Digital analysis and quantitative assessment of the cervical surface with dysplasia.

Autor:	Dushkin AD; The Loginov Moscow Clinical Scientific Center is State Institution funded by Moscow Health Department., Afanasiev MS; I.M. Sechenov First Moscow State Medical University (Sechenov University)., Zatevalov AM; G.N. Gabrichevsky Moscow Research Institute for Epidemiology and Microbiology., Aleshkin VA; G.N. Gabrichevsky Moscow Research Institute for Epidemiology and Microbiology., Mironov AY; G.N. Gabrichevsky Moscow Research Institute for Epidemiology and Microbiology., Afanasiev SS; G.N. Gabrichevsky Moscow Research Institute for Epidemiology and Microbiology., Nesvizhsky YV; I.M. Sechenov First Moscow State Medical University (Sechenov University)., Borisova OY; G.N. Gabrichevsky Moscow Research Institute for Epidemiology and Microbiology., Grishacheva TG; First Pavlov State Medical University of St. Peterburg., Karaulov AV; I.M. Sechenov First Moscow State Medical University (Sechenov University).
Jazyk:	angličtina
Zdroj:	Klinicheskaia laboratornaia diagnostika [Klin Lab Diagn] 2021 Jul 16; Vol. 66 (7), pp. 417-421.
DOI:	10.51620/0869-2084-2021-66-7-417-421
Abstrakt:	The investigation aims - a quantitative assessment of cervical surface changes with digital analysis and computer technologies in dysplasia. Colposcopy was made in 90 women from 21 to 52 years (avr. age 33,9±8,13 y.o.) with mild epithelial dysplasia (CIN1), moderate dysplasia (CIN2), severe dysplasia (CIN3). The algorithm detected indicators which provide the cervical dysplasia classification on pre cytological and pre molecular-genetic patients investigations. The outcome of an algorithm was the identification of the cervix surface condition severity by an objective quantification. The cervical dysplasia type (CIN) was classified as IndGV values. The mild dysplasia (CIN1) had IndGV=8,5, moderate dysplasia (CIN2) - IndGV=13, severe dysplasia (CIN3) - IndGV=15,6. The cervical affected surface area (IndInt) equalled 0,17 in CIN1, 0,19 in CIN2, 0,22 in CIN3. A change severity has a direct relation with a grey color value. It demonstrates quantify classification in digital analysis. The algorithm is used in real-time mode and no requires considerable material outlays. This makes it possible to use an algorithm after clinical examination and predict patient management. Competing Interests: The authors declare no conflict of interest.
Databáze:	MEDLINE
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