Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids
Article
Tripathi, Aditya, Kumar, Preetham, Tulsani, Akshat, Chakrapani, Pavithra Kodiyalbail, Maiya, Geetha, Bhandary, Sulatha V., Mayya, Veena, Pathan, Sameena, Achar, Raghavendra and Acharya, U. Rajendra. 2023. "Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids." Diagnostics. 13 (15). https://doi.org/10.3390/diagnostics13152550
Article Title | Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids |
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ERA Journal ID | 212275 |
Article Category | Article |
Authors | Tripathi, Aditya, Kumar, Preetham, Tulsani, Akshat, Chakrapani, Pavithra Kodiyalbail, Maiya, Geetha, Bhandary, Sulatha V., Mayya, Veena, Pathan, Sameena, Achar, Raghavendra and Acharya, U. Rajendra |
Journal Title | Diagnostics |
Journal Citation | 13 (15) |
Article Number | 2550 |
Number of Pages | 20 |
Year | 2023 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2075-4418 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/diagnostics13152550 |
Web Address (URL) | https://www.mdpi.com/2075-4418/13/15/2550 |
Abstract | Diabetic Macular Edema (DME) is a severe ocular complication commonly found in patients with diabetes. The condition can precipitate a significant drop in VA and, in extreme cases, may result in irreversible vision loss. Optical Coherence Tomography (OCT), a technique that yields high-resolution retinal images, is often employed by clinicians to assess the extent of DME in patients. However, the manual interpretation of OCT B-scan images for DME identification and severity grading can be error-prone, with false negatives potentially resulting in serious repercussions. In this paper, we investigate an Artificial Intelligence (AI) driven system that offers an end-to-end automated model, designed to accurately determine DME severity using OCT B-Scan images. This model operates by extracting specific biomarkers such as Disorganization of Retinal Inner Layers (DRIL), Hyper Reflective Foci (HRF), and cystoids from the OCT image, which are then utilized to ascertain DME severity. The rules guiding the fuzzy logic engine are derived from contemporary research in the field of DME and its association with various biomarkers evident in the OCT image. The proposed model demonstrates high efficacy, identifying images with DRIL with 93.3% accuracy and successfully segmenting HRF and cystoids from OCT images with dice similarity coefficients of 91.30% and 95.07% respectively. This study presents a comprehensive system capable of accurately grading DME severity using OCT B-scan images, serving as a potentially invaluable tool in the clinical assessment and treatment of DME. |
Keywords | clinical decision support system; DME; fuzzy engine; healthcare; computer vision; semantic segmentation; image classification |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Manipal Academy of Higher Education, India |
School of Mathematics, Physics and Computing |
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