Aleksandr Chuprov, Irina Pavlovna Bolodurina, A. O. Lositskiy, A. Zhigalov
{"title":"ORGANIZATION OF DISPENSARY OBSERVATION OF PATIENTS WITH PATHOLOGY OF THE MACULAR RETINA USING ARTIFICIAL INTELLIGENCE SYSTEMS","authors":"Aleksandr Chuprov, Irina Pavlovna Bolodurina, A. O. Lositskiy, A. Zhigalov","doi":"10.17816/dd623956","DOIUrl":null,"url":null,"abstract":"Background: Despite the mention in the provision of medical care for diseases of the organ of vision, adnexa and orbit about equipping the medical consultative and diagnostic department of the clinic with an optical coherence tomograph, dynamic monitoring of patients with retinal pathology after the start of treatment is most often carried out in a medical ophthalmological center, which reduces accessibility for patients with primary (first identified) morbidity requiring initiation of treatment. The required technology needs to be changed, intensified, incl. using artificial intelligence technologies. \nAim: development of methodological foundations for organizational technology for dispensary observation of patients with pathology of the posterior segment of the eye using clinical decision support system (CDSS) based on artificial intelligence. \nMaterials and methods: The assessment of the existing regulatory framework was carried out on the basis of an analysis of the Constitution of the Russian Federation, Federal laws, by-laws, and judicial practice. The creation of a methodology for a structured medical document describing an optical coherence tomography image was carried out using an expert method: a survey of 100 ophthalmologists with the appropriate level of education, incl. additional professional, engaged in the provision of medical services - specialized medical care (treatment) to patients with pathology of the posterior segment of the eye. \nResults: Using an expert method, 123 binary signs were selected and described to describe the structure of the macular region of the retina in normal and pathological conditions, of which 26 signs were identified that can be interpreted as predictors of worsening of the clinical course of the disease. \nConclusion: The developed classifier made it possible to create and train a medical decision support system based on 60,000 medical images, which, as an information service, without making a diagnosis, can change the organization of the dynamic observation process: the formation of patient routing - a primary service using the developed CDSS; if there are signs of deterioration in the clinical picture - routing to a medical ophthalmological center to assess the dynamics, provide specialized services, incl. high-tech medical care.","PeriodicalId":34831,"journal":{"name":"Digital Diagnostics","volume":"48 5","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Digital Diagnostics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.17816/dd623956","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Despite the mention in the provision of medical care for diseases of the organ of vision, adnexa and orbit about equipping the medical consultative and diagnostic department of the clinic with an optical coherence tomograph, dynamic monitoring of patients with retinal pathology after the start of treatment is most often carried out in a medical ophthalmological center, which reduces accessibility for patients with primary (first identified) morbidity requiring initiation of treatment. The required technology needs to be changed, intensified, incl. using artificial intelligence technologies.
Aim: development of methodological foundations for organizational technology for dispensary observation of patients with pathology of the posterior segment of the eye using clinical decision support system (CDSS) based on artificial intelligence.
Materials and methods: The assessment of the existing regulatory framework was carried out on the basis of an analysis of the Constitution of the Russian Federation, Federal laws, by-laws, and judicial practice. The creation of a methodology for a structured medical document describing an optical coherence tomography image was carried out using an expert method: a survey of 100 ophthalmologists with the appropriate level of education, incl. additional professional, engaged in the provision of medical services - specialized medical care (treatment) to patients with pathology of the posterior segment of the eye.
Results: Using an expert method, 123 binary signs were selected and described to describe the structure of the macular region of the retina in normal and pathological conditions, of which 26 signs were identified that can be interpreted as predictors of worsening of the clinical course of the disease.
Conclusion: The developed classifier made it possible to create and train a medical decision support system based on 60,000 medical images, which, as an information service, without making a diagnosis, can change the organization of the dynamic observation process: the formation of patient routing - a primary service using the developed CDSS; if there are signs of deterioration in the clinical picture - routing to a medical ophthalmological center to assess the dynamics, provide specialized services, incl. high-tech medical care.