A paper written by Harvard Ophthalmology faculty Ivana K. Kim, MD; Demetrios G. Vavvas, MD, PhD; John B. Miller, MD; Joan W. Miller, MD; Teresa C. Chen, MD; and Deeba Husain, MD; research fellow Inês Laíns, MD; and former research fellow Edem Tsikata, PhD; entitled “Automated Brightness and Contrast Adjustment of Color Fundus Photographs for the Grading of Age-Related Macular Degeneration,” was the tenth most-read paper of 2017 in Translational Vision Science and Technology (TVST).
The current gold-standard for diagnosing and classifying age-related macular degeneration (AMD) is through the detection of fundus abnormalities, such as drusen and focal pigmentation changes, on digital color fundus photographs. However, the quality of the images can vary based on patient factors and camera characteristics. Standardizing these images is essential to accurately detect and grade AMD. Therefore, the investigators in this study developed an algorithm to automatically standardize the brightness, contrast, and color balance of digital color fundus photographs, and validated this algorithm by determining the effects of the standardization on image quality and disease grading. The automated software enables rapid and accurate standardization of color photographs for AMD grading.
TVST is an official journal of the Association for Research in Vision and Ophthalmology, and emphasizes multidisciplinary research that bridges the gap between basic research and clinical care. They seek manuscripts that may advance or change the way we understand and/or treat vision-threatening diseases.