Investigative ophthalmology & visual science · 2026
Hyperspectral Imaging for the Classification and Analysis of Drusen in Age-Related Macular Degeneration.
СтатьяНаблюдательное исследованиеПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Chen Chong, Razavi Raha, Romano Francesco, Niessen Heiko, Sabokrohiyeh Samin, Konda Anudeep, Chevrefils Claudia, Arbour Jean Daniel, Rhéaume Marc-André, Nissan Ramina, Rojewski Anthéa, Sorya Patricia, Santucci Lara, Campbell Shannon, Ploumi Ioanna, Zhu Ying, Ding Xinyi, Sylvestre Jean-Philippe, Miller John B, Nassar Khaled
Аннотация
PURPOSE: To characterize the spatial-spectral signatures of drusenoid deposits [soft, hard, and reticular pseudodrusen (RPD)] in eyes with non-exudative age-related macular degeneration (AMD).
METHODS: In this observational, cross-sectional, prospective study, hyperspectral retinal images and corresponding Optical Coherence Tomography (OCT) images were collected from 152 eyes of 100 participants with non-exudative AMD. Drusenoid deposits were manually annotated in 148 eyes (97 participants) by retinal specialists using OCT as the reference standard. Automated drusen identification was then performed using spectral angle mapper (SAM) and random forest classifiers.
RESULTS: Hyperspectral imaging revealed that RPD was most distinct at 470-500 nm, soft drusen at 525-575 nm, hard drusen at 620-675 nm, and pigmentary abnormalities at 650-725 nm. Using the SAM classifier, drusenoid deposits were identified with 97% sensitivity and 70% specificity. The random forest classifier achieved higher overall performance, with 88% sensitivity, 99% specificity, and an area under the receiver operating characteristic curve of 0.96. Pixel-wise SAM mapping delineated drusenoid deposits and healthy retina, extending beyond annotated regions.
CONCLUSIONS: Distinct spatial-spectral signatures for soft drusen, hard drusen, and RPD were identified using hyperspectral imaging. Both SAM and random forest classifiers demonstrated promising results for automated drusenoid deposit detection. These findings support the role of hyperspectral retinal imaging as a noninvasive tool for automated drusen mapping and patient risk stratification in nonexudative AMD.
METHODS: In this observational, cross-sectional, prospective study, hyperspectral retinal images and corresponding Optical Coherence Tomography (OCT) images were collected from 152 eyes of 100 participants with non-exudative AMD. Drusenoid deposits were manually annotated in 148 eyes (97 participants) by retinal specialists using OCT as the reference standard. Automated drusen identification was then performed using spectral angle mapper (SAM) and random forest classifiers.
RESULTS: Hyperspectral imaging revealed that RPD was most distinct at 470-500 nm, soft drusen at 525-575 nm, hard drusen at 620-675 nm, and pigmentary abnormalities at 650-725 nm. Using the SAM classifier, drusenoid deposits were identified with 97% sensitivity and 70% specificity. The random forest classifier achieved higher overall performance, with 88% sensitivity, 99% specificity, and an area under the receiver operating characteristic curve of 0.96. Pixel-wise SAM mapping delineated drusenoid deposits and healthy retina, extending beyond annotated regions.
CONCLUSIONS: Distinct spatial-spectral signatures for soft drusen, hard drusen, and RPD were identified using hyperspectral imaging. Both SAM and random forest classifiers demonstrated promising results for automated drusenoid deposit detection. These findings support the role of hyperspectral retinal imaging as a noninvasive tool for automated drusen mapping and patient risk stratification in nonexudative AMD.