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QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY.
Retina 2020; 40(2):322-332R

Abstract

PURPOSE

This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging.

METHODS

One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, that is, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, blood vessel density, foveal avascular zone area, and foveal avascular zone contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine classification model was trained and tested for computer-aided classification of NPDR stages. Sensitivity, specificity, and accuracy were used as performance metrics of computer-aided classification, and receiver operation characteristics curve was plotted to measure the sensitivity-specificity tradeoff of the classification algorithm.

RESULTS

Among 6 individual OCTA features, blood vessel density shows the best classification accuracies, 93.89% and 90.89% for control versus disease and control versus mild NPDR, respectively. Combined feature classification achieved improved accuracies, 94.41% and 92.96%, respectively. Moreover, the temporal-perifoveal region was the most sensitive region for early detection of DR. For multiclass classification, support vector machine algorithm achieved 84% accuracy.

CONCLUSION

Blood vessel density was observed as the most sensitive feature, and temporal-perifoveal region was the most sensitive region for early detection of DR. Quantitative OCTA analysis enabled computer-aided identification and staging of NPDR.

Authors+Show Affiliations

Departments of Bioengineering.Mathematics, Statistics and Computer Sciences, and.Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois.Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois.Mathematics, Statistics and Computer Sciences, and.Departments of Bioengineering. Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois.

Pub Type(s)

Journal Article

Language

eng

PubMed ID

31972803

Citation

Alam, Minhaj, et al. "QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES for OBJECTIVE CLASSIFICATION and STAGING of DIABETIC RETINOPATHY." Retina (Philadelphia, Pa.), vol. 40, no. 2, 2020, pp. 322-332.
Alam M, Zhang Y, Lim JI, et al. QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY. Retina (Philadelphia, Pa). 2020;40(2):322-332.
Alam, M., Zhang, Y., Lim, J. I., Chan, R. V. P., Yang, M., & Yao, X. (2020). QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY. Retina (Philadelphia, Pa.), 40(2), pp. 322-332. doi:10.1097/IAE.0000000000002373.
Alam M, et al. QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES for OBJECTIVE CLASSIFICATION and STAGING of DIABETIC RETINOPATHY. Retina (Philadelphia, Pa). 2020;40(2):322-332. PubMed PMID: 31972803.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - QUANTITATIVE OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY FEATURES FOR OBJECTIVE CLASSIFICATION AND STAGING OF DIABETIC RETINOPATHY. AU - Alam,Minhaj, AU - Zhang,Yue, AU - Lim,Jennifer I, AU - Chan,Robison V P, AU - Yang,Min, AU - Yao,Xincheng, PY - 2020/1/24/entrez PY - 2020/1/24/pubmed PY - 2020/1/24/medline SP - 322 EP - 332 JF - Retina (Philadelphia, Pa.) JO - Retina (Philadelphia, Pa.) VL - 40 IS - 2 N2 - PURPOSE: This study aims to characterize quantitative optical coherence tomography angiography (OCTA) features of nonproliferative diabetic retinopathy (NPDR) and to validate them for computer-aided NPDR staging. METHODS: One hundred and twenty OCTA images from 60 NPDR (mild, moderate, and severe stages) patients and 40 images from 20 control subjects were used for this study conducted in a tertiary, subspecialty, academic practice. Both eyes were photographed and all the OCTAs were 6 mm × 6 mm macular scans. Six quantitative features, that is, blood vessel tortuosity, blood vascular caliber, vessel perimeter index, blood vessel density, foveal avascular zone area, and foveal avascular zone contour irregularity (FAZ-CI) were derived from each OCTA image. A support vector machine classification model was trained and tested for computer-aided classification of NPDR stages. Sensitivity, specificity, and accuracy were used as performance metrics of computer-aided classification, and receiver operation characteristics curve was plotted to measure the sensitivity-specificity tradeoff of the classification algorithm. RESULTS: Among 6 individual OCTA features, blood vessel density shows the best classification accuracies, 93.89% and 90.89% for control versus disease and control versus mild NPDR, respectively. Combined feature classification achieved improved accuracies, 94.41% and 92.96%, respectively. Moreover, the temporal-perifoveal region was the most sensitive region for early detection of DR. For multiclass classification, support vector machine algorithm achieved 84% accuracy. CONCLUSION: Blood vessel density was observed as the most sensitive feature, and temporal-perifoveal region was the most sensitive region for early detection of DR. Quantitative OCTA analysis enabled computer-aided identification and staging of NPDR. SN - 1539-2864 UR - https://www.unboundmedicine.com/medline/citation/31972803/QUANTITATIVE_OPTICAL_COHERENCE_TOMOGRAPHY_ANGIOGRAPHY_FEATURES_FOR_OBJECTIVE_CLASSIFICATION_AND_STAGING_OF_DIABETIC_RETINOPATHY L2 - http://dx.doi.org/10.1097/IAE.0000000000002373 DB - PRIME DP - Unbound Medicine ER -