BACKGROUND: In recent years, the availability of medical datasets and technologies for software development based on artificial intelligence technology has resulted in a growth in the number of solutions for medical diagnostics, particularly mammography. Registered as a medical device, this program can interpret digital mammography, significantly saving time, material, and human resources in healthcare while ensuring the quality of mammary gland preventive studies. AIM: This study aims to justify the possibility and effectiveness of artificial intelligence-based software for the first interpretation of digital mammograms while maintaining the practice of a radiologists second description of X-ray images. MATERIALS AND METHODS: A dataset of 100 digital mammography studies (50 absence of target pathology and 50 ― presence of target pathology, with signs of malignant neoplasms) was processed by software based on artificial intelligence technology that was registered as a medical device in the Russian Federation. Receiver operating characteristic analysis was performed. Limitations of the study include the values of diagnostic accuracy metrics obtained for software based on artificial intelligence technology versions, relevant at the end of 2022. RESULTS: When set to 80.0% sensitivity, artificial intelligence specificity was 90.0% (95% CI, 81.798.3), and accuracy was 85.0% (95% CI, 78.092.0). When set to 100% specificity, artificial intelligence demonstrated 56.0% sensitivity (95% CI, 42.269.8) and 78.0% accuracy (95% CI, 69.986.1). When the sensitivity was set to 100%, the artificial intelligence specificity was 54.0% (95% CI, 40.267.8), and the accuracy was 77.0% (95% CI, 68.885.2). Two approaches have been proposed, providing an autonomous first interpretation of digital mammography using artificial intelligence. The first approach is to evaluate the X-ray image using artificial intelligence with a higher sensitivity than that of the double-reading mammogram by radiologists, with a comparable level of specificity. The second approach implies that artificial intelligence-based software will determine the mammogram category (absence of target pathology or presence of target pathology), indicating the degree of confidence in the obtained result, depending on the corridor into which the predicted value falls. CONCLUSIONS: Both proposed approaches for using artificial intelligence-based software for the autonomous first interpretation of digital mammograms can provide diagnostic quality comparable to, if not superior to, double-image reading by radiologists. The economic benefit from the practical implementation of this approach nationwide can range from 0.6 to 5.5 billion rubles annually.

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.

An international reader study was conducted to gauge an average diagnostic accuracy of radiologists interpreting chest X-ray images, including those from fluorography and mammography, and establish requirements for stand-alone radiological artificial intelligence (AI) models. The retrospective studies in the datasets were labelled as containing or not containing target pathological findings based on a consensus of two experienced radiologists, and the results of a laboratory test and follow-up examination, where applicable. A total of 204 radiologists from 11 countries with various experience performed an assessment of the dataset with a 5-point Likert scale via a web platform. Eight commercial radiological AI models analyzed the same dataset. The AI AUROC was 0.87 (95% CI:0.83–0.9) versus 0.96 (95% CI 0.94–0.97) for radiologists. The sensitivity and specificity of AI versus radiologists were 0.71 (95% CI 0.64–0.78) versus 0.91 (95% CI 0.86–0.95) and 0.93 (95% CI 0.89–0.96) versus 0.9 (95% CI 0.85–0.94) for AI. The overall diagnostic accuracy of radiologists was superior to AI for chest X-ray and mammography. However, the accuracy of AI was noninferior to the least experienced radiologists for mammography and fluorography, and to all radiologists for chest X-ray. Therefore, an AI-based first reading could be recommended to reduce the workload burden of radiologists for the most common radiological studies such as chest X-ray and mammography.

At the beginning of the first wave of the COVID-19 pandemic, a network of outpatient CT centers (OCTC) for lung pathology diagnostics in patients with suspected viral pneumonia with the round-the-clock operation was formed in Moscow. The introduction of the CT 0-4 scale allowed for effective routing. To prevent the spread of infection among patients and staff, OCTC zoning was introduced, dividing into red, buffer, and green zones. As part of the mobilization of the Radiology Service, the Moscow Reference Center was established, aimed at quality control, remote expert consultations, and organizational and methodological support. Several online courses and training webinars have been developed. Artificial Intelligence services were connected to recognize the signs of COVID-19 and assess the severity. The developed strategy of the Moscow Radiology Service ensured readiness for the high burden on the city health care system and minimized losses among medical personnel. The experts significantly contributed to effective infection control through accessible, timely, and high-quality diagnostics and routing.

Introduction: A rapid development of artificial intelligence software, including that for the analysis of digital fluorography images, has been noted recently. Pre-registered as a medical device, this software can be used for stand-alone analysis and triage of test results, allowing radiologists to focus on pathological findings. Objective: To substantiate the possibility and efficiency of using artificial intelligence software for stand-alone analysis and triage of digital fluorography images. Materials and methods: 88,048 digital fluorograms obtained in the first quarter of 2023 were processed using the artificial intelligence software registered in the Russian Federation as a medical device and a ROC analysis of the findings was carried out. Results: We established that default software settings with the sensitivity of 90.4 % (95 % CI: 88.2–92.7) produced specificity of 75.5 % (95 % CI: 75.2–75.8) and accuracy of 75.6 % (95 % CI: 75.3–75.9). At the maximum sensitivity of 100.0 % (95 % CI: 100–100), specificity was 77.4 % (95 % CI: 74.8–80.0) and accuracy was as high as 77.9 % (95 % CI: 75.3–80.5). We have proposed a model of organizing health care which provides for stand-alone sorting of fluorography images by the software, saving normal results without their verification by a radiologist, and sending images with abnormal findings to a radiologist for diagnosis (in the future, as artificial intelligence improves, the latter will be immediately sent to a physician of the clinical specialty). Conclusions: The established optimal scenario includes the use of artificial intelligence software to identify normal findings, which examination by a radiologist is optional when the algorithm is set to maximum sensitivity. Only the findings classified as abnormal will be subject to mandatory revision. The annual economic benefit gained by practical implementation of this approach nationwide can reach 5.6 billion rubles.

Методы лучевой диагностики все более масштабно применяются при массовых профилактических осмотрах (скринингах) для выявления различных патологических состояний. С целью повышения эффективности программ скрининга в ряде ведущих стран мира предусмотрены двойные описания результатов исследований, что неоспоримо увеличивает рабочую нагрузку на врачей-рентгенологов. В связи с эти крайне актуальной становится задача автоматизированного анализа результатов скрининговых исследований. ЦЕЛЬ ИССЛЕДОВАНИЯ Оценить влияние делегирования полномочий по выполнению первого описания медицинскому программному обеспечению на основе технологий искусственного интеллекта (ИИ) на длительность процесса двойного описания результатов флюорографии. МАТЕРИАЛ И МЕТОДЫ Исследование выполнено на базе Московского референс-центра лучевой диагностики (ГБУЗ «НПКЦ ДиТ ДЗМ»). В исследование включено 13 901 флюорографическое исследование. Реализованы два сценария двойного описания исследований: в первом случае участвовали врач-рентгенолог и алгоритм ИИ, во втором — два врача-рентгенолога. Просмотр результатов ИИ и описание исследований проводилось в Едином радиологическом информационном сервисе Единой медицинской информационно-аналитической системы города Москвы. Выполнен статистический анализ данных. РЕЗУЛЬТАТЫ По сценарию №1 проведено двойное описание 1435 результатов флюорографии, по сценарию №2 — 12 446. В первом сценарии врач, получив данные машинного анализа, затрачивал на подготовку заключения в среднем 0,9±3,0 мин. Во втором сценарии продолжительность работы врача, осуществлявшего первый просмотр, составила 0,8±2,1 мин; второй просмотр — 0,4±1,0 мин. Общая длительность проведения двойного описания в формате «врач + ИИ» колебалась от 0,7 до 1241,2 мин, составив в среднем 199,3±330,3 мин. Во втором сценарии общая длительность проведения двойного описания составила 1 838,5±3671,4 мин. ЗАКЛЮЧЕНИЕ Делегирование первого описания алгоритму искусственного интеллекта принципиально ускоряет предоставление результатов флюорографии, повышая их доступность для обследованных лиц и медицинских работников, направляющих пациентов на обследование. Актуальнейшим вопросом становится точность работы соответствующих технологий искусственного интеллекта, а обязательность их регистрации в качестве медицинского изделия не подлежит дальнейшему обсуждению.