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Advancing AMD screening with an offline, AI-powered smartphone-based fundus camera: A prospective, real-world clinical validation

Eye volume 39, pages 2548–2554 (2025)Cite this article

Subjects

Abstract

Objectives

This study evaluated a novel offline, AI-driven age-related macular degeneration (AMD) screening algorithm against fundus image-only grading and the standard of care (combined Spectral Domain-Optical Coherence Tomography (SD-OCT) and fundus image grading).

Methods

Conducted prospectively at a South Asian tertiary eye hospital, this study utilized a validated smartphone-based non-mydriatic fundus camera to capture macula-centred images. The Medios AI’s ability to detect referable AMD was compared to a reference standard image grading, using fundus images from the Zeiss Clarus 700 table-top camera and SD-OCT line scan across fovea. Three retina specialists provided blinded AMD diagnoses based on: (1) Zeiss Clarus 700 fundus images alone, and (2) combined SD-OCT and fundus images (standard of care). Referable AMD was defined as intermediate or advanced AMD.

Results

Among 984 eyes from 492 patients (mean age 61.8 ± 9.9 years), 52% had referable AMD. Inter-grader agreement was strong, with Cohen’s Kappa scores of 0.81–0.84. The Medios AI’s sensitivity and specificity for detecting referable AMD against fundus-only grading (n = 492) were 88.48% (95% CI: 84.04–92.03%) and 87% (95% CI: 81.86–91.11%), respectively. Against combined grading (n = 489), AI sensitivity was 90.62% (95% CI: 86.37–93.90%), and specificity was 85.41% (95% CI: 80.21–89.68%). False negatives were primarily intermediate AMD (71%), while 59% of false positives were early AMD.

Conclusion

The novel, automated, offline AMD AI integrated on a smartphone fundus camera demonstrated robust performance in identifying referable forms of AMD, supporting its potential as an affordable and accessible screening solution.

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Fig. 1
Fig. 2
Fig. 3: Confusion matrix of Medios AI – AMD performance against reference standard image grading.
Fig. 4: Comparative performance of various fundus image-based AI models for AMD detection.

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Data availability

The datasets analysed in the current study are not publicly available but are available from the corresponding author on reasonable request.

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Funding

The study received no external funding. The study was funded in part by Remidio Innovative Solutions, Pvt Ltd.

Author information

Author notes

  1. These authors contributed equally: Kalpa Negiloni, Prabu Baskaran.

Authors and Affiliations

  1. Remidio Innovative Solutions Pvt Ltd, Bengaluru, India

    Kalpa Negiloni & M. J. Vighnesh

  2. Aravind Eye Hospital, Chennai, India

    Prabu Baskaran, Aditya Maitray, Shruthi Suresh, Maanasi Mahalingam & Anand Rajendran

  3. Remidio Innovative Solutions Inc, Glen Allen, VA, USA

    Divya Parthasarathy Rao

  4. Medios Technologies, Remidio Innovative Solutions Pte Ltd, Singapore, Singapore

    Florian M. Savoy

Authors
  1. Kalpa Negiloni
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Contributions

KN, PB, DRP: Study conception and design, analysis and interpretation of data, manuscript drafting and revision. AM, FMS, SS, MM, VMJ: acquisition of data, analysis of data, manuscript revision. AR: study design & manuscript revision. All authors have read and approved the final manuscript.

Corresponding author

Correspondence to Kalpa Negiloni.

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Competing interests

KN, DPR, and VMJ reported being an employee of Remidio Innovative Solutions. FMS reported being an employee of Medios Technologies and has a financial interest in Remidio Innovative Solutions, Pvt Ltd (Stock & Patent). Other authors report no financial disclosures.

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Negiloni, K., Baskaran, P., Rao, D.P. et al. Advancing AMD screening with an offline, AI-powered smartphone-based fundus camera: A prospective, real-world clinical validation. Eye 39, 2548–2554 (2025). https://doi.org/10.1038/s41433-025-03902-2

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