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Article

Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids

by
Aditya Tripathi
1,†,
Preetham Kumar
1,†,
Akshat Tulsani
1,
Pavithra Kodiyalbail Chakrapani
1,
Geetha Maiya
2,
Sulatha V. Bhandary
3,
Veena Mayya
1,*,
Sameena Pathan
1,
Raghavendra Achar
1,* and
U. Rajendra Acharya
4
1
Department of Information & Communication Technology, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
2
Department of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
3
Department of Ophthalmology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal 576104, India
4
School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield Central, QLD 4300, Australia
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Diagnostics 2023, 13(15), 2550; https://doi.org/10.3390/diagnostics13152550
Submission received: 20 June 2023 / Revised: 19 July 2023 / Accepted: 21 July 2023 / Published: 31 July 2023
(This article belongs to the Special Issue Artificial Intelligence in Eye Disease, 3rd Edition)
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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: DME; fuzzy engine; healthcare; clinical decision support system; computer vision; semantic segmentation; image classification DME; fuzzy engine; healthcare; clinical decision support system; computer vision; semantic segmentation; image classification

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MDPI and ACS Style

Tripathi, A.; Kumar, P.; Tulsani, A.; Chakrapani, P.K.; Maiya, G.; Bhandary, S.V.; Mayya, V.; Pathan, S.; Achar, R.; Acharya, U.R. Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids. Diagnostics 2023, 13, 2550. https://doi.org/10.3390/diagnostics13152550

AMA Style

Tripathi A, Kumar P, Tulsani A, Chakrapani PK, Maiya G, Bhandary SV, Mayya V, Pathan S, Achar R, Acharya UR. Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids. Diagnostics. 2023; 13(15):2550. https://doi.org/10.3390/diagnostics13152550

Chicago/Turabian Style

Tripathi, Aditya, Preetham Kumar, Akshat Tulsani, Pavithra Kodiyalbail Chakrapani, Geetha Maiya, Sulatha V. Bhandary, Veena Mayya, Sameena Pathan, Raghavendra Achar, and U. Rajendra Acharya. 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, no. 15: 2550. https://doi.org/10.3390/diagnostics13152550

APA Style

Tripathi, A., Kumar, P., Tulsani, A., Chakrapani, P. K., Maiya, G., Bhandary, S. V., Mayya, V., Pathan, S., Achar, R., & Acharya, U. R. (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), 2550. https://doi.org/10.3390/diagnostics13152550

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