Yuliya Fedorovna Shumskaya

This article is an adapted version of the clinical practice guidelines titled “Telemonitoring of patients with inflammatory bowel diseases,” published in the series “Best Practices in Radiological and Instrumental Diagnostics” (EDN: GZQHJZ). These guidelines address the critical need to systematize the use of telemedicine technologies to improve access to medical care for patients with inflammatory bowel disease. These guidelines aim to standardize general organizational, managerial, methodological, and technical aspects of inflammatory bowel disease patients’ telemonitoring across medical facilities of all ownership types. The guidelines also describe effective approaches to monitoring and managing the clinical condition of patients with inflammatory bowel disease, thereby helping to optimize diagnostic and treatment processes. The methodological approaches presented may serve as a basis for further investigation of the clinical, social, organizational, and economic effects of telemonitoring in patients with inflammatory bowel disease. Their implementation is expected to have the potential to facilitate the timely detection of changes in the patient's condition, improve disease control, quality of life, and psychological well-being, as well as to optimize the use of healthcare resources. The potential economic significance might be related to a possible reduction in treatment costs, including at the expense of the prevention of severe disease exacerbations. The guidelines are intended for gastroenterologists, coloproctologists, internists, and heads of medical facilities providing care to patients with inflammatory bowel disease.

Cardiovascular diseases (CVDs) continue to be the leading cause of mortality in Russia. Over the past three decades, effective preventive measures have led to a one-third reduction in morbidity rates. However, there remains a significant risk of mortality among patients under 50 years of age who experience acute myocardial infarction (AMI). This highlights the need to identify additional predictors, including radiological features, to enhance prognostic models for CVD. There is an approach suggesting that incorporating low-correlation pairs of features may yield more informative model predictions. Aim. To evaluate the hypothesis that patients under 50 years of age with AMI exhibit distinct low-correlation pairs of characteristics, as identifi ed by chest computed tomography (CT), compared to patients over 50 years of age with AMI. Material and methods. This was a retrospective study involving patients who experienced their first AMI and had undergone chest CT 6–12 months prior to the cardiovascular event. Correlation matrices were created for patients under 50 years of age and those over 50. Subsequently, pairs of features with low correlation that were common to all patients were removed from the set of feature pairs for the younger group. Results. The study included 59 patients, with 12 under the age of 50 and 47 over 50. The groups diff ered in the prevalence of multivessel lesions, the presence of osteoporosis and hydrothorax as assessed by chest CT, and the average aortic size. By constructing correlation matrices and eliminating common feature pairs with weak correlations, we identified 12 unique feature pairs that could enhance existing risk models. Conclusion. Utilizing radiological markers to improve CVD prognosis shows promise, but further prospective cohort studies with larger sample sizes are necessary to validate these findings.

BACKGROUND: Traumatologists often order and independently interpret radiology studies in outpatient trauma care, since radiologists’ reports may be delayed by several hours, rendering them largely formal. Artificial intelligence (AI) could assist traumatologists in real-time image analysis to prevent diagnostic errors and improve quality of care. AIM: To evaluate the possibility of using AI technologies for fracture detection in a trauma outpatient setting. METHODS: A multicenter, retrospective, observational study was conducted. Three AI tools were assessed. For the shoulder, wrist, and ankle joints, a dataset of 100 anonymized X-rays was compiled, annotated by three radiologists. AI outputs were compared against expert annotations using contingency tables. Diagnostic accuracy was evaluated via receiver operating characteristic analysis. RESULTS: For shoulder X-rays, AI demonstrated an accuracy of 0.95 (95% CI: 0.91–0.99), sensitivity of 0.92 (95% CI: 0.84–1.0), and AUROC of 0.96 (95% CI: 0.92–1.00). Wrist fracture analysis showed accuracy of 0.93 (95% CI: 0.88–0.98), sensitivity of 0.9 (95% CI: 0.82–0.98), and AUROC of 0.95 (95% CI: 0.90–0.99). Ankle studies yielded accuracy of 0.89 (95% CI: 0.83–0.95), sensitivity of 0.82 (95% CI: 0.71–0.93), and AUROC of 0.90 (95% CI: 0.84–0.96). CONCLUSIONS: The current level of AI technology development allows the use of AI-based software in emergency outpatient trauma care, thereby optimizing care processes in the trauma center and eliminating radiologist reporting of X-ray results.

Background: In patients with inflammatory bowel diseases (IBD), skeletal muscle involvement includes not only reduced muscle mass but also decreased muscle strength (dynapenia). Previous studies in IBD have mainly focused on quantitative assessment of muscle area by computed tomography (CT), while reduced muscle strength has been studied separately from CT characteristics. It remains unclear whether dynapenia in IBD patients is primarily associated with decreased skeletal muscle area or with changes in muscle quality, including reduced muscle density and myosteatosis. Aim: To evaluate the association between handgrip strength and CT-derived skeletal muscle parameters in patients with IBD. Methods: A single-center cross-sectional study was conducted, including patients with IBD aged 18–75 years hospitalized from January to April 2026 at the Department of Gastroenterology of University Clinical Hospital No. 1 of I. M. Sechenov First Moscow State Medical University. All participants underwent abdominal CT during hospitalization. Muscle strength was assessed by handgrip dynamometry; dynapenia was defined according to the European Working Group on Sarcopenia in Older People (EWGSOP2) criteria. Skeletal muscle area, skeletal muscle index (SMI), and CT-derived muscle density were calculated from abdominal CT images using a convolutional neural network-based artificial intelligence algorithm Comp2Comp. Association analysis was performed using multivariable linear regression adjusted for sex, age, and body mass index. Results: The study included 53 IBD patients, of whom 32 (60%) were male; median age was 38 [28; 51] years. Crohn’s disease was diagnosed in 24 (45%) patients, ulcerative colitis in 29 (55%). Dynapenia was identified in 7 (13%) of 53 patients. In multivariable regression analysis, no statistically significant association was found between handgrip strength and SMI (β = 0.29 kg per 1 cm²/m²; p = 0.182). At the same time, a positive association was observed between handgrip strength and CT-derived muscle density (β = 0.44 kg per 1 HU; 95% confidence interval 0.002–0.87; p = 0.048). Conclusion: In IBD patients, dynapenia is associated with lower muscle density and is not related to SMI. These findings demonstrate the potential of using CT-derived qualitative skeletal muscle characteristics in IBD to identify patients with reduced functional muscle properties.

Background: Large language models (LLMs) are increasingly integrated into real-world medical practice as chatbots for answering clinical queries. However, the perceptions of this technology among its end-users remain understudied. Existing research on physicians’ attitudes toward LLMs relies on non-validated questionnaires, raising concerns about the accuracy and reliability of the findings. The aim of this study is to develop and validate a questionnaire to assess physicians’ attitudes toward LLM-based chatbots used as a reference tool for answering queries. Methods: The instrument was based on the previously developed and validated ATRAI-14 questionnaire assessing radiologists’ attitudes toward artificial intelligence. Items for the new questionnaire were formulated and refined through focus group testing. Validation involved 562 physicians of various specialties working in medical institutions within the Moscow healthcare system. Some respondents had prior experience working with medical LLMs. We assessed face, content, construct, and criterion validity. Criterion validity was evaluated through correlation between respondents’ self-assessed attitudes toward LLMs measured by visual analogue scale (VAS), and construct validity through confirmatory factor analysis. Results: The resulting ATRAI-LLM questionnaire comprised 19 items (8 in the background part and 11 in the main part). The questionnaire demonstrated acceptable internal consistency (Cronbach’s α = 0.770, McDonald’s ω_t = 0.830). It encompasses three domains: “Willingness to Use”, “Implementation Perspective”, and “Hopes and Fears.” Confirmatory factor analysis supported the three-factor structure, with satisfactory fit indices achieved (RMSEA = 0.05, CFI = 0.97, TLI = 0.96, SRMR = 0.03). Criterion validity was confirmed as acceptable with moderate correlation between the final score and VAS scores (Spearman’s rho 0.68, p < 0.001). Conclusions: ATRAI-LLM is a validated instrument for assessing physicians’ attitudes toward LLMs as a knowledge base.

Background: Telemedicine is increasingly used in inflammatory bowel disease (IBD), but its effects on quality of life (QoL) and psychological outcomes remain unclear. Objectives: This study aimed to evaluate the impact of 6-month telemonitoring on QoL, disease activity, treatment adherence, psychological well-being, patient satisfaction, and healthcare utilization. Methods: This randomized, open-label, single-center study conducted in Russia (July 2023–December 2024) included adults with ulcerative colitis or Crohn’s disease, who were assigned 1:1 to telemonitoring or standard care. The intervention involved monthly remote assessments and access to a web-based platform containing educational information, disease activity assessment, and a chat with a gastroenterologist. The primary outcome was health-related QoL (SIBDQ). Exploratory outcomes included general QoL (WHOQOL-26), psychological well-being (HADS), alexithymia (TAS-26), visceral sensitivity (VSI), treatment adherence (GMAS), patient satisfaction (PSQ-18), achievement of clinical remission, and healthcare utilization. Results: Sixty-eight patients completed the study (32 intervention, 36 control). Telemonitoring was associated with lower anxiety levels (β = −1.76, p = 0.021), reduced visceral sensitivity (β = −5.08, p = 0.039), and higher medication adherence (β = 1.75, p = 0.008). No significant associations were observed for SIBDQ, WHOQOL-26 domains, depressive symptoms, alexithymia, achievement of clinical remission, or patient satisfaction with care (p > 0.05). Patients in the telemonitoring group also required fewer outpatient visits (p < 0.001), with no difference in hospitalizations. Within-group analysis demonstrated improvements in QoL, treatment adherence, visceral sensitivity, and disease activity in the telemonitoring group, but not in the controls. Conclusions: Six-month telemonitoring in IBD was associated with lower anxiety, reduced visceral sensitivity, improved treatment adherence, and fewer outpatient visits. The health-related QoL assessed by the SIBDQ did not differ compared to standard care. No clear clinical disadvantage compared with standard care was detected during the study period.

Background. Nutritional status impairments accompanying inflammatory bowel disease (IBD) adversely affect both the achievement and maintenance of remission and patients’ quality of life. Despite the potential utility of computed tomography (CT) as an adjunctive method for body composition assessment, its application in this context remains insufficiently explored. Aim. To evaluate the applicability of CT-derived body composition parameters for the objective characterization of nutritional status in patients with IBD. Materials and methods. A retrospective analysis was conducted using data from patients who underwent evaluation and treatment in a gastroenterology department. Outcomes included laboratory parameters (hemoglobin, total protein, albumin, serum iron, and total cholesterol concentrations) and body composition metrics assessed on CT using a computer vision algorithm: Visceral Fat Area (VFA), Subcutaneous Fat Area (SFA), Intramuscular Fat Area (IFA), VFA/SFA ratio, intramuscular fat fraction, Skeletal Muscle Area (SMA), Skeletal Muscle Index (SMI), SMA/VFA ratio, and CT-derived Skeletal Muscle Radiodensity (SMRD). Results. The study included 142 patients with IBD: 45 (31.7%) with ulcerative colitis and 97 (68.3%) with Crohn’s disease. IBD exacerbation was associated with lower serum iron levels (odds ratio – 1.08, 95% confidence interval – 1.02–1.15; p=0.005). No associations were identified between clinical disease activity and body composition parameters. In analyses of laboratory markers and body composition, higher albumin and hemoglobin levels were associated with higher SMI and SMRD. Total protein was positively associated with SMA (β=0.024; p=0.003), SMI (β=0.028; p=0.003), and SMRD (β=0.024; p=0.020), and inversely associated with intramuscular fat fraction (β=-0.027; p=0.043). Conclusion. Opportunistic CT analysis may enable objective assessment of protein nutritional status in patients with IBD without increasing radiation exposure. The robustness of muscle-related parameters as morphological markers is supported by their independence from disease activity phase and their correlation with biochemical indicators of protein metabolism.

BACKGROUND. In clinical practice, orthopedic trauma surgeons are often required to interpret diagnostic images without the immediate availability of official radiology reports. The use of artificial intelligence (AI) has the potential to enhance diagnostic accuracy and the overall quality of care. However, the success of AI implementation depends on its acceptance by doctors. Sociological surveys allow assessing awareness of AI capabilities, readiness for its adoption, and the key barriers to their integrating. AIM. To evaluate the attitudes of orthopedic trauma surgeons toward AI, with a specific focus on its application for the autonomous interpretation of radiological studies. METHODS. A cross-sectional anonymous online survey was conducted among orthopedic trauma surgeons working in Moscow healthcare institutions. Data was collected using an adapted version of the validated ATRAI-14 questionnaire, designed to assess radiologists’ attitudes toward AI. Data were analyzed using descriptive statistics. RESULTS. The majority of orthopedic trauma surgeons (62.8%) reported no prior experience with AI for radiological interpretation, and 41.9% indicated a lack of access to such tools in their current practice. Nevertheless, 39.5% expressed willingness to use AI for analyzing radiological studies, while only 18.6% reported actual use of it. The most highly valued functionality, cited by more than one-third of respondents (32.6%), was autonomous triage of studies into “normal” versus “pathological,” followed by specialist interpretation of identified abnormalities supported by decision-support systems. CONCLUSIONS. Orthopedic trauma surgeons’ attitudes toward AI are neutral and skeptical, reflecting the early stage of innovation adoption in clinical practice. To shift this perception, it is essential to build a robust evidence base for the effectiveness of AI and to implement educational initiatives aimed to develop relevant competencies among specialists.

Background: Inflammatory bowel diseases (IBDs) are becoming increasingly prevalent worldwide. They affect not only physical, but also psychological well-being and are associated with anxiety and depression, which worsen the disease progression and reduce patients' quality of life. Fear of their disease or limited access to consistent medical care may also make a negative contribution. Telemonitoring seems to be a promising solution of this problem, as it has been shown to be effective in management of non-communicable chronic diseases. Remote monitoring provides access to educational resources and medical support regardless of a geographic distance between the patient and the doctor. It may be also considered a potential tool to support psychological well-being of IBD patients. Aim: To evaluate the impact of telemonitoring on changes over time in psychological well-being of IBD patients compared to standard care and follow-up. Methods: This was an interventional prospective randomized controlled study. The study patients were consecutively enrolled from April 2023 to April 2024 in the Department of Gastroenterology, University Clinical Hospital No. 1 (Sechenov University). The patients meeting the inclusion criteria (age over 18 years, with confirmed diagnosis of IBD, having signed a written informed consent to participate in the study) were randomized in a 1:1 ratio to either telemonitoring or standard care groups. At baseline and after 6 months of the follow-up they were assessed for the disease activity parameters (Simple Clinical Colitis Activity Index / Harvey Bradshaw Index, SCCAI / HBI questionnaire), depression and anxiety (Hospital Anxiety and Depression Scale, HADS), alexithymia (26-item Toronto Alexithymia Scale-26, TAS-26), and visceral sensitivity (Visceral Sensitivity Index, VSI) levels. The telemedicine intervention was done through a custom web platform with monthly audio check-ups to track the patients’ condition. The analysis of the questionnaires was based solely on the data from the available patients, and no missing data was imputed. Results: One hundred and sixteen (116) patients were initially enrolled into the study; 48 of them were excluded (due to pregnancy, 4; withdrawal of consent, 44), resulting in a final sample of 68 patients (30 women) with a median age of 30.5 years. There were 32 patients in the telemonitoring group and 36 in the standard care group. At baseline, 57.4% (n = 39) of the participants were in clinical remission, 47% (n = 32) exhibited anxiety or depression symptoms, 92.4% (n = 61) had visceral hypersensitivity, and 58.7% (n = 37) were alexithymic. There were no significant between-group differences at baseline. At 6 months of the follow-up, the telemonitoring group showed lower HADS and VSI scores compared to the standard care group (anxiety 5.5 vs 8, p = 0.025; depression 4 vs 6, p = 0.04; visceral sensitivity: 21.5 vs 30, p = 0.013, respectively). No significant differences between the groups were found for alexithymia or disease activity scores. Conclusion: Telemonitoring facilitates an improvement of psychological well-being in IBD patients compared to conventional face-to-face follow-up. The integration of telemonitoring into the current standards of care could enhance medical care for IBD patients, especially those with anxiety and depression symptoms or visceral hypersensitivity.

Background/Objectives: Large language models (LLMs) are increasingly considered for use in radiology, including the summarization of patient medical records to support radiologists in processing large volumes of data under time constraints. This task requires not only accuracy and completeness but also clinical applicability. Automatic metrics and general-purpose questionnaires fail to capture these dimensions, and no standardized tool currently exists for the expert evaluation of LLM-generated summaries in radiology. Here, we aimed to develop and validate such a tool. Methods: Items for the questionnaire were formulated and refined through focus group testing with radiologists. Validation was performed on 132 LLM-generated summaries of 44 patient records, each independently assessed by radiologists. Criterion validity was evaluated through known-group differentiation and construct validity through confirmatory factor analysis. Results: The resulting seven-item instrument, ELEGANCE (Expert-Led Evaluation of Generative AI Competence and Excellence), demonstrated excellent internal consistency (Cronbach’s α = 0.95). It encompasses seven dimensions: relevance, completeness, applicability, falsification, satisfaction, structure, and correctness of language and terminology. Confirmatory factor analysis supported a two-factor structure (content and form), with strong fit indices (RMSEA = 0.079, CFI = 0.989, TLI = 0.982, SRMR = 0.029). Criterion validity was confirmed by significant between-group differences (p < 0.001). Conclusions: ELEGANCE is the first validated tool for expert evaluation of LLM-generated medical record summaries for radiologists, providing a standardized framework to ensure quality and clinical utility.