Autonomous artificial intelligence for sorting results of preventive radiological examinations of chest organs: medical and economic efficiency

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Abstract

BACKGROUND: This article proposes a model for organizing preventive radiological examinations of chest organs through autonomous sorting of examination results using medical devices based on artificial intelligence technologies, optimized for maximum sensitivity — 1.0 (95% CI: 1.0; 1.0). Sorting involves classifying the results of mass preventive screenings (fluoroscopy and chest X-rays) into two: “not normal” and “normal.” The “not normal” category includes all cases of abnormalities (e.g., pathological conditions, post-disease or post-surgery consequences, and age-related and congenital features), which are sent for interpretation by a radiologist. The “normal” category consists of cases without signs of pathological deviations, which potentially do not require a radiologist’s description.

AIM: To evaluate the feasibility, effectiveness, and efficiency of autonomous sorting of results from preventive radiological examinations of chest organs.

MATERIALS AND METHODS: A prospective multicenter diagnostic study was conducted on the safety and quality of autonomous sorting of results from preventive radiological examinations of chest organs. Analytical and statistical methods of scientific inquiry were used.

RESULTS: The study included results from 575,549 preventive radiological examinations obtained through fluoroscopy and chest X-rays and processed using three medical devices based on artificial intelligence technologies. In autonomous sorting, 54.8% of the preventive radiological examinations of chest organs were classified as “normal,” resulting in a proportional reduction in the radiologist’s workload for interpreting and describing the examination results. Fully correct autonomous sorting was achieved in 99.95% of cases. Clinically significant discrepancies were determined in 0.05% of cases (95% CI: 0.04; 0.06%).

CONCLUSIONS: This study confirmed the medical and economic effectiveness of the model for autonomous sorting of results from preventive radiological examinations of chest organs using medical devices based on artificial intelligence technologies. The next phase should involve updating the regulatory framework and ensuring the legitimacy of the autonomous application of certain medical devices based on artificial intelligence technologies in established conditions and preventive tasks.

About the authors

Yuriy A. Vasilev

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: npcmr@zdrav.mos.ru
ORCID iD: 0000-0002-5283-5961
SPIN-code: 4458-5608

MD, Dr. Sci. (Medicine)

Russian Federation, Moscow

Dmitry A. Sychev

Medical Academy of Continuous Professional Education

Email: dimasychev@mail.ru
ORCID iD: 0000-0002-4496-3680
SPIN-code: 4525-7556

MD, Dr. Sci. (Medicine), Professor, academician of the Russian Academy of Sciences

Russian Federation, Moscow

Alexander V. Bazhin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: BazhinAV@zdrav.mos.ru
ORCID iD: 0000-0003-3198-1334
SPIN-code: 6122-5786

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Igor M. Shulkin

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: ShulkinIM@zdrav.mos.ru
ORCID iD: 0000-0002-7613-5273
SPIN-code: 5266-0618

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Anton V. Vladzymyrskyy

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Author for correspondence.
Email: vladzimirskijAV@zdrav.mos.ru
ORCID iD: 0000-0002-2990-7736
SPIN-code: 3602-7120

MD, Dr. Sci. (Medicine)

Russian Federation, Moscow

Alexandra Yu. Golikova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: GolikovaAY1@zdrav.mos.ru
ORCID iD: 0009-0001-5020-2765
Russian Federation, Moscow

Kirill M. Arzamasov

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: ArzamasovKM@zdrav.mos.ru
ORCID iD: 0000-0001-7786-0349
SPIN-code: 3160-8062

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Andrei V. Mishchenko

Medical Academy of Continuous Professional Education

Email: dr.mishchenko@mail.ru
ORCID iD: 0000-0001-7921-3487
SPIN-code: 8825-4704

MD, Dr. Sci. (Medicine)

Russian Federation, Moscow

Gevorg A. Bekdzhanyan

Medical Academy of Continuous Professional Education

Email: rmapo@rmapo.ru
ORCID iD: 0009-0007-7150-7166
SPIN-code: 4579-9457
Russian Federation, Moscow

Arcadiy S. Goldberg

Medical Academy of Continuous Professional Education

Email: goldarcadiy@gmail.com
ORCID iD: 0000-0002-2787-4731
SPIN-code: 8854-0469

MD, Cand. Sci. (Medicine)

Russian Federation, Moscow

Larisa G. Rodionova

Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies

Email: RodionovaLG@zdrav.mos.ru
ORCID iD: 0009-0008-9862-8205
Russian Federation, Moscow

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2. Fig. 1. General study procedure AI, artificial intelligence; RMACPE MoH of Russia, Russian Medical Academy of Continuous Professional Education of the Ministry of Health of Russia; RCRPCC D&TT MCHD, Research and Practical Clinical Center for Diagnostics and Telemedicine Technologies of the Moscow City Health Department.

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3. Fig. 2. A presentation example for artificial intelligence conclusions in the electronic medical record in the Unified Medical Information and Analytical System as an automatically generated conclusion about the absence of abnormal changes in an electronic medical record format.

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4. Fig. 3. Presentation example for the artificial intelligence-generated results in the Unified Radiological Information Service of the Unified Medical Information and Analytical System of Moscow (image, DICOM SR).

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5. Fig. 4. A set of images for a male patient aged 53 years. A clinically significant discrepancy: signs of small, focal, polysegmental dissemination in the lungs.

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6. Fig. 5. Images for a female patient aged 47 years. A clinically significant discrepancy: lesion in the lower lobe of the left lung (red arrow).

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