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Objectives <p>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).</p> Methods <p>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.</p> Results <p>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 (<i>n</i> = 492) were 88.48% (95% CI: 84.04–92.03%) and 87% (95% CI: 81.86–91.11%), respectively. Against combined grading (<i>n</i> = 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.</p> Conclusion <p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年7月11日

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  • Kalpa Negiloni,
  • Prabu Baskaran,
  • Divya Parthasarathy Rao,
  • Aditya Maitray,
  • Florian M. Savoy,
  • Shruthi Suresh,
  • Maanasi Mahalingam,
  • M. J. Vighnesh,
  • Anand Rajendran

摘要

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.