{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T17:57:35Z","timestamp":1779386255119,"version":"3.53.1"},"reference-count":39,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"ASPIRE Award for Research Excellence 2019","award":["Advanced Technology Research Council - ASPIRE."],"award-info":[{"award-number":["Advanced Technology Research Council - ASPIRE."]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diabetic retinopathy (DR) refers to the ophthalmological complications of diabetes mellitus. It is primarily a disease of the retinal vasculature that can lead to vision loss. Optical coherence tomography angiography (OCTA) demonstrates the ability to detect the changes in the retinal vascular system, which can help in the early detection of DR. In this paper, we describe a novel framework that can detect DR from OCTA based on capturing the appearance and morphological markers of the retinal vascular system. This new framework consists of the following main steps: (1) extracting retinal vascular system from OCTA images based on using joint Markov-Gibbs Random Field (MGRF) model to model the appearance of OCTA images and (2) estimating the distance map inside the extracted vascular system to be used as imaging markers that describe the morphology of the retinal vascular (RV) system. The OCTA images, extracted vascular system, and the RV-estimated distance map is then composed into a three-dimensional matrix to be used as an input to a convolutional neural network (CNN). The main motivation for using this data representation is that it combines the low-level data as well as high-level processed data to allow the CNN to capture significant features to increase its ability to distinguish DR from the normal retina. This has been applied on multi-scale levels to include the original full dimension images as well as sub-images extracted from the original OCTA images. The proposed approach was tested on in-vivo data using about 91 patients, which were qualitatively graded by retinal experts. In addition, it was quantitatively validated using datasets based on three metrics: sensitivity, specificity, and overall accuracy. Results showed the capability of the proposed approach, outperforming the current deep learning as well as features-based detecting DR approaches.<\/jats:p>","DOI":"10.3390\/s22062342","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"2342","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Automated Diagnosis of Optical Coherence Tomography Angiography (OCTA) Based on Machine Learning Techniques"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5411-2567","authenticated-orcid":false,"given":"Ibrahim","family":"Yasser","sequence":"first","affiliation":[{"name":"Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3318-2851","authenticated-orcid":false,"given":"Fahmi","family":"Khalifa","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6268-5955","authenticated-orcid":false,"given":"Hisham","family":"Abdeltawab","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9045-6698","authenticated-orcid":false,"given":"Mohammed","family":"Ghazal","sequence":"additional","affiliation":[{"name":"Electrical and Computer Engineering Department, Abu Dhabi University, Abu Dhabi P.O. Box 59911, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harpal Singh","family":"Sandhu","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7264-1323","authenticated-orcid":false,"given":"Ayman","family":"El-Baz","sequence":"additional","affiliation":[{"name":"Department of Bioengineering, University of Louisville, Louisville, KY 40292, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Wang, W., and Lo, A.C. (2018). Diabetic retinopathy: Pathophysiology and treatments. Int. J. Mol. Sci., 19.","DOI":"10.3390\/ijms19061816"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Romero-Aroca, P., Baget-Bernaldiz, M., Pareja-Rios, A., Lopez-Galvez, M., Navarro-Gil, R., and Verges, R. (2016). Diabetic macular edema pathophysiology: Vasogenic versus inflammatory. J. Diabetes Res., 2016.","DOI":"10.1155\/2016\/2156273"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1615","DOI":"10.2337\/diabetes.54.6.1615","article-title":"The pathobiology of diabetic complications: A unifying mechanism","volume":"54","author":"Brownlee","year":"2005","journal-title":"Diabetes"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s11892-017-0909-9","article-title":"Diameter changes of retinal vessels in diabetic retinopathy","volume":"17","author":"Bek","year":"2017","journal-title":"Curr. Diabetes Rep."},{"key":"ref_5","first-page":"343560","article-title":"Pathophysiology of diabetic retinopathy","volume":"2013","author":"Stewart","year":"2010","journal-title":"Diabet. Retin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1159\/000500026","article-title":"Pathophysiology of diabetic retinopathy: Contribution and limitations of laboratory research","volume":"62","author":"Kern","year":"2019","journal-title":"Ophthalmic Res."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1608","DOI":"10.1016\/j.ophtha.2018.04.007","article-title":"Guidelines on diabetic eye care: The international council of ophthalmology recommendations for screening, follow-up, referral, and treatment based on resource settings","volume":"125","author":"Wong","year":"2018","journal-title":"Ophthalmology"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"678","DOI":"10.3129\/i08-153","article-title":"Use of fluorescein and indocyanine green angiography in polypoidal choroidal vasculopathy patients following photodynamic therapy","volume":"43","author":"Windisch","year":"2008","journal-title":"Can. J. Ophthalmol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5229","DOI":"10.1167\/iovs.15-17140","article-title":"OCT angiography compared to fluorescein and indocyanine green angiography in chronic central serous chorioretinopathy","volume":"56","author":"Teussink","year":"2015","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1186\/s40942-015-0005-8","article-title":"A review of optical coherence tomography angiography (OCTA)","volume":"1","author":"Romano","year":"2015","journal-title":"Int. J. Retin. Vitr."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40662-019-0160-3","article-title":"Optical coherence tomography angiography in diabetic retinopathy: A review of current applications","volume":"6","author":"Tey","year":"2019","journal-title":"Eye Vis."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"389","DOI":"10.2147\/OPTH.S41731","article-title":"Comparison of ultra-widefield fluorescein angiography with the Heidelberg Spectralis\u00ae noncontact ultra-widefield module versus the Optos\u00ae Optomap\u00ae","volume":"7","author":"Witmer","year":"2013","journal-title":"Clin. Ophthalmol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"100390","DOI":"10.1016\/j.imu.2020.100390","article-title":"Effective blood vessels reconstruction methodology for early detection and classification of diabetic retinopathy using OCTA images by artificial neural network","volume":"20","author":"Abdelsalam","year":"2020","journal-title":"Inform. Med. Unlocked"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1001\/jamaophthalmol.2014.3616","article-title":"Retinal vascular layers imaged by fluorescein angiography and optical coherence tomography angiography","volume":"133","author":"Spaide","year":"2015","journal-title":"JAMA Ophthalmol."},{"key":"ref_15","first-page":"3469","article-title":"Artificial intelligence and deep learning in ophthalmology","volume":"20","author":"Wang","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3440","DOI":"10.1109\/TIP.2006.881959","article-title":"A statistical evaluation of recent full reference image quality assessment algorithms","volume":"15","author":"Sheikh","year":"2006","journal-title":"IEEE Trans. Image Process."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"574","DOI":"10.1148\/radiol.2017162326","article-title":"Deep learning at chest radiography: Automated classification of pulmonary tuberculosis by using convolutional neural networks","volume":"284","author":"Lakhani","year":"2017","journal-title":"Radiology"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e172","DOI":"10.1016\/S2589-7500(19)30085-8","article-title":"Detection of glaucomatous optic neuropathy with spectral-domain optical coherence tomography: A retrospective training and validation deep-learning analysis","volume":"1","author":"Ran","year":"2019","journal-title":"Lancet Digit. Health"},{"key":"ref_20","first-page":"264","article-title":"Promising artificial intelligence-machine learning-deep learning algorithms in ophthalmology","volume":"8","author":"Balyen","year":"2019","journal-title":"Asia-Pac. J. Ophthalmol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2211","DOI":"10.1001\/jama.2017.18152","article-title":"Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes","volume":"318","author":"Ting","year":"2017","journal-title":"JAMA"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1533","DOI":"10.1016\/j.ophtha.2019.06.005","article-title":"A deep learning approach for automated detection of geographic atrophy from color fundus photographs","volume":"126","author":"Keenan","year":"2019","journal-title":"Ophthalmology"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1687","DOI":"10.1056\/NEJMoa1917130","article-title":"Artificial intelligence to detect papilledema from ocular fundus photographs","volume":"382","author":"Milea","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Owais, M., Arsalan, M., Choi, J., and Park, K.R. (2019). Effective diagnosis and treatment through content-based medical image retrieval (CBMIR) by using artificial intelligence. J. Clin. Med., 8.","DOI":"10.3390\/jcm8040462"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s12886-018-0778-2","article-title":"Assessment of capillary dropout in the superficial retinal capillary plexus by optical coherence tomography angiography in the early stage of diabetic retinopathy","volume":"18","author":"Shen","year":"2018","journal-title":"BMC Ophthalmol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1167\/tvst.9.2.20","article-title":"Ensemble deep learning for diabetic retinopathy detection using optical coherence tomography angiography","volume":"9","author":"Heisler","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.compbiomed.2017.08.008","article-title":"Automatic blood vessels segmentation based on different retinal maps from OCTA scans","volume":"89","author":"Eladawi","year":"2017","journal-title":"Comput. Biol. Med."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1167\/tvst.9.2.35","article-title":"Transfer learning for automated OCTA detection of diabetic retinopathy","volume":"9","author":"Le","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Nagasato, D., Tabuchi, H., Masumoto, H., Enno, H., Ishitobi, N., Kameoka, M., Niki, M., and Mitamura, Y. (2019). Automated detection of a nonperfusion area caused by retinal vein occlusion in optical coherence tomography angiography images using deep learning. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0223965"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"5249","DOI":"10.1364\/BOE.399514","article-title":"AV-Net: Deep learning for fully automated artery-vein classification in optical coherence tomography angiography","volume":"11","author":"Alam","year":"2020","journal-title":"Biomed. Opt. Express"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"D\u00edaz, M., Novo, J., Cutr\u00edn, P., G\u00f3mez-Ulla, F., Penedo, M.G., and Ortega, M. (2019). Automatic segmentation of the foveal avascular zone in ophthalmological OCT-A images. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0212364"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2103","DOI":"10.1007\/s00417-021-05099-y","article-title":"Quantification of retinal microvascular parameters by severity of diabetic retinopathy using wide-field swept-source optical coherence tomography angiography","volume":"259","author":"Kim","year":"2021","journal-title":"Graefe\u2019s Arch. Clin. Exp. Ophthalmol."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ong, J.X., Kwan, C.C., Cicinelli, M.V., and Fawzi, A.A. (2020). Superficial capillary perfusion on optical coherence tomography angiography differentiates moderate and severe nonproliferative diabetic retinopathy. PLoS ONE, 15.","DOI":"10.1371\/journal.pone.0240064"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1097\/IAE.0000000000002403","article-title":"Quantification of retinal capillary nonperfusion in diabetics using wide-field optical coherence tomography angiography","volume":"40","author":"Alibhai","year":"2020","journal-title":"Retina"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Iwanami, T., Goto, T., Hirano, S., and Sakurai, M. (2012, January 12\u201315). An adaptive contrast enhancement using regional dynamic histogram equalization. Proceedings of the 2012 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE.2012.6162054"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1167\/iovs.11-8249","article-title":"Noninvasive imaging of the foveal avascular zone with high-speed, phase-variance optical coherence tomography","volume":"53","author":"Kim","year":"2012","journal-title":"Investig. Ophthalmol. Vis. Sci."},{"key":"ref_37","first-page":"2016","article-title":"Accuracy, precision, recall & f1 score: Interpretation of performance measures","volume":"1","author":"Joshi","year":"2016","journal-title":"Retr. April"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Das, H., Pattnaik, P.K., Rautaray, S.S., and Li, K.C. (2020). Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019, Springer.","DOI":"10.1007\/978-981-15-2414-1"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., and Batra, D. (2017, January 22\u201329). Grad-cam: Visual explanations from deep networks via gradient-based localization. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2342\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:38:41Z","timestamp":1760135921000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/6\/2342"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":39,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["s22062342"],"URL":"https:\/\/doi.org\/10.3390\/s22062342","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,18]]}}}