BACKGROUND: Chest X-ray is the primary modality for screening malignant lung neoplasms, especially solitary pulmonary nodules, which are the most common. Enhancing the accuracy of lung nodule detection facilitates timely medical intervention and enhances the likelihood of achieving a favorable therapeutic outcome. One approach to enhancing the efficiency of lung nodule detection in chest X-rays involves adopting novel techniques, such as those based on artificial intelligence. However, concerns regarding the effectiveness of integrating these technologies into clinical practice remain largely unaddressed due to insufficient data on radiologists' performance metrics. AIM: This study aims to assess the diagnostic performance of 100 radiologists in identifying lung nodules on chest X-ray images. METHODS: Each of 100 radiologist was asked to evaluate 100 chest radiographs, of which 50 contained abnormal findings while the other 50 were normal. The presence of lung nodules was assessed using the following scale: Absent (0 on the probability scale), likely absent (0.25), undecided (0.50), likely present (0.75), present (1.00). The validation of the presence or absence of pulmonary nodules was performed using a binary scale (0/1) by three expert physicians based on chest CT data acquired no more than 14 days after the chest X-ray. The study assessed the image interpretation time, the difference in performance between radiologists and expert physicians (expressed in absolute units as Delta), and the primary diagnostic accuracy metrics of the radiologists. RESULTS: The test yielded a ROC AUC of 0.858±0.059, accuracy of 0.822±0.048, sensitivity of 0.779±0.097, and specificity of 0.864±0.095. The results demonstrated a negligible positive correlation between expert accuracy and average study processing time (Spearman correlation coefficient rs =0.189) and a low positive correlation (rs =0.344) between study processing time and the Delta value. CONCLUSION: The obtained results can be used to assess the quality of automated detection systems under development, as well as to evaluate the efficacy of alternative methods and approaches for pulmonary nodule detection.

Background. The regulated procedure for quality control of radiography involves the analysis of test objects without simulating anatomical features. However, for subsequent assessment of the quality of work of radiologists or decision support systems using artificial intelligence, it is necessary to use anthropomorphic test objects (phantoms), the X-ray image of which will be as close as possible to the analyzed images of patients. Aim. Development and testing of an anthropomorphic aluminum phantom for digital radiography Materials and methods. A digital radiograph of the chest of a healthy person was used as a standard when designing the phantom. A sample of the designed phantom was made by layer-by-layer milling of an aluminum blank. Validation was carried out by comparing the phantom images and the reference image. In addition, an assessment was made of the effect of the phantom sample on the spatial resolution and contrast sensitivity for various shooting modes of X-ray machine. The applicability of the phantom for modeling focal lung lesions was studied, with an assessment of the results by experts. Results. It was found that the phantom reproduces the reference image with a high degree of reliability and allows simulating pathological changes in chest X-rays by placing plastic pads in the required position. The ratios of attenuation coefficients for different anatomical areas during X-ray imaging of the phantom and three patients were identified. Conclusion. The study presents a method for developing an anthropomorphic phantom made of D16T aluminum alloy, designed for calibrating X-ray systems and testing radiology specialists. The phantom imitates the anatomical structures of the chest organs and pathological changes, such as focal lesions. The main advantages of the solution include reduced production costs, compactness and high accuracy of reproduction of X-ray characteristics.

Early diagnosis of thoracic aortic aneurysms and pathological pulmonary trunk dilation is crucial to prevent severe complications, including vascular wall rupture and acute right ventricular failure, and reduce cardiovascular mortality. This review examines contemporary imaging approaches for these conditions, focusing on computed tomography as the gold standard modality. Emphasis was placed on the implementation of artificial intelligence technologies, which enable automatic segmentation of vascular structures, measurement of their diameter, and opportunistic screening, allowing early detection of asymptomatic conditions without additional diagnostic procedures, thereby reducing radiologist workload and improving medical care quality. The study comprehensively analyzed the Moscow Experiment, wherein the application of artificial intelligence in medical image analysis showed high sensitivity, reproducibility, and reduced reporting time. Despite these significant advantages, the need for expert supervision of artificial intelligence-generated results to ensure diagnostic accuracy and reliability is emphasized. Moreover, the review highlights the importance of adapting algorithms to different scanning protocols and population-specific features. Additionally, the importance of interdisciplinary collaboration among cardiologists, radiologists, data scientists, and software developers for the effective integration into routine clinical practice is pointed out. Therefore, the review outlines the potential of artificial intelligence technologies to enhance diagnostic quality and underscores the need for further clinical research and standardization of methods for successful integration into daily practice.

BACKGROUND: The validation of quantitative cerebral blood flow assessment using non-contrast magnetic resonance imaging remains unresolved. The optimal approach involves applying a method based on a different physiological model to enhance the reliability of the obtained data. AIM: To verify the results of quantitative cerebral tissue blood flow assessment using non-contrast MRI against quantitative 2D phase-contrast angiography in healthy adults. METHODS: The prospective study enrolled healthy adults (aged 18–75 years). Cerebral perfusion was assessed using non-contrast magnetic resonance imaging, while macrovascular blood flow was measured in the vertebral and internal carotid arteries using quantitative 2D phase-contrast angiography. Brain volume and relative mass were evaluated based on T1-weighted image segmentation. Macrovascular blood flow values were converted into tissue perfusion metrics through mathematical adjustment accounting for brain mass. RESULTS: In the study 80 adults were examined using both methods. Non-contrast magnetic resonance imaging revealed mean perfusion values of 17.88 ± 2.39 mL/100g/min in white matter and 42.06 ± 7.13 mL/100g/min in gray matter, with total cerebral perfusion at 59.63 ± 8.56 mL/100g/min. Total cerebral perfusion calculated from phase-contrast angiography and arterial blood flow velocity was 58.96 ± 8.16 mL/s. A strong positive correlation was found between total cerebral perfusion values derived from non-contrast magnetic-resonance and phase-contrast angiography (r = 0.892; p 0.001). CONCLUSION: A strong positive correlation was demonstrated between cerebral perfusion values obtained via non-contrast magnetic resonance imaging and phase-contrast angiography, despite their reliance on distinct physiological models.

BACKGROUND: With the increasing volume of data, laboratory medicine requires automation and standardization of routine processes to reduce workload on healthcare professionals and clear their time for more specialized tasks. Machine learning models and artificial neural networks support image recognition and analysis of large data sets, which allows their integration into laboratory workflows to solve routine tasks. AIM: This study aimed to analyze global scientific publications on the application of artificial intelligence technologies in laboratory medicine and their potential to address current challenges and identify barriers in their integration into laboratory workflows. METHODS: A search for publications was conducted using PubMed, manufacturer websites offering ready-to-use laboratory solutions, and reference lists from other reviews. The Mendeley software was utilized for bibliographic data management. The search covered the time interval 2019–2024. Obtained data included bibliometric indicators, research areas, key methodological characteristics, diagnostic effectiveness values for artificial intelligence systems and healthcare professionals, the number and experience of involved healthcare professionals, and validated outcomes of artificial intelligence implementation. The study quality was assessed using a modified QUADAS-CAD checklist. RESULTS: Twenty-three publications presenting studies at the pre-analytical (n = 1), analytical (n = 19), and post-analytical (n = 3) stages of laboratory analysis were included. Most studies focused on cytology and microbiology, accounting for 48% and 35% of the studies, respectively. Artificial intelligence demonstrated high effectiveness in solving tasks across all stages of the laboratory process. Moreover, its diagnostic accuracy was comparable to that of healthcare professionals; however, decision-making speed was higher. All studies demonstrated a risk of systematic bias, which was associated with unbalanced samples, lacking external data validation, and incomplete description of datasets and analytical methods. CONCLUSION: Artificial intelligence demonstrates high potential in diagnostic accuracy and processing speed, making it a promising tool to be integrated into laboratory practice and automation of routine processes. However, to achieve this, research methodologies for artificial intelligence should be standardized to reduce the risk of systematic bias, establish reference values for laboratories to ensure the reproducibility and generalizability of results, raise awareness among healthcare professionals and patients on how artificial intelligence works to overcome prejudices, and develop reliable mechanisms for protecting personal data when using artificial intelligence.

This article introduces a new submission policy for original research manuscripts. Starting next year, such manuscripts will be considered only if they comply with the reporting guidelines recommended by the EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network and are approved by an ethics committee during the planning stage. The information to be included in a manuscript upon submission has been expanded. When these rules are fully implemented, prior study registration and submission of primary research data to the editorial office (with subsequent publication upon article acceptance) will become mandatory for manuscript consideration. These changes aim to transition the journal from organic growth to controlled development according to the principles of scientific integrity.

Mental disorders are a significant medical and social issue globally. Currently, approximately 970 million individuals suffer from mental disorders, with over 300 million diagnosed with depression or bipolar disorder. Recently, there has been significant advancement in digital technologies, particularly in artificial intelligence, encompassing machine learning and deep learning. Given the growing interest in their use in psychiatry and the need to develop new approaches to psychiatric care. This review explores the current and promising directions for the application of artificial intelligence technologies in clinical practice, focusing on patients with depression and bipolar disorder. A literature search was conducted from January to February 2024 in the databases PubMed, Google Scholar, and eLibrary using the following keywords: «психиатрия» ("psychiatry"), «психическое здоровье» ("mental health"), «психическое расстройство» ("psychiatric disorder"), «депрессия» ("depression"), «депрессивный эпизод» ("depressive episode"), «рекуррентное депрессивное расстройство» ("recurrent brief depression"), «биполярное расстройство» ("bipolar disorder"), «машинное обучение» ("machine learning"), «глубокое обучение» ("deep learning"), «искусственный интеллект» ("artificial intelligence"); "psychiatry", "mental health", "psychiatric disorder", "depression", "depressive episode", "major depressive disorder", "bipolar disorder", "machine learning", "deep learning", "artificial intelligence". Studies on the use of artificial intelligence technologies in patients with depression and bipolar disorders and review articles discussing the difficulties of their application in psychiatry were excluded. Publications in Russian and English in the past 10 years were selected. The most commonly used machine learning models for diagnosing patients with affective disorders utilize neuroimaging data (primarily magnetic resonance imaging and electroencephalography), text, audio, and video data and data from electronic devices, molecular-genetic markers, and clinical indicators. The models were trained using mono- or multimodal datasets. Notably, many of the reviewed studies have significant limitations, making the implementation of artificial intelligence technologies in clinical practice challenging. These include small sample sizes, low representativeness and standardization, inclusion of “noise” and correlated variables, and absence of validation using independent datasets. Studies on machine learning methods have demonstrated promising results in the early diagnosis of affective episodes and in predicting treatment responses. However, their clinical application is limited, owing to insufficient validation. Well-designed prospective cohort studies and the creation of extensive, high-quality datasets and models capable of uncovering new relationships between variables are required to address this limitation.

BACKGROUND: Recently, significant efforts have been undertaken to find potential noninvasive biomarkers for predicting the response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy. AIM: To assess the texture characteristics of locally advanced rectal cancer in primary T2-weighted imaging (T2-WI) as a potential predictor for the efficacy of standard neoadjuvant chemoradiotherapy and develop a prediction system for the efficacy of neoadjuvant chemoradiotherapy based on them. MATERIALS AND METHODS: The retrospective study enrolled 82 patients with locally advanced rectal cancer who received combination treatment with neoadjuvant chemoradiotherapy. Patient data were divided into the training (n=58) and control (n=24) sets. For texture analysis, primary high-resolution T2-WI at the level of the tumor center, oriented perpendicular to the intestinal wall, was used. The texture analysis was performed by second-order statistics based on the gray-level co-occurrence matrices using MAZDA ver. 4.6 featuring the calculation of 11 texture parameters. In the training set, based on the morphological assessment of surgical specimens, significantly different texture analysis parameters were found for two groups of patients: neoadjuvant chemoradiotherapy responders (good prognosis group) and nonresponders (poor prognosis group). Accordingly, a scoring system was created for assessing the efficacy of neoadjuvant chemoradiotherapy. The system was tested on the control set, and diagnostic efficacy parameters were determined. RESULTS: In the training set, the good and poor prognosis groups differed significantly in five texture parameters: AngScMom (p=0.021), SumofSqs (p=0.003), SumEntrp (p=0.003), Entropy (p=0.038), and SumVarnc (p=0.015), for which the cutoff points were found. These parameters were applied to create the scoring system (excluding the Entropy parameter, which had a strong direct correlation with SumEntrp and the lowest area under the curve, and SumofSqs, which had low reproducibility). The diagnostic efficiency of the scoring system for predicting the response had sensitivity, specificity, positive-predictive value, and negative- predictive value of 72%, 69%, 70%, and 71% for the training set and 80%, 64%, 62%, and 82% for the control set, respectively. The areas under the ROC curve were 0.77 and 0.72 for the training and control sets, respectively. CONCLUSIONS: Texture analysis of the primary T2-WI of tumors in patients with locally advanced rectal cancer allows for predicting the efficacy of neoadjuvant chemoradiotherapy with moderate diagnostic efficiency. The results suggest good prospects for further research in this area.

Nowadays, computed tomography urography and multiparametric magnetic resonance are the most often used imaging techniques in patients with bladder cancer; however, dual-energy computed tomography is making its way into the oncological field. This narrative review article aimed to show the fundamentals of dual-energy computed tomography by outlining its physical principles, techniques, protocols, and postprocessing images to help those who used this cutting-edge technology as the first approach to fully understand its emerging applications in the evaluation of bladder cancer, a field yet to be explored. In particular, we discuss the usefulness of dual-energy computed tomography by focusing on the main images obtained, such as the iodine map, comparing them to the images obtained with conventional computed tomography scans. Dual-energy computed tomography applications may be beneficial for bladder cancer diagnosis, staging, and treatment planning. Nevertheless, its application is limited to its availability in healthcare structures and the training of healthcare personnel who can perform and interpret the scans correctly.

BACKGROUND: Osteoporosis is often diagnosed at the stage with complications, i.e., low-energy fractures. Vertebral compression fractures, which are complications of osteoporosis and predictors of subsequent fractures, are often asymptomatic. Compression fractures can be found by computed tomography performed for other indications with vertebral morphometry. Approaches to using artificial intelligence algorithms designed for diagnosing vertebral compression fractures were analyzed. AIM: Testing artificial intelligence algorithms to conduct morphometric analysis of vertebrae on chest computed tomography scans and assess the possibility of their implementation in medical organizations of the Moscow Healthcare Department. MATERIALS AND METHODS: To set a clinical task for artificial intelligence algorithms, basic diagnostic requirements in the area of “vertebral compression fractures (osteoporosis)” were formulated. The testing of the artificial intelligence algorithms included the following stages: self-testing, functional and calibration testing, practical evaluation, and operation testing. The first three stages of testing were performed using previously generated datasets. At practical evaluation and operation testing, artificial intelligence algorithms analyzed the data from computed tomography performed in medical organizations. The expert group of radiologists assessed the diagnostic accuracy and functional capacity of the AI algorithms at all stages. The resulting quantitative metrics of the accuracy of artificial intelligence algorithms were compared with the required target values. RESULTS: From June 2021 to June 2022, two artificial intelligence algorithms (Nos. 1 and 2) with different methods of detecting compression fractures were tested. Both artificial intelligence algorithms successfully passed the self-testing (6 tests), functional (5 tests), and calibration (100 tests) stages. The area under the ROC curve for artificial intelligence algorithm No. 1 was 0.99 (95% CI, 0.98–1), and for artificial intelligence algorithm No. 2, it was 0.91 (95% CI, 0.85–0.96). Artificial intelligence algorithm No. 1 passed the practical evaluation stage without any significant remarks, whereas algorithm No. 2 was sent for fine-tuning. After the operation testing stage, the following accuracy metrics were obtained: the areas under the ROC curve for artificial intelligence algorithm Nos. 1 and 2 were 0.93 (95% CI, 0.89–0.96) and 0.92 (95% CI, 0.90–0.94), respectively. At all stages, both artificial intelligence algorithms demonstrated sufficient metrics for clinical validation. CONCLUSION: Artificial intelligence algorithms for the automatic diagnosis of vertebral compression fractures have been tested, demonstrating the high quality of their operation. artificial intelligence algorithms can be applied as a supplementary tool in the medical decision support system.