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Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids
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 ret...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416860/ https://www.ncbi.nlm.nih.gov/pubmed/37568913 http://dx.doi.org/10.3390/diagnostics13152550 |
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author | Tripathi, Aditya Kumar, Preetham Tulsani, Akshat Chakrapani, Pavithra Kodiyalbail Maiya, Geetha Bhandary, Sulatha V. Mayya, Veena Pathan, Sameena Achar, Raghavendra Acharya, U. Rajendra |
author_facet | Tripathi, Aditya Kumar, Preetham Tulsani, Akshat Chakrapani, Pavithra Kodiyalbail Maiya, Geetha Bhandary, Sulatha V. Mayya, Veena Pathan, Sameena Achar, Raghavendra Acharya, U. Rajendra |
author_sort | Tripathi, Aditya |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-10416860 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-104168602023-08-12 Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids Tripathi, Aditya Kumar, Preetham Tulsani, Akshat Chakrapani, Pavithra Kodiyalbail Maiya, Geetha Bhandary, Sulatha V. Mayya, Veena Pathan, Sameena Achar, Raghavendra Acharya, U. Rajendra Diagnostics (Basel) Article 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. MDPI 2023-07-31 /pmc/articles/PMC10416860/ /pubmed/37568913 http://dx.doi.org/10.3390/diagnostics13152550 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Tripathi, Aditya Kumar, Preetham Tulsani, Akshat Chakrapani, Pavithra Kodiyalbail Maiya, Geetha Bhandary, Sulatha V. Mayya, Veena Pathan, Sameena Achar, Raghavendra Acharya, U. Rajendra Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids |
title | Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids |
title_full | Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids |
title_fullStr | Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids |
title_full_unstemmed | Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids |
title_short | Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids |
title_sort | fuzzy logic-based system for identifying the severity of diabetic macular edema from oct b-scan images using dril, hrf, and cystoids |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10416860/ https://www.ncbi.nlm.nih.gov/pubmed/37568913 http://dx.doi.org/10.3390/diagnostics13152550 |
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