1. Deep learning-based correction of cataract-induced influence on macular pigment optical density measurement by autofluorescence spectroscopy.
- Author
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Obana A, Ote K, Gohto Y, Yamada H, Hashimoto F, Okazaki S, and Asaoka R
- Subjects
- Humans, Lutein, Cross-Sectional Studies, Zeaxanthins, Spectrum Analysis, Macular Pigment, Deep Learning, Cataract therapy
- Abstract
Purpose: Measurements of macular pigment optical density (MPOD) using the autofluorescence spectroscopy yield underestimations of actual values in eyes with cataracts. Previously, we proposed a correction method for this error using deep learning (DL); however, the correction performance was validated through internal cross-validation. This cross-sectional study aimed to validate this approach using an external validation dataset., Methods: MPODs at 0.25°, 0.5°, 1°, and 2° eccentricities and macular pigment optical volume (MPOV) within 9° eccentricity were measured using SPECTRALIS (Heidelberg Engineering, Heidelberg, Germany) in 197 (training dataset inherited from our previous study) and 157 eyes (validating dataset) before and after cataract surgery. A DL model was trained to predict the corrected value from the pre-operative value using the training dataset, and we measured the discrepancy between the corrected value and the actual postoperative value. Subsequently, the prediction performance was validated using a validation dataset., Results: Using the validation dataset, the mean absolute values of errors for MPOD and MPOV corrected using DL ranged from 8.2 to 12.4%, which were lower than values with no correction (P < 0.001, linear mixed model with Tukey's test). The error depended on the autofluorescence image quality used to calculate MPOD. The mean errors in high and moderate quality images ranged from 6.0 to 11.4%, which were lower than those of poor quality images., Conclusion: The usefulness of the DL correction method was validated. Deep learning reduced the error for a relatively good autofluorescence image quality. Poor-quality images were not corrected., Competing Interests: A patent application on the deep learning-based correction technology was filed by Seirei Hamamatsu General Hospital and Hamamatsu Photonics K. K., a patent application number: WO2021/171788. There are no further patents, products in development, or marketed products to declare. The remaining authors declare no competing financial interests., (Copyright: © 2024 Obana et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
- Published
- 2024
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