Digital Diagnostics (Jul 2024)
The experience of using artificial intelligence for automated analysis of digital radiographs in a city hospital
Abstract
BACKGROUND: The volume of medical diagnostic studies continues to increase annually, intensifying the desire to implement advanced technologies in the field of medical diagnostics. One of the promising approaches that has attracted attention is the use of artificial intelligence in this area. A study was conducted on the automated analysis of chest radiographs using the AI service PhthisisBioMed at a city hospital specializing in the treatment of respiratory diseases. AIM: The study aimed to assess the diagnostic accuracy of the artificial intelligence service “PhthisisBioMed” for the detection of respiratory pathologies in the context of a city hospital that provides 24-hour specialized care in the field of pulmonology. MATERIALS AND METHODS: This study employed a prospective design, with the results of the artificial intelligence service available to the physician on request. This enabled the physician to review the results of the service if an alternative opinion was needed. The reference test was conducted by radiologists at Samara City Hospital No. 4, who described the examinations performed during the testing period. The index test was performed on the software “Program for Automated Analysis of Digital Chest Radiographs/Fluorograms according to TU 62.01.29-001-96876180-2019” produced by PhthisisBioMed LLC. The PhthisisBioMed software was employed to analyze digital fluorograms of the lungs in direct anterior projection. The software automatically identified the following radiological signs of pathologies: pleural effusion, pneumothorax, atelectasis, darkening, infiltration/consolidation, dissemination, cavity, calcification/calcified shadow, and cortical layer integrity violation. Fluorograms of patients over the age of 18 were included in the analysis. The study was conducted within the framework of research and development work No. 121051700033-3, entitled “Lung Damage of Infectious Etiology. Improvement of Methods of Detection, Diagnosis and Treatment” (14.05.2021). RESULTS: Following the pilot operation of the PhthisisBioMed artificial intelligence service and subsequent ROC analysis, the diagnostic accuracy metrics claimed by the manufacturer of the artificial intelligence medical device were confirmed. The service provided the probability of the presence of various pathologies. According to the highlighted labels, 63 patients (4.8%) were suspected of tuberculosis based on characteristic radiologic features. The conclusion was made independently by the radiologist, and the results were evaluated by the attending physician. The attending physician had the opportunity to compare the results and discuss them with the radiologist if differences were found. The results of the survey of pulmonologists who participated in the study indicated that the conclusion of the artificial intelligence service was received automatically within 15 seconds, while the conclusion of the physician was received within 30 minutes or more. CONCLUSIONS: The results of the study indicate that the implementation of the PhthisisBioMed software is expedient both in the outpatient department of the hospital in terms of assessing the annual fluorographic examination of the population, and in the pulmonology service of the city, inpatient and admission department of the hospital.
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