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<p>This proof-of-concept study developed and validated an automated, biomarker-targeted, real-time microperimetry (MP) approach for individualized structure-function assessment in geographic atrophy (GA). Patients underwent volumetric spectral-domain optical coherence tomography (OCT) and mesopic MP. Automated deep learning-based OCT segmentation using a clinically validated software was applied to identify regions of ellipsoid zone loss (EZL) and retinal pigment epithelium loss (RPEL). A rule-based algorithm then selected 40 biomarker-targeted retinal test locations, with test-point density dynamically adapted to individual lesion morphology and deliberately prioritized within EZL rather than uniformly distributed. Statistical analysis employed multivariable mixed-effects models. In 64 eyes from 44 patients (mean age 79.1 ± 5.0 years; 59% female), 2,560 targeted MP stimuli were tested. Mean retinal sensitivity (RS) was 7.2 ± 7.4 dB in RPEL, 13.3 ± 7.1 dB in EZL, and 19.0 ± 5.0 dB in preserved retina (<i>p</i> &lt; 0.001). The median deviation between intended and actual test locations was 38.2&#xa0;μm (IQR 24.6–59.3&#xa0;μm), with 98.5% of points correctly localized. This OCT-based, biomarker-guided MP approach enables precise, patient-specific structure-function mapping within a single visit and may serve as a sensitive functional endpoint in future GA trials and clinical practice.</p>

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Automated OCT-tailored and same-visit biomarker-targeted microperimetry in geographic atrophy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月27日

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  • Merle Tratnig-Frankl,
  • Lukas Kuchernig,
  • Klaudia Birner,
  • Simon Schürer-Waldheim,
  • Anna Eidenberger,
  • Johannes Schrittwieser,
  • Markus Gumpinger,
  • Georg Faustmann,
  • Magdalena Baratsits,
  • Oliver Leingang,
  • Gregor S. Reiter,
  • Ursula Schmidt-Erfurth

摘要

This proof-of-concept study developed and validated an automated, biomarker-targeted, real-time microperimetry (MP) approach for individualized structure-function assessment in geographic atrophy (GA). Patients underwent volumetric spectral-domain optical coherence tomography (OCT) and mesopic MP. Automated deep learning-based OCT segmentation using a clinically validated software was applied to identify regions of ellipsoid zone loss (EZL) and retinal pigment epithelium loss (RPEL). A rule-based algorithm then selected 40 biomarker-targeted retinal test locations, with test-point density dynamically adapted to individual lesion morphology and deliberately prioritized within EZL rather than uniformly distributed. Statistical analysis employed multivariable mixed-effects models. In 64 eyes from 44 patients (mean age 79.1 ± 5.0 years; 59% female), 2,560 targeted MP stimuli were tested. Mean retinal sensitivity (RS) was 7.2 ± 7.4 dB in RPEL, 13.3 ± 7.1 dB in EZL, and 19.0 ± 5.0 dB in preserved retina (p < 0.001). The median deviation between intended and actual test locations was 38.2 μm (IQR 24.6–59.3 μm), with 98.5% of points correctly localized. This OCT-based, biomarker-guided MP approach enables precise, patient-specific structure-function mapping within a single visit and may serve as a sensitive functional endpoint in future GA trials and clinical practice.