnosis and management of glaucoma; Eye (2020) 34:1–11
48

49. RNFL thickness maps extracted from all participant SS-OCT scans, Structural RNFL
features were identified using principal component analysis (PCA)
49
En face and RNFL thickness map
Bright area suggestive of RNFL defect
An accuracy of 93% (based on expert opinion) was reported for a hybrid deep
learning ANN analyzing single-scan SS-OCT data to classify eyes as normal or
glaucomatous

50. AI IN GLAUCOMA- IMPORTANCE OF VISUAL FIELD
50

51. AI TOOL PREDICT TREATMENT
RESPONSE
51
Improvement in visual field

52. FILTERED FORECASTING
METHOD
52
Kalman filtering - Novel glaucoma forecasting tool that can generate a menu
of personalized and dynamically updated target iOPs
Fast progression Slow progression

53. TOWARDS “AUTOMATED GONIOSCOPY”
• An independent test set of 39 936 SS-OCT scans from 312 phakic subjects (128 SS-OCT
meridional scans per eye) was analysed.
• Participants above 50 years with no previous history of intraocular surgery were
consecutively recruited from glaucoma clinics.
• Indentation gonioscopy and dark room SS-OCT were performed.
• For each subject, all images were analysed by a DL-based network based on the VGG-16
architecture, for gonioscopic angle-closure detection.
• RESULTS: the AUC of the DLA was 0.85 (95% CI:0.80 to 0.90, with sensitivity of 83% and a
specificity of 87%) to classify gonioscopic angle closure with the optimal cut-off value of
>35% of circumferential angle closure.
Porporato N, et al. Br J Ophthalmol 2022;106:1387–1392. doi:10.1136/bjophthalmol-2020-318275
53

54. COMBINING STRUCTURE
AND FUNCTION IN
GLAUCOMA DIAGNOSIS
• Global VF indices (mean defect, corrected loss variance, and short-term fluctuation)
in combination with structural data (CDR, rim area, cup volume, and nerve fiber
layer height) analyzed by an ANN was capable to correctly identify glaucomatous
eyes with an accuracy of 88% in an early study.*
• This figure was higher than that of the same ANN trained with only structural or
functional data.
Brigatti L, Hoffman D, Caprioli J. Neural networks to identify glaucoma with structural and functional measurements.
Am J Ophthalmol. 1996;121:511–21.*
Bowd C, Hao J, Tavares IM, et al. Bayesian machine learning classifiers for combining structural and functional measurements to classify healthy and
glaucomatous eyes. Invest Ophthalmol Vis Sci. 2008;49:945–53.
54

55. #AI IN ROP SCREENING IN NEWBORNS
• Retinopathy of Prematurity (ROP)
accounts for 6–18% of childhood
blindness, worldwide
• The decision to treat is primarily
based on the presence of plus
disease, defined as dilation and
tortuosity of retinal vessels.
• However, clinical diagnosis of plus
disease is highly subjective and
variable.
Chiang et al; International Classification of Retinopathy of Prematurity; Third Edition; Elsevier on behalf of AAO; 2021
JamesM. Brown et al; Automated Diagnosis of Plus Disease in Retinopathy of Prematurity Using Deep Convolutional Neural Networks; JAMA Ophthalmol. 2018;136(7):803-
810. doi:10.1001/jamaophthalmol.2018.1934
55

56. Challenges to delivery of care:
• Clinical diagnosis is highly variable, and high interobserver inconsistency on plus disease
diagnosis, even among ROP experts.
• The number of ophthalmologists and neonatologists willing and able to manage
ROP is insufficient because of logistical difficulties, extensive training process, time-
consuming examination, and significant malpractice liability.
• The incidence of ROP worldwide is rising because of advances in neonatology
These challenges have stimulated research in developing quantitative and objective
approaches to ROP diagnosis using computer-based image analysis (CBIA)
56

57. COMMERCIALLY AVAILABLE DL SYSTEM
USED IN ROP
• I-ROP DL algorithm
Limitations
•Convolutional neural networks are only as
robust as the data on which they are trained.
•System currently only classifies plus discase
•Ideally, a fully automated ROP screening
platform could classify zone, stage, and
overall disease category as well as predict
need for treatment.
JamesM. Brown et al; Automated Diagnosis of Plus Disease in Retinopathy of Prematurity
Using Deep Convolutional Neural Networks; JAMA Ophthalmol. 2018;136(7):803-810.
doi:10.1001/jamaophthalmol.2018.1934
57

58. ADVANTAGES/PROMISES OF AI IN
OPHTHALMOLOGY
• outperform doctors,
• help to diagnose what is presently undiagnosable,
• help to treat what is presently untreatable,
• to recognize on images what is presently unrecognizable,
• predict the unpredictable,
• classify the unclassifiable,
• decrease the workflow inefficiencies,
• decrease hospital admissions and readmissions,
• increase medication adherence
• decrease patient harm
• decrease or eliminate misdiagnosis
• Provide personalised, high-precision medicine (“Pharmacogenomics”)
Topol E. Deep Medicine: How Artificial Intelligence Can Make
Healthcare Human Again. Basic Books, New York 2019
58

59. CHALLENGES IN THE APPLICATION OF AI
• Requires a large amount of data -Insufficient data may decrease the
performance of DL models.
• For rare eye diseases - Low incidence makes it difficult for researchers to
collect enough data for Al research.
• Requirement of prospective data -Some cohort studies have high
requirements for patient data, and researchers need to collect data
prospectively, which is very time- and energy-consuming
59

60. SECURITY AND ETHICS
• Reporting guidelines- Al often overlooks clinically relevant details. For example, the
criteria for participant recruitment, demographics, risk control. Sandardized
reporting protocols are the solutions, like- CON-SORT-AI, STARD-AI, SPIRIT-Al and
TRIPOD
• Security Medical -Al research should be performed on multicenter datasets. Data
transfers between research collaborators, especially for international collaborations,
are often limited because of patient privacy and data security.
• Ethics- As Al is gradually being integrated in clinical practice and medical
professionals are getting used to it, there is also a growing trend for an ethical
framework to guide the real application of DI systems.
60

61. WHAT DOES AI HERALD FOR THE
FUTURE?
• Retinal microvasculature analyses potentially predict CVD risk factors (e.g. blood pressure, diabetes),
direct CVD events (e.g. CVD mortality), retinal features (e.g. retinal vessel calibre) and CVD
biomarkers (e.g. coronary artery calcium score) by “Google Deep Mind Health”*
• Automated grading of cataracts
• Managing pediatric conditions such as refractive errors, congenital cataracts, detect strabismus,
predicting future high myopia, and diagnosing reading disability
• To automatically detect leukocoria in children from a recreational smartphone or digital camera
photographs
• Measuring inner and outer retinal layer thicknesses to predict the risk for Alzheimer’s disease**
• OCULOPLASTY: Measuring referable blepharoptosis and also in ocular oncology
• High fidelity mobile holograms and Extended reality(XR) for remote health care through upcoming
6G!!
Wong DY, Lam MC, Ran A, Cheung CY. Artificial intelligence in retinal imaging for cardiovascular disease prediction: current trends and future directions. Current Opinion in
Ophthalmology. 2022 Sep 1;33(5):440-6.
Balyen L, Peto T. Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology. Asia Pac J Ophthalmol 2019;8:264-72**
61

62. SUMMARY
• Artificial intelligence is a disruptive technology which has myriad applications at
present and in the offing for the medical field.
• It has revolutionised Ophthalmology through aiding in screening and diagnosis of
regularly encountered diseases like Keratoconus, cataract, DR, AMD, ROP,
Glaucoma, etc.
• It has infused accuracy in pre- and post-op calculations in cataract and refractive
surgeries.
• Prediction of systemic diseases through ocular measurements with AI is expected in
the future.
• AI-is a double-edged sword- caution needs to be exercised by including the
supervisory human touch.
62

63. 63