Decision-making support system for the personalization of retinal laser treatment in diabetic retinopathy

Decision-making support system for the personalization of retinal laser treatment in diabetic retinopathy

Автор: Ilyasova Nataly Yurievna, Kirsh Dmitriy Victorovich, Demin Nikita Sergeevich

Журнал: Компьютерная оптика @computer-optics

Рубрика: Обработка изображений, распознавание образов

Статья в выпуске: 5 т.46, 2022 года.

Бесплатный доступ

The purpose of research to create automated personalization of diabetic macular edema laser treatment. The results are based on analysis of large semi-structured data, methods and algorithms for fundus image processing. The technology improves the quality of retina laser coagulation in the treatment of diabetic macular edema, which is one of the main reasons for pronounced vision decrease. The proposed technology includes original solutions to establish an optimal localization of multitude burns by determining zones exposed to laser. It also includes the recognition of large amount of unstructured data on the anatomical and pathological locations' structures in the area of edema and data optical coherent tomography. As a result, a uniform laser application on the pigment epithelium of the affected retina is ensured. It will increase the treatment safety and its effectiveness, thus avoiding the use of more expensive treatment methods. Assessment of retinal lesions volume and quality will allow predicting the laser photocoagulation results and will contribute to the improvement of laser surgeon's skills. The architecture of a software complex comprises a number of modules, including image processing methods, algorithms for photocoagulation pattern mapping, and intelligent analysis methods.

Еще

Fundus, laser coagulation, diabetic retinopathy, image processing, segmentation, classification

Короткий адрес: https://sciup.org/140296223

IDR: 140296223   |   DOI: 10.18287/2412-6179-CO-1129

Список литературы Decision-making support system for the personalization of retinal laser treatment in diabetic retinopathy

  • Rottier JB. Artificial intelligence: reinforcing the place of humans in our healthcare system. La Revue du Praticien 2018; 68(10): 1150-1151.
  • Fourcade A, Khonsari RH. Deep learning in medical image analysis: A third eye for doctors. J Stomatol Oral Maxillo-fac Surg 2019; 120(4): 279-288.
  • Gao A. Progress in robotics for combating infectious diseases. Science Robotics 2021; 6(52): 1-17.
  • Trinh M, Ghassibi M, Lieberman R. Artificial Intelligence in retina. Adv Ophthalmol Optom 2021; 6: 175-185.
  • Vorobieva IV, Merkushenkova DA. Diabetic retinopathy in patients with type 2 Diabetes Mellitus. Epidemiology, a modern view of pathogenesis. Ophthalmology 2012; 9(4): 18-21.
  • Dedov II, Shestakova MV, Galstyan GR. Prevalence of type 2 Diabetes Mellitus in the adult population of Russia (NATION study). Diabetes Mellitus 2016; 19(2): 104-112.
  • Tan GS, Cheung N, Simo R. Diabetic macular edema. Lancet Diab Endoc 2017; 5: 143-155.
  • Amirov AN, Abdulaeva EA, Minkhuzina EL. Diabetic macular edema: Epidemiology, pathogenesis, diagnosis, clinical presentation, and treatment. Kazan Medical Journal 2015; 96(1): 70-74.
  • Doga AV, Kachalina GF, Pedanova EK, Buryakov DA. Modern diagnostic and treatment aspects of diabetic macular edema. Ophthalmology Diabetes 2014; 4: 51-59.
  • Bratko GV, Chernykh VV, Sazonova OV. On early diagnostics and the occurence rate of diabetic macular edema and identification of diabetes risk groups. Siberian Scientific Medical Journal 2015; 35(1): 33-36.
  • Wong TY, Liew G, Tapp RJ. Relation between fasting glucose and retinopathy for diagnosis of diabetes: three population-based cross-sectional studies. Lancet 2008; 371(9614): 736-743.
  • Acharya UR, Ng EY, Tan JH, Sree SV, Ng KH. An integrated index for the identification of diabetic retinopathy stages using texture parameters. J Med Syst 2012; 36(3): 2011-2020.
  • Astakhov YuS, Shadrichev FE, Krasavina MI, Grigorieva NN. Modern approaches to the treatment of diabetic macular edema. Ophthalmological Statements 2009; 4: 59-69.
  • Zamytsky EA, Zolotarev AV, Karlova EV, Zamytsky PA. Analysis of the coagulates intensity in laser treatment of diabetic macular edema in a Navilas robotic laser system. Saratov Journal of Medical Scientific Research 2017; 13(2): 375-378.
  • Zamytskiy EA, Zolotarev AV, Karlova EV. Comparative quantitative assessment of the placement and intensity of laser spots for treating diabetic macular edema. Russian Journal of Clinical Ophthalmology 2021; 21(2): 58-62.
  • Kotsur TV, Izmailov AS. The effectiveness of laser coagulation in the macula and high-density microphotocoagula-tion in the treatment of diabetic maculopathy. Ophthalmo-logical Statements 2016; 9(4): 43-45.
  • Kozak I, Luttrull J. Modern retinal laser therapy. Saudi J Ophthalmol 2014; 29(2) 137-146.
  • Kernt M, Cheuteu R, Liegl R. Navigated focal retinal laser therapy using the NAVILAS® system for diabetic macula edema. Ophthalmologe 2012; 109: 692-700.
  • Ober MD. Time required for navigated macular laser photo coagulation treatment with the Navilas®. Graefes Arch Clin Exp Ophthalmol 2013; 251(4): 1049-1053.
  • Syeda AM, Hassanb T, Akramc MU, Nazc S, Khalid S. Automated diagnosis of macular edema and central serous retinopathy through robust reconstruction of 3D retinal surfaces. Comput Methods Programs Biomed 2016; 137: 1-10.
  • Chhablani J, Kozak I, Barteselli G, Oman Sel-E. A novel navigated laser system brings new efficacy to the treatment of retinovascular disorders. J Ophthalmol 2013; 6(1): 18-22.
  • Odstrcilik J, Kolar R, Tornow RP, Jan J, Budai A, Mayer M, Vodakova M, Laemmer R, Lamos M, Kuna Z, Gazarek J, Kubena T, Cernosek P, Ronzhina M. Thickness related textural properties of retinal nerve fiber layer in color fundus images. Comput Med Imaging Graph 2014; 38(6): 508-516.
  • HeiShun Yu, Tischler B, Qureshi MM, Soto JA, Anderson S, Daginawala N, Li B, Buch K. Using texture analyses of contrast enhanced CT to assess hepatic fibrosis. Eur J Radiol 2016; 85(3): 511-517.
  • Ilyasova N, Paringer R, Kupriyanov A. Intelligent feature selection technique for segmentation of fundus images. 7th Int Conf on Innovative Computing Technology 2017; 138143.
  • Anan'in MA, Ilyasova NYu, Kupriyanov AV. Estimating directions of optic disk blood vessels in retinal images. Pattern Recognit Image Anal 2007; 17(4): 523-526.
  • Mukhin A, Kilbas I, Paringer R, Ilyasova N. Application of the gradient descent for data balancing in diagnostic image analysis problems. 2020 Int Conf on Information Technology and Nanotechnology 2020; 1-4.
  • Ilyasova NYu, Shirokanev AS, Kupriynov AV, Paringer RA. Technology of intellectual feature selection for a system of automatic formation of a coagulate plan on retina.Computer Optics 2019; 43(2): 304-315.
  • Shirokanev AS, Kirsh DV, Ilyasova NYu, Kupriynov AV. Investigation of algorithms for coagulate arrangement in fundus images. Computer Optics 2018; 42(4): 712-721.
  • Kazakov AL, Lebedev PD. Algorithms of optimal packing construction for planar compact sets. Computational Methods and Programming 2015; 16: 307-317.
  • Tamborski S, Wrobel K, Bartuzel M, Szkulmowski M. Spectral and time domain optical coherence spectroscopy. Opt Lasers Eng 2020; 133: 106120.
  • Shirokanev A, Ilyasova N, Andriyanov N, Zamytskiy E, Zolo-tarev A, Kirsh D. Modeling of fundus laser exposure for estimating safe laser coagulation parameters in the treatment of diabetic retinopathy. Mathematics 2021; 9: 967.
  • Shirokanev AS, Andriyanov NA, Ilyasova NY. Development of vector algorithm using CUDA technology for three-dimensional retinal laser coagulation process modeling. Computer Optics 2021; 45(3): 427-437.
  • Fukunaga K. Introduction to statistical pattern recognition. New York, London: Academic Press; 1979.
Еще
Статья научная
QR-код для установки приложения SciUp Наведите камеру и скачайте бесплатное приложение SciUp