Study Design: Retrospective cohort study.Purpose: To evaluate the effectiveness of opportunistic osteoporosis screening using an artificial intelligence (AI) algorithm for detecting vertebral compression deformity (VCD >25%) and reduced bone mineral density (BMD) from routine chest computed tomography (CT) scans.Overview of Literature: Osteoporosis is an insidious metabolic disease that often remains asymptomatic for a long time, and is typically diagnosed due to the occurrence of complications. An approach for diagnosing osteoporosis based on routine CT examinations, including the use of AI services, is being actively studied.Methods: Patients aged >50 years who underwent chest CT using the standard protocol between 09.06.2021 and 30.06.2021 at four inpatient and three outpatient clinics were retrospectively enrolled. The morphometric AI algorithm detected vertebral compression index and vertebrae density in Hounsfield units (HU). The AI algorithm was calibrated for BMD measurements using a phantom. Osteoporotic BMD was defined according to the American College of Radiology criteria (<80 mg/mL). The presence of vertebral fracture (VF) was verified using semiquantitative and algorithm-based qualitative methods by three radiologists, followed by two experts with 15 and 35 years of experience in the field.Results: CT studies of 1,888 patients (mean age, 66.3 years) were included. The AI algorithm detected VCD in 336 patients (17.8%), with 105 (5.5%) having VF. Low BMD values were detected in 501 patients (26.5%; 31.0% of females, 18.6% of males).Conclusions: We observed high diagnostic accuracy of opportunistic osteoporosis screening using AI algorithms for detecting VF and low BMD. AI-based opportunistic screening of osteoporosis and VF in chest CT scans can facilitate the identification of high-risk cohorts.

BACKGROUND: Pathological low-energy (LE) vertebral compression fractures (VFs) are common complications of osteoporosis and predictors of subsequent LE fractures. In 84% of cases, VFs are not reported on chest CT (CCT), which calls for the development of an artificial intelligence-based (AI) assistant that would help radiology specialists to improve the diagnosis of osteoporosis complications and prevent new LE fractures.AIMS: To develop an AI model for automated diagnosis of compression fractures of the thoracic spine based on chest CT images.MATERIALS AND METHODS: Between September 2019 and May 2020 the authors performed a retrospective sampling study of ССТ images. The 160 of results were selected and anonymized. The data was labeled by seven readers. Using the morphometric analysis, the investigators received the following metric data: ventral, medial and dorsal dimensions. This was followed by a semiquantitative assessment of VFs degree. The data was used to develop the Comprise-G AI mode based on CNN, which subsequently measured the size of the vertebral bodies and then calculates the compression degree. The model was evaluated with the ROC curve analysis and by calculating sensitivity and specificity values.RESULTS: Formed data consist of 160 patients (a training group - 100 patients; a test group - 60 patients). The total of 2,066 vertebrae was annotated. When detecting Grade 2 and 3 maximum VFs in patients the Comprise-G model demonstrated sensitivity - 90,7%, specificity - 90,7%, AUC ROC - 0.974 on the 5-FOLD cross-validation data of the training dataset; on the test data - sensitivity - 83,2%, specificity - 90,0%, AUC ROC - 0.956; in vertebrae demonstrated sensitivity - 91,5%, specificity - 95,2%, AUC ROC - 0.981 on the cross-validation data; for the test data sensitivity - 79,3%, specificity - 98,7%, AUC ROC - 0.978.CONCLUSIONS: The Comprise-G model demonstrated high diagnostic capabilities in detecting the VFs on CCT images and can be recommended for further validation.

Rationale. Quantitative CT (QCT) bone densitometry with asynchronous calibration not require a phantom during the scan procedure. Based on calibration data it converts X-ray density in HU to bone mineral density (BMD). Given the large number of CT studies performed on patients at risk of osteoporosis, there is a need for a hands-on method capable of assessing BMD in a short period of time without tailored software or protocols. Goal. To develop a method for QCT bone densitometry using an PHK (PHantom Kalium), to compare the volume BMD measurements with the QCT data with asynchronous calibration provided by software from a reputable developer. Methods. The studies were performed at 64-slice CT unit with body scanning parameters. The BMD was measured using two techniques: 1) QCT with asynchronous calibration using software from a reputable developer; 2) QCT using a PHK phantom (QCT-PHK). For convert the HU to BMD values, we scanned the PHK phantom and calculate correction factor. Phantom contains “vertebrae” filled with potassium hydrogen phosphate in different concentrations. In both methods, the BMD values measured for LI–II, and sometimes for ThXII, LIII. Results. The study enrolled 65 subjects (11 male and 54 female patients); median age 69.0 years. A comparison of the vertebrae BMD measured by QCT and QCT-PHK revealed a significant linear Pearson correlation r = 0.977 (p < 0.05). The Bland–Altman analysis demonstrated a lack of relationship between the difference in measurements and the average BMD and a systematic BMD; bias of +4.50 mg/ml in QCT vs. QCT-PHK. Differences in the division into groups osteoporosis / osteopenia / norm according to the ACR criteria for the two methods were not significant. Conclusion. The developed asynchronous QCT-PHK method measure BMD comparable to the widely used QCT with asynchronous calibration. This method can be used for opportunistic screening for osteoporosis.

Goal : To develop a method for automated assessment of the volumetric bone mineral density (BMD) of the vertebral bodies using an artificial intelligence (AI) algorithm and a phantom modeling method. Materials and Methods: Evaluation of the effectiveness of the AI algorithm designed to assess BMD of the vertebral bodies based on chest CT data. The test data set contains 100 patients aged over 50 y.o.; the ratio between the subjects with/without compression fractures (Сfr) is 48/52. The X-ray density (XRD) of vertebral bodies at T ₁₁ -L ₃ was measured by experts and the AI algorithm for 83 patients (205 vertebrae). We used a recently developed QCT PK (Quantitative Computed Tomography Phantom Kalium) method to convert XRD into BMD followed by building calibration lines for seven 64-slice CT scanners. Images were taken from 1853 patients and then processed by the AI algorithm after the calibration. The male to female ratio was 718/1135. Results : The experts and the AI algorithm reached a strong agreement when comparing the measurements of the XRD. The coefficient of determination was R2=0.945 for individual vertebrae (T ₁₁ -L ₃ ) and 0.943 for patients (p=0.000). Once the subjects from the test sample had been separated into groups with/without Сfr, the XRD data yielded similar ROC AUC values for both the experts – 0.880, and the AI algorithm – 0.875. When calibrating CT scanners using a phantom containing BMD samples made of potassium hydrogen phosphate, the following averaged dependence formula BMD =0.77*HU-1.343 was obtained. Taking into account the American College Radiology criteria for osteoporosis, the cut-off value of BMD<80 mg/ml was 105.6HU; for osteopenia BMD<120 mg/ml was 157.6HU. During the opportunistic assessment of BMD in patients aged above 50 years using the AI algorithm, osteoporosis was detected in 31.72% of female and 18.66% of male subjects. Conclusion s : This paper demonstrates good comparability for the measurements of the vertebral bodies’ XRD performed by the AI morphometric algorithm and the experts. We presented a method and demonstrated great effectiveness of opportunistic assessment of vertebral bodies’ BMD based on computed tomography data using the AI algorithm and the phantom modeling.

BACKGROUND: Computed tomography is the method of choice for assessing the volume of lung damage in viral pneumonia, including those associated with COVID-19. In addition, computed tomography can determine the main vessels size of the thorax. This allowed us to analyze the relationship between the severity of COVID-19 and the changes in the diameters of the pulmonary artery (PA) and ascending aorta (Ao). Dilation of the PA is a sign of pulmonary hypertension. The study of these patterns may be of clinical significance in determining the treatment tactics and prognosis of the course of COVID-19 disease. AIM: To evaluate the relationship between PA diameter and the severity of the COVID-19 course in patients of different ages. MATERIALS AND METHODS: This study is a single-centered, cross-section, continuous, uncontrolled study performed on a group of patients (n=511, 267 men, median 59 years, IQR 49.065.0, ages 3184 years) who were treated in a temporary hospital to treat patient with COVID-19. During hospitalization all patients were examined by CT scan of the chest. All studies were carried out using a mobile CT scan system Airo TruCT (Stryker, USA). The degree of damage to the lung tissue was assessed using the CT volume scale 14. Measurement of the LA and Ao diameters was carried out using standard instruments of the radiologists CT workstation perpendicular to the long axis of the vessel. RESULTS: The following statistically significant regularities were obtained: the detection of a dilated pulmonary artery (PA) and an increased PA/Ao ratio correlated to an increase in the degree of lung damage in COVID-19 (Kruskal-Wallis test, K-W p 0.001; median test, MT p 0.001), the diameter of the ascending aorta (Ao) significantly increases with the patients age (K-W p 0.001; MT p 0.001). An insignificant correlation between an increase in the diameter of the pulmonary artery (PA) and the patients age (K-W p=0.094; MT p=0.311) and an insignificant correlation between detection of a change in aortic (Ao) diameter and the degree of lung damage (K-W p=0.061; MT p=0.165) were shown. In groups with a severe course of the disease and a large volume of lung lesions (CT-3 and CT-4), a significantly greater number of patients with signs of pulmonary hypertension (detection of the dilated pulmonary artery: 29 mm and more) was shown for all age groups. CONCLUSION: The study showed that PA dilatation and increased PA/Ao diameter ratio were significantly associated with increased pulmonary lesion volume in COVID-19 in all age groups.

Objective: to conduct the study of age distribution of bone mineral density (BMD) by the database of dualenergy X-ray absorptiometry (DXA) and to compare it with datа of population NHANES study. Material and methods. We used data from the densitometry of three-zone (total hip (TH), femoral neck (FN), and lumbar spine) measured by DXA from two outpatient clinics. The obtained data were compared with NHANES III for TH and FN and with NHANES 2005-08 for lumbar spine. The BMD value was corrected with the calibration coefficient for each DXA scanner. Adjustments were also made for the population distribution by sex and age. Results. Compared with NHANES for FN and TH, the obtained BMD values were significantly decreased for patients aged less than 50 years (men and women). The BMD values for FN and TH were unsignificantly decreased in men older 50 years. In women older 50 years unsignificantly decreased BMD values for FN and a significantly increased BMD values for TH were observed. The BMD values were decreased for lumbar spine in men and women throughout this age interval (more than 50 years old). Conclusion. The population BMD distribution in men and women was assessed by DXA method. The obtained dependence of the BMD for FN in women older 50 years was in good agreement with the results given by Russian and foreign authors.