Influence of the implementation of automatic artificial intelligence-based CT image analysis systems in diagnosis of middle cerebral artery stroke in human-Reference-Cited by-同舟云学术

Influence of the implementation of automatic artificial intelligence-based CT image analysis systems in diagnosis of middle cerebral artery stroke in human

Published:2024-05-11 Issue: Volume: Page:
ISSN:2408-9516
Container-title:Medical Visualization
language:
Short-container-title:Medicinskaâ vizualizaciâ

Author:

Andropova P. L.¹^ORCID,Kolesnikova P. A.²^ORCID,Gavrilov P. V.³^ORCID,Kokorev O. V.⁴^ORCID,Chernova E. V.¹^ORCID,Kurnosenko O. A.¹^ORCID,Galustyan D. H.¹^ORCID,Boytsova M. G.⁵^ORCID,Trofimova T. N.⁵^ORCID

Affiliation:

1. St. Petersburg City Hospital of the Holy Martyr Elizabeth

2. Emanuel Institute of Biochemical Physics of the Russian Academy of Sciences

3. Saint-Petersburg State Research Institute of Phthisiopulmonology

4. GE HealthCare

5. St. Petersburg State University

Abstract

The purpose of the study was to investigate the possibility of applying first-reader and second-reader modes in the implementation of an automatic detection program for MCA ischemic stroke in the diagnostic process of radiologists with less than 3 years of experience and varying expertise in emergency neuroradiology.Material and methods. The study included a software product based on artificial intelligence technologies, as well as seven doctors with less than 3 years of experience and varying expertise in the diagnosis of ischemic stroke. Complementary evaluation was performed based on a cohort of 100 patients admitted to the regional vascular center in Saint Petersburg with clinical presentation of ischemic stroke in the territory of the middle cerebral artery, who underwent native CT brain studies. Ischemic stroke was confirmed in half of the patients based on clinical data, as well as CT angiography of the cerebral vessels and CT perfusion. The diagnosis was ruled out in the other half. Two variants of implementing the artificial intelligence algorithm as a decision support system in the diagnostic process of a radiologist were simulated: the first (parallel) and second reader modes.Results. The results of the study showed that the application of the complementary evaluation parallel- reader mode leads to an increase in diagnostic efficiency indicators and interobserver agreement in assessing ASPECTS scale among young specialists, regardless of their experience with urgent pathology.

Publisher

Vidar, Ltd.

Reference11 articles.

1. Decree of the President of the Russian Federation dated October 10, 2019 No.490 “On the development of artificial intelligence in the Russian Federation”(together with the “National Strategy for the Development of Artificial Intelligence for the period until 2030”). URL:http://www.consultant.ru/document/cons_doc_LAW_335184/. Accessdate:03/15/2022 (In Russian)

2. Morozov S.P., Vladzymyrskyy A.V., Klyashtornyy V.G. et al. Clinical acceptance of software based on artificial intelligence technologies (radiology). Moscow, Series “Best practices in medical imaging”. Issue 23. 2019. 34 p. (In Russian)

3. Obuchowski N.A., Bullen J.A. Statistical considerations for testing an AI algorithm used for prescreening lung CT images. Contemp Clin Trials Commun. 2019; 16: 100434. https://doi.org/10.1016/j.conctc.2019.100434. Erratum in: Contemp Clin Trials Commun. 2020 Dec 10; 20: 100689. PMID: 31485545; PMCID: PMC6717063

4. Smolnikova U.A. Possibilities of automated analysis of digital X-rays in diagnosing pulmonary nodules and masses: Scientific speciality 3.1.25. Diagnostic radiology. Dissertation submitted for the degree of candidate of medical sciences / Smolnikova Uliana Alekseevna, 2022. 281 p. (In Russian)

5. Andropova P.L., Gavrilov P.V., Kolesnikova P.A. et al. Diagnostic efficiency of individual systems for automatic analysis of computed tomography images in the detection of ischemic stroke in the basin of the middle cerebral artery. The Siberian Journal of Clinical and Experimental Medicine. 2023; 38 (3): 194–200. https://doi.org/10.29001/2073-8552-2023-39-3-194-200 (In Russian)