Recent Advancements in AI: A New Era for Vision Science and Eyecare Delivery

Varsha Singh, B. Optom.

M. Optom Student, The Sankara Nethralaya Academy, Chennai

 

Artificial Intelligence (AI) is transforming the future of eye care by providing viable solutions to the current barriers in eye care, such as inaccurate diagnoses and a lack of manpower. Day by day, it is gaining popularity as it enhances eye care practice by reducing the time required for disease screening, detection, diagnosis, and management, and by improving diagnostic accuracy. The option of AI for eye care is available from basic refraction to advanced retinal diagnosis. ⁽¹⁾

Recent Advancements in AI Tools for Eye Care

Automated Retinoscopy

The AI-powered, portable retinoscopy system uses a smartphone attached to the standard retinoscope to record the video and estimate the net refractive error by using a modified retinoscopy algorithm. The reported sensitivity and specificity values are 91% and 74% respectively, with the Mean Absolute Refractive Error (MAE) compared to subjective refraction being 0.75 ± 0.67 D. ⁽²⁾

Feature	Description
Technology	AI-powered portable retinoscopy system using a smartphone attached to a retinoscope.
Principle	Records video during retinoscopy → applies modified retinoscopy algorithm → estimates net refractive error.
Performance	Sensitivity: 91%, Specificity: 74%, Mean Absolute Error (MAE): 0.75 ± 0.67 D compared to subjective refraction.
Applications	Screening and diagnosis of refractive errors in community eye care and low-resource settings.
Advantages	Portable, inexpensive compared to autorefractors, reduces manpower dependency.
Limitations	Less precise than subjective refraction, may require stable fixation and good video quality.

Table 1: Features of Automated Retinoscopy

AI-Driven Personalisation in Myopia Management

Fundus2Globe is an AI tool that constructs a 3D model of the human eye by taking 2D inputs, which is useful in predicting disease progression and in deciding personalised treatment strategies. ⁽³⁾

CeViT (Copula-Enhanced Vision Transformer) is a deep learning architecture designed for image-based myopia screening using ultra-widefield (UWF) fundus images. It also classifies high myopia and predicts axial length elongation. ⁽⁴⁾

Feature	Description
Technology	Deep learning model (Vision Transformer architecture enhanced with copula-based dependency modeling).
Principle	Uses ultra-widefield (UWF) fundus images to classify myopia severity and predict axial length elongation.
Performance	High accuracy in detecting high myopia.
Applications	Population-based screening programs, clinical myopia monitoring, prediction of progression.
Advantages	Handles large-field imaging better than conventional CNNs, enabling early intervention.
Limitations	Requires UWF devices (expensive), not yet widely validated in all populations.

Table 2: Features of CeViT

Anterior Segment Evaluation

CorneAI is an AI-based deep learning model that is developed to improve the diagnostic accuracy of the anterior segment, with an accuracy of 86%. It is also reported that it has improved the diagnostic accuracy by 9.6%, and when compared to the slit-lamp, the diagnostic accuracy improved by 9.4%. ⁽⁵⁾

KerNet is the model to detect the asymmetric keratoconic eye by utilising the data from the maps provided by Pentacam HR, with the reported accuracy of 94.12%. ⁽⁶⁾

Feature	Description
Technology	AI-based model for keratoconus detection.
Principle	Utilises Pentacam HR maps (topography & tomography) to differentiate asymmetric keratoconic eyes.
Performance	Accuracy: 94.12%.
Applications	Early detection of keratoconus, screening candidates for refractive surgery.
Advantages	More sensitive in detecting subtle asymmetry compared to human grading.
Limitations	Pentacam required (costly), limited to keratoconus and not other anterior segment pathologies.

Table 3: Features of KerNet

Retinal Diagnostics and Screening

IDx‑DR and AEYE are AI software programs that are FDA-approved. They are designed particularly for the detection of diabetic retinopathy (including macular oedema). They do not require an eye care professional to interpret the result; they will automatically give you the diagnosis. ^(7,8)

Feature	IDx-DR	AEYE
Approval	First FDA-approved autonomous AI for DR (2018)	FDA-approved AI for DR
Input	Fundus photographs	Fundus photographs
Output	Detects referable DR ± macular edema	Detects referable DR ± macular edema
Mode	Standalone, offline certified system	Cloud-based, integrates with workflows
Performance	Sensitivity & specificity >85–90%	Sensitivity & specificity >85–90%
Advantages	Proven regulatory credibility, no clinician needed	Faster reporting, scalable, remote use
Limitations	DR only, high-quality images needed, limited availability	DR only, internet dependent, limited global access

Table 4: Features of IDx-DR and AEYE

EyeFound and GlobeReady are the multimodal foundation AI models in the field of eyecare to diagnose multiple retinal diseases by utilising Colour Fundus Photographs (CFPs) and OCT images. The reported accuracy ranges from 94.9%-99.4% for CFPs and 88.2%–96.2% for OCT images, respectively, for the GlobeReady model. ^(9,10)

Feature	GlobeReady	EyeFound
Type	Multimodal foundation AI model	Multimodal foundation AI model
Input	Fundus photographs (CFPs) + OCT images	Fundus photographs (CFPs)
Output	Detects multiple retinal diseases using combined CFP + OCT data	Detects multiple retinal diseases from CFPs
Performance	Accuracy: 94.9–99.4% (CFPs), 88.2–96.2% (OCTs)	Accuracy: 94.9–99.4% (CFPs)
Applications	Comprehensive retinal screening (DR, AMD, glaucoma, etc.)	Broad retinal disease screening (mainly CFP-based)
Advantages	Integrates structural + functional imaging, higher reliability	High accuracy from single-modality input, scalable
Limitations	Requires OCT (expensive, less accessible), high computational demand	Limited to CFPs only, may miss OCT-based features

Table 5: Features of GlobeReady and EyeFound

Conclusion

There are various AI tools and models available, and new modalities are emerging, providing several advantages in eye care, be it early detection, time-saving screening, or accurate diagnoses. However, most of these are not easily accessible and remain confined to research laboratories. With the innovation, attention should also be directed toward the accessibility aspect of these tools and models by considering all these points. ^(1,11)
 

References

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7. Cornwell, K. (n.d.). Artificial intelligence and emerging technologies in optometry and ophthalmology. EyesOnEyecare.
8. Healio Staff. (2024). Top AI stories of 2024: New opportunities for screening innovation. Healio: Optometry.
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10. Wang, M., Lin, T., Hou, Q., Lin, A., Wang, J., Peng, Q., … Cheng, C.-Y. (2025). A clinician-friendly platform for ophthalmic image analysis without technical barriers.
11. Blandford, A., Abdi, S., Aristidou, A., Carmichael, J., Cappellaro, G., Hussain, R., & Balaskas, K. (2022). Protocol for a qualitative study to explore acceptability, barriers and facilitators of the implementation of new teleophthalmology technologies between community optometry practices and hospital eye services. BMJ open, 12(7), e060810.