TY - JOUR A1 - Bilous, Nataliya A1 - Malko, Vladyslav A1 - Frohme, Marcus A1 - Nechyporenko, Alina T1 - Comparison of CNN-Based Architectures for Detection of Different Object Classes JF - AI N2 - (1) Background: Detecting people and technical objects in various situations, such as natural disasters and warfare, is critical to search and rescue operations and the safety of civilians. A fast and accurate detection of people and equipment can significantly increase the effectiveness of search and rescue missions and provide timely assistance to people. Computer vision and deep learning technologies play a key role in detecting the required objects due to their ability to analyze big volumes of visual data in real-time. (2) Methods: The performance of the neural networks such as You Only Look Once (YOLO) v4-v8, Faster R-CNN, Single Shot MultiBox Detector (SSD), and EfficientDet has been analyzed using COCO2017, SARD, SeaDronesSee, and VisDrone2019 datasets. The main metrics for comparison were mAP, Precision, Recall, F1-Score, and the ability of the neural network to work in real-time. (3) Results: The most important metrics for evaluating the efficiency and performance of models for a given task are accuracy (mAP), F1-Score, and processing speed (FPS). These metrics allow us to evaluate both the accuracy of object recognition and the ability to use the models in real-world environments where high processing speed is important. (4) Conclusion: Although different neural networks perform better on certain types of metrics, YOLO outperforms them on all metrics, showing the best results of mAP-0.88, F1-0.88, and FPS-48, so the focus was on these models. KW - EfficientDet KW - SSD KW - YOLO KW - deep learning KW - neural network KW - object detection Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19634 VL - 5 IS - 4 SP - 2300 EP - 2320 PB - MDPI ER - TY - JOUR A1 - Przymus, Piotr A1 - Rykaczewski, Krzysztof A1 - Martín-Segura, Adrián A1 - Truu, Jaak A1 - De Santa Pau, Enrique Carrillo A1 - Kolev, Mikhail A1 - Naskinova, Irina A1 - Gruca, Aleksandra A1 - Sampri, Alexia A1 - Frohme, Marcus A1 - Nechyporenko, Alina T1 - Deep Learning in Microbiome Analysis: A Comprehensive Review of Neural Network Models JF - Frontiers in Microbiology N2 - Microbiome research, the study of microbial communities in diverse environments, has seen significant advances due to the integration of deep learning (DL) methods. These computational techniques have become essential for addressing the inherent complexity and high-dimensionality of microbiome data, which consist of different types of omics datasets. Deep learning algorithms have shown remarkable capabilities in pattern recognition, feature extraction, and predictive modeling, enabling researchers to uncover hidden relationships within microbial ecosystems. By automating the detection of functional genes, microbial interactions, and host-microbiome dynamics, DL methods offer unprecedented precision in understanding microbiome composition and its impact on health, disease, and the environment. However, despite their potential, deep learning approaches face significant challenges in microbiome research. Additionally, the biological variability in microbiome datasets requires tailored approaches to ensure robust and generalizable outcomes. As microbiome research continues to generate vast and complex datasets, addressing these challenges will be crucial for advancing microbiological insights and translating them into practical applications with DL. This review provides an overview of different deep learning models in microbiome research, discussing their strengths, practical uses, and implications for future studies. We examine how these models are being applied to solve key problems and highlight potential pathways to overcome current limitations, emphasizing the transformative impact DL could have on the field moving forward. KW - clasiffication KW - clustering KW - deep learning KW - natural language processing KW - microbiome Y1 - 2025 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19889 SN - 1664-302X VL - 15 PB - Frontiers ER - TY - JOUR A1 - Alekseeva, Victoriia A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Gargin, Vitaliy A1 - Meniailov, Ievgen A1 - Chumachenko, Dmytro T1 - Intelligent Decision Support System for Differential Diagnosis of Chronic Odontogenic Rhinosinusitis Based on U-Net Segmentation JF - Electronics N2 - The share of chronic odontogenic rhinosinusitis is 40% among all chronic rhinosinusitis. Using automated information systems for differential diagnosis will improve the efficiency of decision-making by doctors in diagnosing chronic odontogenic rhinosinusitis. Therefore, this study aimed to develop an intelligent decision support system for the differential diagnosis of chronic odontogenic rhinosinusitis based on computer vision methods. A dataset was collected and processed, including 162 MSCT images. A deep learning model for image segmentation was developed. A 23 convolutional layer U-Net network architecture has been used for the segmentation of multi-spiral computed tomography (MSCT) data with odontogenic maxillary sinusitis. The proposed model is implemented in such a way that each pair of repeated 3 × 3 convolutions layers is followed by an Exponential Linear Unit instead of a Rectified Linear Unit as an activation function. The model showed an accuracy of 90.09%. To develop a decision support system, an intelligent chatbot allows the user to conduct an automated patient survey and collect patient examination data from several doctors of various profiles. The intelligent information system proposed in this study made it possible to combine an image processing model with a patient interview and examination data, improving physician decision-making efficiency in the differential diagnosis of Chronic Odontogenic Rhinosinusitis. The proposed solution is the first comprehensive solution in this area. KW - CT data KW - deep learning KW - image segmentation KW - U-Net KW - decision support system KW - chronic odontogenic rhinosinusitis KW - machine learning KW - artificial intelligence Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-17088 SN - 2079-9292 VL - 12 IS - 5 PB - MDPI ER - TY - JOUR A1 - Chumachenko, Dmytro A1 - Butkevych, Mykola A1 - Lode, Daniel A1 - Frohme, Marcus A1 - Schmailzl, Kurt J. G. A1 - Nechyporenko, Alina T1 - Machine Learning Methods in Predicting Patients with Suspected Myocardial Infarction Based on Short-Time HRV Data JF - Sensors N2 - Diagnosis of cardiovascular diseases is an urgent task because they are the main cause of death for 32% of the world’s population. Particularly relevant are automated diagnostics using machine learning methods in the digitalization of healthcare and introduction of personalized medicine in healthcare institutions, including at the individual level when designing smart houses. Therefore, this study aims to analyze short 10-s electrocardiogram measurements taken from 12 leads. In addition, the task is to classify patients with suspected myocardial infarction using machine learning methods. We have developed four models based on the k-nearest neighbor classifier, radial basis function, decision tree, and random forest to do this. An analysis of time parameters showed that the most significant parameters for diagnosing myocardial infraction are SDNN, BPM, and IBI. An experimental investigation was conducted on the data of the open PTB-XL dataset for patients with suspected myocardial infarction. The results showed that, according to the parameters of the short ECG, it is possible to classify patients with a suspected myocardial infraction as sick and healthy with high accuracy. The optimized Random Forest model showed the best performance with an accuracy of 99.63%, and a root mean absolute error is less than 0.004. The proposed novel approach can be used for patients who do not have other indicators of heart attacks. KW - myocardial infraction KW - heart rate variability KW - 10-second heart rate variability KW - diagnostics KW - machine learning KW - k-nearest neighbor classifier KW - radial basis function KW - decision tree KW - random forest Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-16521 SN - 1424-8220 VL - 22 IS - 18 PB - MDPI ER - TY - JOUR A1 - Pfeil, Juliane A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Hufert, Frank T. A1 - Schulze, Katja T1 - Examination of blood samples using deep learning and mobile microscopy JF - BMC Bioinformatics N2 - Microscopic examination of human blood samples is an excellent opportunity to assess general health status and diagnose diseases. Conventional blood tests are performed in medical laboratories by specialized professionals and are time and labor intensive. The development of a point-of-care system based on a mobile microscope and powerful algorithms would be beneficial for providing care directly at the patient's bedside. For this purpose human blood samples were visualized using a low-cost mobile microscope, an ocular camera and a smartphone. Training and optimisation of different deep learning methods for instance segmentation are used to detect and count the different blood cells. The accuracy of the results is assessed using quantitative and qualitative evaluation standards. Y1 - 2022 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-15802 SN - 1471-2105 VL - 23 PB - BioMed Central ER - TY - JOUR A1 - D'Elia, Domenica A1 - Truu, Jaak A1 - Lahti, Leo A1 - Berland, Magali A1 - Papoutsoglou, Georgios A1 - Ceci, Michelangelo A1 - Zomer, Aldert A1 - Lopes, Marta B. A1 - Ibrahimi, Eliana A1 - Gruca, Aleksandra A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Klammsteiner, Thomas A1 - Carrillo de Santa Pau, Enrique A1 - Marcos-Zambrano, Laura Judith A1 - Hron, Karel A1 - Pio, Gianvito A1 - Simeon, Andrea A1 - Suharoschi, Ramona A1 - Moreno-Indias, Isabel A1 - Temko, Andriy A1 - Nedyalkova, Miroslava A1 - Apostol, Elena-Simona A1 - Truică, Ciprian-Octavian A1 - Shigdel, Rajesh A1 - Telalović, Jasminka Hasić A1 - Bongcam-Rudloff, Erik A1 - Przymus, Piotr A1 - Jordamović, Naida Babić A1 - Falquet, Laurent A1 - Tarazona, Sonia A1 - Sampri, Alexia A1 - Isola, Gaetano A1 - Pérez-Serrano, David A1 - Trajkovik, Vladimir A1 - Klucar, Lubos A1 - Loncar-Turukalo, Tatjana A1 - Havulinna, Aki S. A1 - Jansen, Christian A1 - Bertelsen, Randi J. A1 - Claesson, Marcus Joakim T1 - Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action JF - Frontiers in Microbiology N2 - The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish “gold standard” protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory ‘omics’ features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices. KW - microbiome KW - machine learning KW - artificial intelligence KW - standard KW - best practice Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18004 SN - 1664-302X VL - 14 PB - Frontiers ER - TY - CHAP A1 - Alekseeva, Victoriia A1 - Reshetnik, Viktor A1 - Frohme, Marcus A1 - Kachailo, Irina A1 - Murizyna, Irina A1 - Nechyporenko, Alina ED - Chumachenko, Dmytro ED - Kaur, Jasleen ED - Yakovlev, Sergiy ED - Morita, Plinio P. T1 - Investigation of the Impact of Insulin Resistance on the Bone Density of the Upper Wall of the Maxillary Sinus T2 - Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023 N2 - The aim of our study was to investigate the impact of insulin resistance on the bone density of the upper wall of the maxillary sinus. Materials and Methods: The study included 100 female participants aged 18 to 44 years, divided into two groups. The first group consisted of individuals with insulin resistance, while the control group comprised individuals without signs of insulin resistance. In each group, we conducted an investigation of the radiological density of the upper wall of the maxillary sinus using uncertainty calculations. Results of the study suggest a potential influence of insulin resistance on the density of bone tissue around the nasal sinuses, specifically the upper wall of the maxillary sinus in our case. This parameter was found to be minimal in the group of individuals with insulin resistance. It is particularly noteworthy that both minimum and maximum bone density decreased in this group. Conclusions. The research focused on how insulin resistance affects the density of the upper wall of the maxillary sinus. By employing uncertainty calculations, the study revealed that insulin resistance is associated with a decrease in the minimum density of the upper wall of the maxillary sinus. This tendency may act as a catalyst for the emergence of significant inflammatory alterations in the nasal sinuses, serving as a foundation for the initiation of complications. KW - bone density KW - multispiral computer tomography KW - uncertainty KW - paranasal sinus KW - resistance to insulin Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:0074-3641-3 UR - https://ceur-ws.org/Vol-3641/short1.pdf VL - 3641 SP - 216 EP - 223 PB - CEUR-WS.org ER - TY - JOUR A1 - Gavrylenko, Svitlana A1 - Poltoratskyi, Vadym A1 - Nechyporenko, Alina T1 - Intrusion Detection Model Based on Improved Transformer JF - Advanced Information Systems N2 - The object of the study is the process of identifying the state of a computer network. The subject of the study are the methods of identifying the state of computer networks. The purpose of the paper is to improve the efficacy of intrusion detection in computer networks by developing a method based on transformer models. The results obtained. The work analyzes traditional machine learning algorithms, deep learning methods and considers the advantages of using transformer models. A method for detecting intrusions in computer networks is proposed. This method differs from known approaches by utilizing the Vision Transformer for Small-size Datasets (ViTSD) deep learning algorithm. The method incorporates procedures to reduce the correlation of input data and transform data into a specific format required for model operations. The developed methods are implemented using Python and the GOOGLE COLAB cloud service with Jupyter Notebook. Conclusions. Experiments confirmed the efficiency of the proposed method. The use of the developed method based on the ViTSD algorithm and the data preprocessing procedure increases the model's accuracy to 98.7%. This makes it possible to recommend it for practical use, in order to improve the accuracy of identifying the state of a computer system. KW - data preprocessing KW - machine learning KW - neural network KW - RNN KW - SVM KW - KNN KW - transformer model KW - vision transformer Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-18750 SN - 2522-9052 VL - 8 IS - 1 SP - 94 EP - 99 PB - National Technical University Kharkiv Polytechnic Institute ER - TY - JOUR A1 - Nechyporenko, Alina A1 - Frohme, Marcus A1 - Strelchuk, Yaroslav A1 - Omelchenko, Vladyslav A1 - Gargin, Vitaliy A1 - Ishchenko, Liudmyla A1 - Alekseeva, Victoriia T1 - Galvanic Skin Response and Photoplethysmography for Stress Recognition Using Machine Learning and Wearable Sensors JF - Applied Sciences N2 - This study investigates stress recognition using galvanic skin response (GSR) and photoplethysmography (PPG) data and machine learning, with a new focus on air raid sirens as a stressor. It bridges laboratory and real-world conditions and highlights the reliability of wearable sensors in dynamic, high-stress environments such as war and conflict zones. The study involves 37 participants (20 men, 17 women), aged 20–30, who had not previously heard an air raid siren. A 70 dB “S-40 electric siren” (400–450 Hz) was delivered via headphones. The protocol included a 5 min resting period, followed by 3 min “no-stress” phase, followed by 3 min “stress” phase, and finally a 3 min recovery phase. GSR and PPG signals were recorded using Shimmer 3 GSR+ sensors on the fingers and earlobes. A single session was conducted to avoid sensitization. The workflow includes signal preprocessing to remove artifacts, feature extraction, feature selection, and application of different machine learning models to classify the “stress “and “no-stress” states. As a result, the best classification performance was shown by the k-Nearest Neighbors model, achieving 0.833 accuracy. This was achieved by using a particular combination of heart rate variability (HRV) and GSR features, which can be considered as new indicators of siren-induced stress. KW - galvanic skin response KW - machine learning KW - photoplethysmography KW - stress Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19803 VL - 14 IS - 24 PB - MDPI ER - TY - JOUR A1 - Nechyporenko, Alina A1 - Nazaryan, Rosana A1 - Semko, Galyna A1 - Lupyr, Andrii A1 - Yurevych, Nadiia A1 - Fomenko, Yuliia A1 - Kostiukov, Eduard A1 - Alekseeva, Victoriia T1 - Application of spiral computed tomography for determination of the minimal bone density variability of the maxillary sinus walls in chronic odontogenic and rhinogenic sinusitis JF - Ukrainian Journal of Radiology and Oncology N2 - Background. There are many questions regarding the peculiarities of the course of odontogenic maxillary sinusitis, the likelihood of complications and the correct dosage of load during dental implantation. Purpose – to identify changes in minimal bone density in chronic odontogenic maxillary sinusitis and to compare it with chronic rhinogenic catarrhal maxillary sinusitis and physiological state of maxillary sinus. Materials and Methods. Our study involved 10 patients with chronic odontogenic maxillary sinusitis, 10 patients with chronic maxillary non-polyposis sinusitis of rhinogenic etiology, combined with hyperplasia of the sinus mucosa up to 1 cm. The control group comprised 10 patients with physiological condition of maxillary sinuses. In all subjects minimal bone density of superior, medial and inferior walls of maxillary sinus was analyzed. Results. The study showed that the maximum in physiological conditions is the minimal density of the upper wall of the maxillary sinus, the minimum is the lower wall. The minimal density of the lower wall was shown to undergo a statistically significant reduction in chronic catarrhal maxillary sinusitis only by 2%, the upper by 5%, the medial by 4% compared with the intact sinus, but with the odontogenic nature of maxillary sinusitis, this figure was 31% in the lower wall, 27% in the medial region. Only the density of the upper wall of the maxillary sinus remained quite stable, it decreased relative to the physiological one only by 6%. Conclusions. Minimal physiological densitometric parameters of lower, medial and upper walls were calculated. The reduction of minimal density of bone tissue in the cases of rhinogenic chronic catarrhal maxillary sinusitis. In the cases of chronic odontogenic maxillary sinusitis, maximum significant decrease of minimal density of both lower and medial walls is determined. KW - bone density KW - computed tomography KW - maxillary sinus KW - odontogenic maxillary sinusitis Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19121 SN - 2708-7166 VL - 29 IS - 4 SP - 65 EP - 75 ER - TY - CHAP A1 - Nechyporenko, Alina A1 - Reshetnik, Viktor A1 - Frohme, Marcus A1 - Alekseeva, Victoriia A1 - Lupyr, Andrii A1 - Gargin, Vitaliy ED - Chumachenko, Dmytro ED - Golland, Polina ED - Yakovlev, Sergiy T1 - Evaluation of Bone Radiodensity in Smokers T2 - Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024 N2 - One of the most prevalent harmful habits that affect people daily is smoking. The aim of our study was to investigate changes in the bone density of the human paranasal sinuses under the influence of smoking using uncertainty calculation. Material and Methods. The study involved 150 male and female individuals aged 20-40 years, which helps to exclude other factors that could influence the reduction in bone radiodensity (e.g., menopause in women, age-related changes, etc.). The participants were divided into three groups based on the duration of their smoking habits. Results. Smoking has almost no effect on the maximum bone density. All groups show nearly identical values. In the first group, consisting of individuals who smoked for up to 5 years, the maximum density is 75.22 ± 37.61 Hu. In the second group, those who smoked for up to 10 years have a density of 84.22 ± 42.11 Hu, while in the third control group, the density is 74.29 ± 37.15 Hu. On the other hand, smoking has a greater impact on the minimum radiological bone density of the upper wall of the maxillary sinus. The lowest values are found in the group of individuals who smoked for 10 or more years, with a minimum density of 23.86 ± 11.93 Hu. For those who smoked for up to 5 years, the radiological bone density was 52.65 ± 26.32 Hu. In the control group, the density values were slightly higher than those of individuals who smoked for up to 5 years, with the highest values being 58.12 ± 29.06 Hu. Conclusion. The study investigated changes in the bone density of the human paranasal sinuses under the influence of smoking, utilizing uncertainty calculation. It was found that the most sensitive indicator to nicotine exposure is the minimum radiological density, which can significantly increase the risk of developing complications. Specifically, the lowest radiological density was observed in the group of individuals who smoked for 10 years or more, with a value of 23.86 ± 11.93 Hu. KW - bone radiodensity KW - computer tomography KW - paranasal sinus KW - uncertainty Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19616 UR - https://ceur-ws.org/Vol-3777/short8.pdf IS - 3777 SP - 238 EP - 244 ER - TY - CHAP A1 - Reshetnik, Viktor A1 - Alekseeva, Victoriia A1 - Devos, Anastasiia A1 - Nazaryan, Rosana A1 - Gargin, Vitaliy A1 - Nechyporenko, Alina ED - Chumachenko, Dmytro ED - Kaur, Jasleen ED - Yakovlev, Sergiy ED - Morita, Plinio P. T1 - Implementation of the Uncertainty Calculation for the Detection of Negative Effect of Smoking on the Bone Density of Paranasal Sinuses T2 - Proceedings of the 3rd International Workshop of IT-professionals on Artificial Intelligence, ProfIT AI 2023, Waterloo, Canada, November 20-22, 2023 N2 - The aim was to implement uncertainty calculation for detecting the negative effects of smoking on the bone density of the paranasal sinus. Materials and Methods: A total of 100 male participants aged 20 to 44 were included in the study and divided into two groups. The first group comprised individuals with minimal harmful habits, while the second group consisted of individuals who had been smoking for at least 10 years, consuming 1 to 2 packs of cigarettes per day. Results Bone density has a negative impact on the bone tissue of the upper wall of the maxillary sinus. The findings suggest that individuals with a pronounced decrease in minimum density, as well as those with a marked difference between minimum and maximum density values, may require heightened medical attention due to potential associations with undiagnosed diseases or specific structural characteristics in the skull. Conclusions. The uncertainty calculation was implemented for the detection of negative effect of smoking on the bone density of paranasal sinuses. The calculated difference between maximum and minimum density during the research suggests significant medical implications, especially considering the heterogeneity of the trabecular bone structure in the skull. Individuals with a marked difference may require heightened medical attention, potentially associated with undiagnosed diseases or specific structural characteristics in the skull. KW - bone density KW - multispiral computer tomography KW - uncertainty KW - paranasal sinus KW - smoking Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19140 UR - https://ceur-ws.org/Vol-3641/short9.pdf IS - 3641 SP - 276 EP - 283 ER - TY - CHAP A1 - Reshetnik, Viktor A1 - Muryzina, Irina A1 - Frohme, Marcus A1 - Alekseeva, Victoriia A1 - Dzyza, Alla A1 - Nechyporenko, Alina ED - Chumachenko, Dmytro ED - Golland, Polina ED - Yakovlev, Sergiy T1 - Uncertainty Estimation Method for Determining Bone Density in Patients with Infiltrating Intraductal Carcinoma Undergoing Anti-Cancer Therapy T2 - Proceedings of the 4th International Workshop of IT-professionals on Artificial Intelligence (ProfIT AI 2024) 2024, Cambridge, MA, USA, September 25-27, 2024 N2 - Identification of a bone density is crucial in all branches of medicine. Assessing bone density is a labor-intensive process. Bone density is quite unstable and depends on many factors, both physiological (aging, hormonal balance) and pathological (medication use, various underlying conditions). The aim of our study is to determine bone density in patients with malignant breast tumors undergoing anti-cancer therapy. Material and Methods. The study included 50 women aged 60-70 years who were diagnosed with infiltrating intraductal carcinoma. According to established protocols, MSCT is recommended for this category of women at intervals of once every six months. The first MSCT scan was performed immediately after the diagnosis was made, before the start of treatment, and the second scan was conducted six months later. Results. The maximum radiological density was 75.8954±37.9477 Hu in the group of women who had been receiving treatment for six months, compared to 93.9388±46.9694 Hu in the group of patients who did not take the drug. Meanwhile, the minimum density showed a slight increase from 29.7295±14.8647 Hu to 38.6919±19.3460 Hu, which can be attributed to the compensatory mechanisms of the body. Conclusions. In the course of this study, bone density in patients with infiltrating intraductal carcinoma undergoing anti-cancer therapy was determined using uncertainty estimation. It was found that after six months, the first to respond to changes in density was the maximum bone density. KW - infiltrarive intraductal adenocarcinoma KW - multislice computer tomography KW - radiological bone density Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:526-opus4-19628 UR - https://ceur-ws.org/Vol-3777/short6.pdf IS - 3777 SP - 222 EP - 228 ER -