{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:37:11Z","timestamp":1774881431516,"version":"3.50.1"},"reference-count":139,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002261","name":"Russian Foundation for Basic Research","doi-asserted-by":"publisher","award":["19-29-01135"],"award-info":[{"award-number":["19-29-01135"]}],"id":[{"id":"10.13039\/501100002261","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>In this article, the development of a computer system for high-tech medical uses in ophthalmology is proposed. An overview of the main methods and algorithms that formed the basis of the coagulation plan planning system is presented. The system provides the formation of a more effective plan for laser coagulation in comparison with the use of existing coagulation techniques. An analysis of monopulse- and pattern-based laser coagulation techniques in the treatment of diabetic retinopathy has shown that modern treatment methods do not provide the required efficacy of medical laser coagulation procedures, as the laser energy is nonuniformly distributed across the pigment epithelium and may exert an excessive effect on parts of the retina and anatomical elements. The analysis has shown that the efficacy of retinal laser coagulation for the treatment of diabetic retinopathy is determined by the relative position of coagulates and parameters of laser exposure. In the course of the development of the computer system proposed herein, main stages of processing diagnostic data were identified. They are as follows: the allocation of the laser exposure zone, the evaluation of laser pulse parameters that would be safe for the fundus, mapping a coagulation plan in the laser exposure zone, followed by the analysis of the generated plan for predicting the therapeutic effect. In the course of the study, it was found that the developed algorithms for placing coagulates in the area of laser exposure provide a more uniform distribution of laser energy across the pigment epithelium when compared to monopulse- and pattern-based laser coagulation techniques.<\/jats:p>","DOI":"10.3390\/sym15020287","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T02:43:29Z","timestamp":1674182609000},"page":"287","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Development of a Computer System for Automatically Generating a Laser Photocoagulation Plan to Improve the Retinal Coagulation Quality in the Treatment of Diabetic Retinopathy"],"prefix":"10.3390","volume":"15","author":[{"given":"Nataly","family":"Ilyasova","sequence":"first","affiliation":[{"name":"Image Processing Systems Institute of the Russian Academy of Sciences, Branch of the Federal Scientific Research Centre \u201cCrystallography and Photonics\u201d RAS, Molodogvardeyskay, 151, 443001 Samara, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikita","family":"Demin","sequence":"additional","affiliation":[{"name":"Image Processing Systems Institute of the Russian Academy of Sciences, Branch of the Federal Scientific Research Centre \u201cCrystallography and Photonics\u201d RAS, Molodogvardeyskay, 151, 443001 Samara, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0735-7697","authenticated-orcid":false,"given":"Nikita","family":"Andriyanov","sequence":"additional","affiliation":[{"name":"Data Analysis and Machine Learning Department, Financial University under the Government of the Russian Federation, Leningradsky pr-t, 49, 125167 Moscow, Russia"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","first-page":"1150","article-title":"Artificial intelligence: Reinforcing the place of humans in our healthcare system","volume":"68","author":"Rottier","year":"2018","journal-title":"Rev. Prat."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.jormas.2019.06.002","article-title":"Deep Learning in Medical Image Analysis: A third eye for doctors","volume":"120","author":"Fourcade","year":"2019","journal-title":"J. Stomatol. Oral Maxillofac. Surg."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bodapati, J.D., Naralasetti, V., Shareef, S.N., Hakak, S., Bilal, M., Maddikunta, P.K., and Jo, O. (2020). Blended multi-modal deep convnet features for diabetic retinopathy severity prediction. Electronics, 9.","DOI":"10.3390\/electronics9060914"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Budai, A., Bock, R., Maier, A., Hornegger, J., and Michelson, G. (2013). Robust vessel segmentation in fundus images. Int. J. Biomed. Imaging, 2013.","DOI":"10.1155\/2013\/154860"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","article-title":"Identifying medical diagnoses and treatable diseases by image-based Deep Learning","volume":"172","author":"Kermany","year":"2018","journal-title":"Cell"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Ojie, O.D., and Saatchi, R. (2021). Kohonen Neural Network Investigation of the Effects of the Visual, Proprioceptive and Vestibular Systems to Balance in Young Healthy Adult Subjects. Healthcare, 9.","DOI":"10.3390\/healthcare9091219"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ahsan, M.M., Nazim, R., Siddique, Z., and Huebner, P. (2021). Detection of COVID-19 Patients from CT Scan and Chest X-ray Data Using Modified MobileNetV2 and LIME. Healthcare, 9.","DOI":"10.3390\/healthcare9091099"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Eckstein, J., Moghadasi, N., K\u00f6rperich, H., Weise Vald\u00e9s, E., Sciacca, V., Paluszkiewicz, L., Burchert, W., and Piran, M. (2022). A Machine Learning Challenge: Detection of Cardiac Amyloidosis Based on Bi-Atrial and Right Ventricular Strain and Cardiac Function. Diagnostics, 12.","DOI":"10.3390\/diagnostics12112693"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Lee, J.H., Lee, J., Cho, S., Song, J.E., Lee, M., Kim, S.H., Lee, J.Y., Shin, D.H., Kim, J.M., and Bae, J.H. (2021). Development of Decision Support Software for deep learning-based automated retinal disease screening using relatively limited fundus photograph data. Electronics, 10.","DOI":"10.3390\/electronics10020163"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ghani, A., See, C., Sudhakaran, V., Ahmad, J., and Abd-Alhameed, R. (2019). Accelerating retinal fundus image classification using artificial neural networks (Anns) and reconfigurable hardware (FPGA). Electronics, 8.","DOI":"10.3390\/electronics8121522"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1126\/scirobotics.abf1462","article-title":"Progress in robotics for Combating Infectious Diseases","volume":"6","author":"Gao","year":"2021","journal-title":"Sci. Robot."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.nima.2012.09.006","article-title":"A software tool for automatic classification and segmentation of 2D\/3D Medical Images","volume":"702","author":"Strzelecki","year":"2013","journal-title":"Nucl. Instrum. Methods Phys. Res. A Accel. Spectrom. Detect. Assoc. Equip."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Jamil, M.F., Pokharel, M., and Park, K. (2022). Light-Controlled Microbots in Biomedical Application: A Review. Appl. Sci., 12.","DOI":"10.3390\/app122111013"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.yaoo.2021.04.012","article-title":"Artificial Intelligence in retina","volume":"6","author":"Trinh","year":"2021","journal-title":"Adv. Ophthalmol. Optom."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Luo, L., Xue, D., and Feng, X. (2020). Automatic Diabetic Retinopathy Grading via Self-Knowledge Distillation. Electronics, 9.","DOI":"10.3390\/electronics9091337"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Wang, R., Miao, Z., Liu, T., Liu, M., Grdinovac, K., Song, X., Liang, Y., Delen, D., and Paiva, W. (2021). Derivation and Validation of Essential Predictors and Risk Index for Early Detection of Diabetic Retinopathy Using Electronic Health Records. J. Clin. Med., 10.","DOI":"10.3390\/jcm10071473"},{"key":"ref_17","first-page":"18","article-title":"Diabetic retinopathy in patients with type 2 Diabetes Mellitus. Epidemiology, a modern view of pathogenesis","volume":"9","author":"Vorobieva","year":"2012","journal-title":"Ophthalmology"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Elgafi, M., Sharafeldeen, A., Elnakib, A., Elgarayhi, A., Alghamdi, N.S., Sallah, M., and El-Baz, A. (2022). Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images. Sensors, 22.","DOI":"10.3390\/s22207833"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ansari, P., Tabasumma, N., Snigdha, N.N., Siam, N.H., Panduru, R.V.N.R.S., Azam, S., Hannan, J.M.A., and Abdel-Wahab, Y.H.A. (2022). Diabetic Retinopathy: An Overview on Mechanisms, Pathophysiology and Pharmacotherapy. Diabetology, 3.","DOI":"10.3390\/diabetology3010011"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.diabres.2013.11.002","article-title":"Global estimates of diabetes prevalence for 2013 and projections for 2035","volume":"103","author":"Guariguata","year":"2014","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1016\/S2213-8587(16)30052-3","article-title":"Diabetic macular edema","volume":"5","author":"Tan","year":"2017","journal-title":"Lancet Diab. Endoc."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Amjad, R., Lee, C.-A., Farooqi, H.M.U., Khan, H., and Paeng, D.-G. (2022). Choroidal Thickness in Different Patterns of Diabetic Macular Edema. J. Clin. Med., 11.","DOI":"10.3390\/jcm11206169"},{"key":"ref_23","first-page":"33","article-title":"On early diagnostics and the occurence rate of diabetic macular edema and identification of diabetes risk groups","volume":"35","author":"Bratko","year":"2015","journal-title":"Sib. Sci. Med. J."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.1016\/S0161-6420(84)34102-1","article-title":"The Wisconsin Epidemiologic Study of Diabetic Retinopathy IV. Diabetic Macular Edema","volume":"91","author":"Klein","year":"1984","journal-title":"Ophthalmology"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"101138","DOI":"10.1016\/j.disamonth.2021.101138","article-title":"Diabetic macular edema","volume":"67","author":"Ixcamey","year":"2021","journal-title":"Dis.-A-Mon."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"70","DOI":"10.17750\/KMJ2015-070","article-title":"Diabetic macular edema: Epidemiology, pathogenesis, diagnosis, clinical presentation, and treatment","volume":"96","author":"Amirov","year":"2015","journal-title":"Kazan Med. J."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Hamad, H., Dwickat, T., Tegolo, D., and Valenti, C. (2021). Exudates as Landmarks Identified through FCM Clustering in Retinal Images. Appl. Sci., 11.","DOI":"10.3390\/app11010142"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1016\/j.sjopt.2014.09.001","article-title":"Modern retinal laser therapy","volume":"29","author":"Kozak","year":"2015","journal-title":"Saudi J. Ophthalmol."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Frizziero, L., Calciati, A., Torresin, T., Midena, G., Parrozzani, R., Pilotto, E., and Midena, E. (2021). Diabetic Macular Edema Treated with 577-nm Subthreshold Micropulse Laser: A Real-Life, Long-Term Study. J. Pers. Med., 11.","DOI":"10.3390\/jpm11050405"},{"key":"ref_30","first-page":"43","article-title":"The effectiveness of laser coagulation in the macula and high-density microphotocoagulation in the treatment of diabetic ma\u0431culopathy","volume":"9","author":"Kotsur","year":"2016","journal-title":"Ophthalmol. Statements"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Artemov, S., Belyaev, A., Bushukina, O., Khrushchalina, S., Kostin, S., Lyapin, A., Ryabochkina, P., and Taratynova, A. (2019, January 15\u201320). Endovenous laser coagulation using two-micron laser radiation: Mathematical modeling and in vivo experiments. Proceedings of the International Conference on Advanced Laser Technologies (ALT), Prague, Czech Republic.","DOI":"10.1109\/ICLO48556.2020.9285909"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Bianco, L., Gaw\u0119cki, M., Antropoli, A., Arrigo, A., Bandello, F., and Battaglia Parodi, M. (2022). Laser Treatment for Retinal Arterial Macroaneurysm. Photonics, 9.","DOI":"10.3390\/photonics9110851"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xie, X., Liu, Q., and Paulus, Y.M. (2022). Optical Coherence Tomography Following Panretinal Photocoagulation Demonstrating Choroidal Detachment. Photonics, 9.","DOI":"10.3390\/photonics9100730"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Zhang, J., Zhang, J., Zhang, C., Zhang, J., Gu, L., Luo, D., and Qiu, Q. (2022). Diabetic Macular Edema: Current Understanding, Molecular Mechanisms and Therapeutic Implications. Cells, 11.","DOI":"10.3390\/cells11213362"},{"key":"ref_35","first-page":"375","article-title":"Analysis of the coagulates intensity in laser treatment of diabetic macular edema in a Navilas robotic laser system","volume":"13","author":"Zamytsky","year":"2017","journal-title":"Saratov J. Med. Sci. Res."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Grzybowski, A., Markeviciute, A., and Zemaitiene, R. (2021). Treatment of Macular Edema in Vascular Retinal Diseases: A 2021 Update. J. Clin. Med., 10.","DOI":"10.3390\/jcm10225300"},{"key":"ref_37","first-page":"92","article-title":"Features of the use of lasers in medicine","volume":"3","author":"Gafurov","year":"2019","journal-title":"Eur. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Da Pozzo, S., Iacono, P., Arrigo, A., and Battaglia Parodi, M. (2021). The Role of Imaging in Planning Treatment for Central Serous Chorioretinopathy. Pharmaceuticals, 14.","DOI":"10.3390\/ph14020105"},{"key":"ref_39","first-page":"1","article-title":"Different lasers and techniques for proliferative diabetic retinopathy","volume":"3(CD012314)","author":"Moutray","year":"2018","journal-title":"Cochrane Database Syst. Rev."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Frizziero, L., Calciati, A., Midena, G., Torresin, T., Parrozzani, R., Pilotto, E., and Midena, E. (2021). Subthreshold Micropulse Laser Modulates Retinal Neuroinflammatory Biomarkers in Diabetic Macular Edema. J. Clin. Med., 10.","DOI":"10.3390\/jcm10143134"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"48","DOI":"10.2174\/1874364101307010048","article-title":"Navilas laser system focal laser treatment for diabetic macular edema\u2014One-year results of a case series","volume":"7","author":"Jung","year":"2013","journal-title":"Open Ophthalmol. J."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Toto, L., D\u2019Aloisio, R., Quarta, A., Libertini, D., D\u2019Onofrio, G., De Nicola, C., Romano, A., and Mastropasqua, R. (2022). Intravitreal Dexamethasone Implant (IDI) Alone and Combined with Navigated 577 nm Subthreshold Micropulse Laser (SML) for Diabetic Macular Oedema. J. Clin. Med., 11.","DOI":"10.3390\/jcm11175200"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Chauhan, M.Z., Rather, P.A., Samarah, S.M., Elhusseiny, A.M., and Sallam, A.B. (2022). Current and Novel Therapeutic Approaches for Treatment of Diabetic Macular Edema. Cells, 11.","DOI":"10.3390\/cells11121950"},{"key":"ref_44","first-page":"1097","article-title":"Comprehensive treatment of diabetic macular edema","volume":"19","author":"Velichko","year":"2014","journal-title":"Bull. Russ. Univ. Math."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Al Zabadi, H., Taha, I., and Zagha, R. (2022). Clinical and Molecular Characteristics of Diabetic Retinopathy and Its Severity Complications among Diabetic Patients: A Multicenter Cross-Sectional Study. J. Clin. Med., 11.","DOI":"10.3390\/jcm11143945"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Tomita, Y., Lee, D., Tsubota, K., Negishi, K., and Kurihara, T. (2021). Updates on the Current Treatments for Diabetic Retinopathy and Possibility of Future Oral Therapy. J. Clin. Med., 10.","DOI":"10.3390\/jcm10204666"},{"key":"ref_47","first-page":"1105","article-title":"The effectiveness of classical and pattern laser coagulation in diabetic retinopathy. Bulletin of Russian Universities","volume":"19","author":"Goidin","year":"2014","journal-title":"Mathematics"},{"key":"ref_48","unstructured":"Zavgorodnya, N.G., Bezugly, M.B., Bezugly, B.S., and Sarzhevskaya, L.E. (2015). The use of lasers in ophthalmology: A manual for interns in the specialty \u201cOphthalmology\u201d. Zaporozhye ZSMU."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Miura, Y., Inagaki, K., Hutfilz, A., Seifert, E., Schmarbeck, B., Murakami, A., Ohkoshi, K., and Brinkmann, R. (2022). Temperature Increase and Damage Extent at Retinal Pigment Epithelium Compared between Continuous Wave and Micropulse Laser Application. Life, 12.","DOI":"10.3390\/life12091313"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Danielescu, C., Moraru, A.D., Anton, N., Bilha, M.-I., Donica, V.-C., Darabus, D.-M., Munteanu, M., and Stefanescu-Dima, A.S. (2023). The Learning Curve of Surgery of Diabetic Tractional Retinal Detachment\u2014A Retrospective, Comparative Study. Medicina, 59.","DOI":"10.3390\/medicina59010073"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"45","DOI":"10.14341\/2072-0351-5997","article-title":"Modern algorithm for laser coagulation of the retina in diabetic retinopathy","volume":"10","author":"Lipatov","year":"2007","journal-title":"Diabetes Mellit."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Dong, J., Li, Q., Wang, X., and Fan, Y. (2022). A Review of the Methods of Non-Invasive Assessment of Intracranial Pressure through Ocular Measurement. Bioengineering, 9.","DOI":"10.3390\/bioengineering9070304"},{"key":"ref_53","first-page":"175","article-title":"Temperature Distribution Simulation of the Human Eye Exposed to Laser Radiation","volume":"4","author":"Abbas","year":"2013","journal-title":"J. Lasers Med. Sci."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Shirokanev, A., Ilyasova, N., Andriyanov, N., Zamytskiy, E., Zolotarev, A., and Kirsh, D. (2021). Modeling of Fundus Laser Exposure for estimating safe laser coagulation parameters in the treatment of diabetic retinopathy. Mathematics, 9.","DOI":"10.3390\/math9090967"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Sabal, B., Teper, S., and Wyl\u0119ga\u0142a, E. (2023). Subthreshold Micropulse Laser for Diabetic Macular Edema: A Review. J. Clin. Med., 12.","DOI":"10.3390\/jcm12010274"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Silviya, S., Anitha, C.M., Prakash, P.S.G., Bahammam, S.A., Bahammam, M.A., Almarghlani, A., Assaggaf, M., Kamil, M.A., Subramanian, S., and Balaji, T.M. (2022). The Efficacy of Low-Level Laser Therapy Combined with Single Flap Periodontal Surgery in the Management of Intrabony Periodontal Defects: A Randomized Controlled Trial. Healthcare, 10.","DOI":"10.3390\/healthcare10071301"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.neucom.2018.10.103","article-title":"Bin Loss for hard exudates segmentation in fundus images","volume":"392","author":"Guo","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.bspc.2017.02.012","article-title":"Multiscale segmentation of exudates in retinal images using contextual cues and ensemble classification","volume":"35","author":"Fraz","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.cmpb.2018.02.011","article-title":"Hard exudates segmentation based on learned initial seeds and iterative graph cut","volume":"158","author":"Kusakunniran","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Fiandono, I., and Firdausy, K. (2018). Median filtering for optic disc segmentation in retinal image. Kinet. Game Technol. Inf. Syst. Comput. Netw. Comput. Electron. Control, 73\u201380.","DOI":"10.22219\/kinetik.v3i1.247"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.cmpb.2018.03.020","article-title":"Macula segmentation and fovea localization employing image processing and heuristic based clustering for automated retinal screening","volume":"160","author":"Ramani","year":"2018","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Jeong, Y., Hong, Y.-J., and Han, J.-H. (2022). Review of Machine Learning Applications Using Retinal Fundus Images. Diagnostics, 12.","DOI":"10.3390\/diagnostics12010134"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Ilyasova, N., Paringer, R., Kupriyanov, A., and Kirsh, D. (2017, January 16\u201318). Intelligent feature selection technique for segmentation of fundus images. Proceedings of the 2017 Seventh International Conference on Innovative Computing Technology (INTECH), Luton, UK.","DOI":"10.1109\/INTECH.2017.8102433"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"102115","DOI":"10.1016\/j.bspc.2020.102115","article-title":"A lightweight CNN for diabetic retinopathy classification from fundus images","volume":"62","author":"Gayathri","year":"2020","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"101749","DOI":"10.1016\/j.artmed.2019.101749","article-title":"A multi-context CNN ensemble for small lesion detection","volume":"103","author":"Savelli","year":"2020","journal-title":"Artif. Intell. Med."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"105788","DOI":"10.1016\/j.cmpb.2020.105788","article-title":"Retinal layer segmentation in rodent OCT images: Local intensity profiles & fully convolutional Neural Networks","volume":"198","author":"Morales","year":"2021","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"101856","DOI":"10.1016\/j.media.2020.101856","article-title":"Structured layer surface segmentation for retina oct using fully convolutional regression networks","volume":"68","author":"He","year":"2021","journal-title":"Med. Image Anal."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Ilyasova, N., Paringer, R., and Kupriyanov, A. (2016). Regions of interest in a fundus image selection technique using the discriminative analysis methods. Comput. Vis. Graph., 408\u2013417.","DOI":"10.1007\/978-3-319-46418-3_36"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"304","DOI":"10.18287\/2412-6179-2019-43-2-304-315","article-title":"Technology of intellectual feature selection for a system of automatic formation of a coagulate plan on Retina","volume":"43","author":"Ilyasova","year":"2019","journal-title":"Comput. Opt."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"012095","DOI":"10.1088\/1742-6596\/1096\/1\/012095","article-title":"A modified technique for smart textural feature selection to extract retinal regions of interest using image pre-processing","volume":"1096","author":"Ilyasova","year":"2018","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"736","DOI":"10.1016\/j.proeng.2017.09.599","article-title":"A smart feature selection technique for object localization in ocular fundus images with the aid of color subspaces","volume":"201","author":"Ilyasova","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"012016","DOI":"10.1088\/1742-6596\/1438\/1\/012016","article-title":"Analysis of convolutional neural network for Fundus Image segmentation","volume":"1438","author":"Shirokanev","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"712","DOI":"10.18287\/2412-6179-2018-42-4-712-721","article-title":"Investigation of algorithms for coagulate arrangement in fundus images","volume":"42","author":"Shirokanev","year":"2018","journal-title":"Comput. Opt."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"271","DOI":"10.1016\/j.proeng.2017.09.623","article-title":"Development of coagulate map formation algorithms to carry out treatment by laser coagulation","volume":"201","author":"Ilyasova","year":"2017","journal-title":"Procedia Eng."},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Ilyasova, N., Shirokanev, A., Paringer, R., and Kupriyanov, A. (2019, January 18\u201320). Biomedical data analysis based on Parallel Programming Technology Application for computation features\u2019 effectiveness. Proceedings of the ICFSP 2019: 5th International Conference on Frontiers of Signal Processing, Marseille, France.","DOI":"10.1109\/ICFSP48124.2019.8938079"},{"key":"ref_76","doi-asserted-by":"crossref","unstructured":"Raku, A., Shirokanev, A., Degtyarev, A., Kibitkina, A., Ilyasova, N., and Zolotarev, A. (2020, January 26\u201329). Study of thermal field of the retina of the human eye in the laser exposure zone during numerical simulation based on the solution of the heat equation in the layered region. Proceedings of the 2020 International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russia.","DOI":"10.1109\/ITNT49337.2020.9253346"},{"key":"ref_77","unstructured":"Shirokanev, A., Degtyaryov, A., Kibitkina, A., Raku, A., and Ilyasova, N. (2020, January 22\u201324). Development of Information Technology for selection of effective strategy of diabetic retinopathy treatment. Proceedings of the 2020 12th International Conference on Bioinformatics and Biomedical Technology, Xi\u2019an, China."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"809","DOI":"10.18287\/2412-6179-CO-760","article-title":"Methods of mathematical modeling of fundus laser exposure for therapeutic effect evaluation","volume":"44","author":"Shirokanev","year":"2020","journal-title":"Comput. Opt."},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Yi, S.-L., Yang, X.-L., Wang, T.-W., She, F.-R., Xiong, X., and He, J.-F. (2021). Diabetic Retinopathy Diagnosis Based on RA-EfficientNet. Appl. Sci., 11.","DOI":"10.3390\/app112211035"},{"key":"ref_80","first-page":"136","article-title":"Estimation of the geometric parameters of the optic disc region on the image of the fundus","volume":"28","author":"Kupriyanov","year":"2005","journal-title":"Comput. Opt."},{"key":"ref_81","first-page":"154","article-title":"Determination of the parameters of the vessel bed using a three-dimensional local fan-shaped transformation","volume":"25","author":"Kupriyanov","year":"2004","journal-title":"Comput. Opt."},{"key":"ref_82","first-page":"165","article-title":"Measurement of biomechanical characteristics of vessels for early diagnosis of vascular pathology of the fundus","volume":"27","author":"Ilyasova","year":"2005","journal-title":"Comput. Opt."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Ilyasova, N., Shirokanev, A., Kirsh, D., Demin, N., Zamytskiy, E., Paringer, R., and Antonov, A. (2021). Identification of prognostic factors and predicting the therapeutic effect of laser photocoagulation for DME treatment. Electronics, 10.","DOI":"10.3390\/electronics10121420"},{"key":"ref_84","first-page":"61","article-title":"Methods of computer analysis of diagnostic images of the fundus","volume":"5","author":"Soifer","year":"2008","journal-title":"Technol. Living Syst."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"511","DOI":"10.18287\/0134-2452-2013-37-4-511-535","article-title":"Methods for digital analysis of human vascular system. literature Review","volume":"37","author":"Ilyasova","year":"2013","journal-title":"Comput. Opt."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/j.dsx.2014.04.015","article-title":"Diagnostic accuracy of direct ophthalmoscopy for detection of diabetic retinopathy using fundus photographs as a reference standard","volume":"8","author":"Ahsan","year":"2014","journal-title":"Diabetes Metab. Syndr. Clin. Res. Rev."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"S242","DOI":"10.1016\/j.bj.2020.11.003","article-title":"MicroRNAs (\u2212146a, \u221221 and \u221234A) are diagnostic and prognostic biomarkers for diabetic retinopathy","volume":"44","author":"Helal","year":"2021","journal-title":"Biomed. J."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"105201","DOI":"10.1016\/j.cmpb.2019.105201","article-title":"Deep multi-instance heatmap regression for the detection of retinal vessel crossings and bifurcations in eye fundus images","volume":"186","author":"Hervella","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"105282","DOI":"10.1016\/j.dib.2020.105282","article-title":"Data on fundus images for vessels segmentation, detection of hypertensive retinopathy, diabetic retinopathy and papilledema","volume":"29","author":"Akram","year":"2020","journal-title":"Data Brief"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.ophtha.2016.11.014","article-title":"Automated Diabetic Retinopathy Image Assessment Software","volume":"124","author":"Tufail","year":"2017","journal-title":"Ophthalmology"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"101724","DOI":"10.1016\/j.media.2020.101724","article-title":"Expert-validated estimation of diagnostic uncertainty for deep neural networks in diabetic retinopathy detection","volume":"64","author":"Ayhan","year":"2020","journal-title":"Med. Image Anal."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.ins.2019.06.011","article-title":"Diagnostic assessment of deep learning algorithms for diabetic retinopathy screening","volume":"501","author":"Li","year":"2019","journal-title":"Inf. Sci."},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.oret.2018.10.014","article-title":"Deep learning\u2013based algorithms in screening of diabetic retinopathy: A systematic review of diagnostic performance","volume":"3","author":"Nielsen","year":"2019","journal-title":"Ophthalmol. Retin."},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Shimada, Y., Shibuya, M., and Shinoda, K. (2021). Transient Increase and Delay of Multifocal Electroretinograms Following Laser Photocoagulations for Diabetic Macular Edema. J. Clin. Med., 10.","DOI":"10.3390\/jcm10020357"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"529","DOI":"10.18287\/0134-2452-2014-38-3-529-538","article-title":"Estimating the geometric features of a 3D vascular structure","volume":"38","author":"Ilyasova","year":"2014","journal-title":"Comput. Opt."},{"key":"ref_96","first-page":"146","article-title":"A method for highlighting the central lines of blood vessels in diagnostic images","volume":"29","author":"Ilyasova","year":"2006","journal-title":"Comput. Opt."},{"key":"ref_97","first-page":"141","article-title":"Evaluation of diagnostic parameters of blood vessels on images of the fundus in the region of the optic nerve head","volume":"29","author":"Kupriyanov","year":"2006","journal-title":"Comput. Opt."},{"key":"ref_98","first-page":"37","article-title":"Digital analysis system for diagnosing vascular pathology of the fundus","volume":"5","author":"Branchevsky","year":"2003","journal-title":"Bull. Ophthalmol."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"316","DOI":"10.1117\/12.302502","article-title":"Methods for estimating geometric parameters of retinal vessels using diagnostic images of Fundus","volume":"3348","author":"Iliasova","year":"1998","journal-title":"SPIE Proc."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1134\/S1054661807040104","article-title":"Estimating directions of optic disk blood vessels in retinal images","volume":"17","author":"Ananin","year":"2007","journal-title":"Pattern Recognit. Image Anal."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.jaapos.2016.05.013","article-title":"The macula in pediatric glaucoma: Quantifying the inner and outer layers via optical coherence tomography automatic segmentation","volume":"20","author":"Silverstein","year":"2016","journal-title":"J. Am. Assoc. Pediatr. Ophthalmol. Strabismus"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1016\/j.bspc.2018.05.028","article-title":"Automatic detection of peripapillary atrophy in retinal fundus images using statistical features","volume":"45","author":"Septiarini","year":"2018","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.compmedimag.2014.05.005","article-title":"Thickness related textural properties of retinal nerve fiber layer in color fundus images","volume":"38","author":"Odstrcilik","year":"2014","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"102597","DOI":"10.1016\/j.jvcir.2019.102597","article-title":"An hybrid feature space from texture information and transfer learning for glaucoma classification","volume":"64","author":"Claro","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.procs.2021.04.170","article-title":"Analysis of the impact of visual attacks on the characteristics of neural networks in image recognition","volume":"186","author":"Andriyanov","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Andriyanov, N. (2021). Methods for Preventing Visual Attacks in Convolutional Neural Networks Based on Data Discard and Dimensionality Reduction. Appl. Sci., 11.","DOI":"10.3390\/app11115235"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"596","DOI":"10.18287\/2412-6179-CO-1010","article-title":"Neural network application for semantic segmentation of Fundus","volume":"46","author":"Paringer","year":"2022","journal-title":"Comput. Opt."},{"key":"ref_108","first-page":"51","article-title":"Modern aspects of diagnosis and treatment of diabetic macular edema","volume":"4","author":"Doga","year":"2014","journal-title":"Ophthalmol. Diabetes"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"104","DOI":"10.14341\/DM2004116-17","article-title":"The prevalence of type 2 diabetes mellitus in the adult population of Russia (nation study)","volume":"19","author":"Dedov","year":"2016","journal-title":"Diabetes Mellit."},{"key":"ref_110","first-page":"59","article-title":"Modern approaches to the treatment of diabetic macular edema","volume":"4","author":"Astakhov","year":"2009","journal-title":"Ophthalmol. Statements"},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"692","DOI":"10.1007\/s00347-012-2559-2","article-title":"Navigated focal retinal laser therapy using the NAVILAS\u00ae system for diabetic macula edema","volume":"109","author":"Kernt","year":"2012","journal-title":"Ophthalmologe"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"e392","DOI":"10.1016\/S1569-9056(19)30294-5","article-title":"Neodynium\/YAG-laser coagulation of urinary tract haemangiomas causing macroscopic haematuria, 5 to 10-years follow-up","volume":"18","author":"Brehmer","year":"2019","journal-title":"Eur. Urol. Suppl."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"1220","DOI":"10.1016\/j.ijheatmasstransfer.2017.07.033","article-title":"An integral mps model of blood coagulation by laser irradiation: Application to the optimization of multi-pulse nd:YAG laser treatment of Port-Wine Stains","volume":"114","author":"Xiang","year":"2017","journal-title":"Int. J. Heat Mass Transf."},{"key":"ref_114","first-page":"196","article-title":"Effects of laser wavelengths on experimental retinal detachments and retinal vessels","volume":"32","author":"Katoh","year":"1988","journal-title":"Jpn. J. Ophthalmol."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1016\/j.future.2020.07.048","article-title":"Human\u2013computer interaction-based decision support system with applications in Data Mining","volume":"114","author":"Yun","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"100987","DOI":"10.1016\/j.cola.2020.100987","article-title":"Lavoisier: A DSL for increasing the level of abstraction of data selection and formatting in data mining","volume":"60","author":"Vega","year":"2020","journal-title":"J. Comput. Lang."},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.isatra.2018.10.016","article-title":"Fault detection in dynamic plant-wide process by multi-block slow feature analysis and support vector data description","volume":"85","author":"Huang","year":"2019","journal-title":"ISA Trans."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"113064","DOI":"10.1016\/j.dss.2019.05.004","article-title":"Feature assessment and ranking for classification with nonlinear sparse representation and approximate dependence analysis","volume":"122","author":"Zhang","year":"2019","journal-title":"Decis. Support Syst."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"103886","DOI":"10.1016\/j.chemolab.2019.103886","article-title":"Determination of the effect of red blood cell parameters in the discrimination of iron deficiency anemia and beta thalassemia via neighborhood component analysis feature selection-based machine learning","volume":"196","year":"2020","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"163368","DOI":"10.1016\/j.ijleo.2019.163368","article-title":"A robust geometric mean-based subspace discriminant analysis feature extraction approach for image set classification","volume":"199","author":"Gao","year":"2019","journal-title":"Optik"},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1007\/978-981-19-3444-5_15","article-title":"Automated System for the Personalization of Retinal Laser Treatment in Diabetic Retinopathy Based on the Intelligent Analysis of OCT Data and Fundus Images","volume":"Volume 309","author":"Czarnowski","year":"2022","journal-title":"Intelligent Decision Technologies. Smart Innovation, Systems and Technologies"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/978-3-030-55187-2_34","article-title":"Detailed Clustering Based on Gaussian Mixture Models. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Systems and Applications","volume":"1251","author":"Andriyanov","year":"2021","journal-title":"Adv. Intell. Syst. Comput."},{"key":"ref_123","doi-asserted-by":"crossref","first-page":"6710","DOI":"10.1167\/iovs.09-5064","article-title":"A systematic correlation between morphology and functional alterations in diabetic macular edema","volume":"51","author":"Bolz","year":"2010","journal-title":"Investig. Opthalmol. Vis. Sci."},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"309","DOI":"10.3103\/S1060992X15040037","article-title":"The discriminant analysis application to refine the diagnostic features of blood vessels images","volume":"24","author":"Ilyasova","year":"2015","journal-title":"Opt. Mem. Neural Netw."},{"key":"ref_125","unstructured":"Ilyasova, N.Y., Kupriyanov, A.V., and Khramov, A.G. (2012). Information technologies of image analysis in the problems of medical diagnostics. Radio Commun."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"511","DOI":"10.1016\/j.ejrad.2015.12.009","article-title":"Using texture analyses of contrast enhanced CT to assess hepatic fibrosis","volume":"85","author":"Daginawala","year":"2016","journal-title":"Eur. J. Radiol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"2011","DOI":"10.1007\/s10916-011-9663-8","article-title":"An integrated index for the identification of diabetic retinopathy stages using texture parameters","volume":"36","author":"Acharya","year":"2011","journal-title":"J. Med. Syst."},{"key":"ref_128","doi-asserted-by":"crossref","unstructured":"Gentillon, H., Stefa\u0144czyk, L., Strzelecki, M., and Respondek-Liberska, M. (2016). Parameter set for computer-assisted texture analysis of fetal brain. BMC Res. Notes, 9.","DOI":"10.1186\/s13104-016-2300-3"},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1016\/j.cmpb.2008.08.005","article-title":"Mazda\u2014A software package for image texture analysis","volume":"94","author":"Strzelecki","year":"2009","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_130","doi-asserted-by":"crossref","unstructured":"Ilyasova, N., Demin, N., Shirokanev, A., and Paringer, R. (2020, January 26\u201329). Fundus image segmentation using decision trees. Proceedings of the 2020 International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russia.","DOI":"10.1109\/ITNT49337.2020.9253229"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"052006","DOI":"10.1088\/1742-6596\/1368\/5\/052006","article-title":"CUDA parallel programming technology application for analysis of big biomedical data based on computation of effectiveness features","volume":"1368","author":"Ilyasova","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_132","doi-asserted-by":"crossref","unstructured":"Ilyasova, N., Shirokanev, A., Klimov, I., and Paringer, R. (2019). Convolutional neural network application for analysis of Fundus Images. International Conference on Intelligent Information Technologies for Industry, Springer.","DOI":"10.1007\/978-3-030-50097-9_7"},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"250","DOI":"10.18287\/2412-6179-CO-691","article-title":"Method for selection macular edema region using optical coherence tomography data","volume":"44","author":"Ilyasova","year":"2020","journal-title":"Comput. Opt."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"012006","DOI":"10.1088\/1742-6596\/1353\/1\/012006","article-title":"Determination of borders between objects on satellite images using a two-proof doubly stochastic filtration","volume":"1353","author":"Andriyanov","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"58","DOI":"10.32364\/2311-7729-2021-21-2-58-62","article-title":"Comparative quantitative assessment of the placement and intensity of laser spots for treating diabetic macular edema","volume":"21","author":"Zamytskiy","year":"2021","journal-title":"Russ. J. Clin. Ophthalmol."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"139","DOI":"10.18287\/2412-6179-CO-922","article-title":"Detection of objects in the images: From likelihood relationships towards scalable and efficient neural networks","volume":"46","author":"Andriyanov","year":"2022","journal-title":"Comput. Opt."},{"key":"ref_137","doi-asserted-by":"crossref","unstructured":"Andriyanov, N.A. (2020, January 1\u20133). Analysis of the Acceleration of Neural Networks Inference on Intel Processors Based on OpenVINO Toolkit. Proceedings of the 2020 Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), Svetlogorsk, Russia.","DOI":"10.1109\/SYNCHROINFO49631.2020.9166067"},{"key":"ref_138","doi-asserted-by":"crossref","unstructured":"Andriyanov, N., and Papakostas, G. (2022, January 23\u201327). Optimization and Benchmarking of Convolutional Networks with Quantization and OpenVINO in Baggage Image Recognition. Proceedings of the 2022 VIII International Conference on Information Technology and Nanotechnology (ITNT), Samara, Russia.","DOI":"10.1109\/ITNT55410.2022.9848757"},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1134\/S1054661822030038","article-title":"Development of a Productive Transport Detection System Using Convolutional Neural Networks","volume":"32","author":"Andriyanov","year":"2022","journal-title":"Pattern Recognit. Image Anal."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/2\/287\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:11:34Z","timestamp":1760119894000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/15\/2\/287"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,20]]},"references-count":139,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["sym15020287"],"URL":"https:\/\/doi.org\/10.3390\/sym15020287","relation":{},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,20]]}}}