Development Of Medical Technology Research Articles

Automated Lung Cancer Diagnosis Applying Butterworth Filtering, Bi-Level Feature Extraction, and Sparce Convolutional Neural Network to Luna 16 CT Images

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature.

Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.

Early-stage disease detection, particularly in Point-Of-Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision-medicine. Public benefits of early detection extend beyond cost-effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground-breaking innovations enabling automation of operations, conducting advanced large-scale data analysis, generating predictive models, and facilitating remote and guided clinical decision-making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translationfrom research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities.

Research on ultrasound-based radiomics: a bibliometric analysis.

A large number of studies related to ultrasound-based radiomics have been published in recent years; however, a systematic bibliometric analysis of this topic has not yet been conducted. In this study, we attempted to identify the hotspots and frontiers in ultrasound-based radiomics through bibliometrics and to systematically characterize the overall framework and characteristics of studies through mapping and visualization. A literature search was carried out in Web of Science Core Collection (WoSCC) database from January 2016 to December 2023 according to a predetermined search formula. Bibliometric analysis and visualization of the results were performed using CiteSpace, VOSviewer, R, and other platforms. Ultimately, 466 eligible papers were included in the study. Publication trend analysis showed that the annual publication trend of journals in ultrasound-based radiomics could be divided into three phases: there were no more than five documents published in this field in any year before 2018, a small yearly increase in the number of annual publications occurred between 2018 and 2022, and a high, stable number of publications appeared after 2022. In the analysis of publication sources, China was found to be the main contributor, with a much higher number of publications than other countries, and was followed by the United States and Italy. Frontiers in Oncology was the journal with the highest number of papers in this field, publishing 60 articles. Among the academic institutions, Fudan University, Sun Yat-sen University, and the Chinese Academy of Sciences ranked as the top three in terms of the number of documents. In the analysis of authors and cocited authors, the author with the most publications was Yuanyuan Wang, who has published 19 articles in 8 years, while Philippe Lambin was the most cited author, with 233 citations. Visualization of the results from the cocitation analysis of the literature revealed a strong centrality of the subject terms papillary thyroid cancer, biological behavior, potential biomarkers, and comparative assessment, which may be the main focal points of research in this subject. Based on the findings of the keyword analysis and cluster analysis, the keywords can be categorized into two major groups: (I) technological innovations that enable the construction of radiomics models such as machine learning and deep learning and (II) applications of predictive models to support clinical decision-making in certain diseases, such as papillary thyroid cancer, hepatocellular carcinoma (HCC), and breast cancer. Ultrasound-based radiomics has received widespread attention in the medical field and has been gradually been applied in clinical research. Radiomics, a relatively late development in medical technology, has made substantial contributions to the diagnosis, prediction, and prognostic evaluation of diseases. Additionally, the coupling of artificial intelligence techniques with ultrasound imaging has yielded a number of promising tools that facilitate clinical decision-making and enable the practice of precision medicine. Finally, the development of ultrasound-based radiomics requires multidisciplinary cooperation and joint efforts from the field biomedicine, information technology, statistics, and clinical medicine.

Applications of Medical Big Data and Personal Information Protection

In the current social environment, the generation and application of medical big data is closely related to the personal information security of every citizen. With the continuous development of medical technology, the application of medical big data has become increasingly widespread. On one hand, it can promote the development of medical technology, including drug development, personalized medicine, and precision treatment. On the other hand, medical big data also concerns the private information of citizens, which is a crucial part of personal information protection. However, in practice, there often exists a contradiction between medical big data and the protection of citizens' personal information rights. From the perspective of medical big data, there are overlaps and differences among citizens' personal information, privacy information, medical health information, and medical big data information. Relying solely on the “informed consent” rule for personal information cannot effectively protect personal information comprehensively or guarantee the application and development of medical big data. This article analyzes the issues surrounding Chinese medical big data in terms of personal information from a legal perspective, and proposes corresponding solutions to these issues. It is hoped that this research can contribute to advancing the legalization of medical data openness, re-examining patients' personal information in the context of medical big data, and balancing the conflicts between individual privacy interests, public interests, and economic benefits of medical data, thus better protecting individuals' personal information interests under social and private benefits.

Efficacy of laparoscopic low anterior resection for colorectal cancer patients with 3D-vascular reconstruction for left coronary artery preservation.

Laparoscopic low anterior resection (LLAR) has become a mainstream surgical method for the treatment of colorectal cancer, which has shown many advantages in the aspects of surgical trauma and postoperative rehabilitation. However, the effect of surgery on patients' left coronary artery and its vascular reconstruction have not been deeply discussed. With the development of medical imaging technology, 3D vascular reconstruction has become an effective means to evaluate the curative effect of surgery. To investigate the clinical value of preoperative 3D vascular reconstruction in LLAR of rectal cancer with the left colic artery (LCA) preserved. A retrospective cohort study was performed to analyze the clinical data of 146 patients who underwent LLAR for rectal cancer with LCA preservation from January to December 2023 in our hospital. All patients underwent LLAR of rectal cancer with the LCA preserved, and the intraoperative and postoperative data were complete. The patients were divided into a reconstruction group (72 patients) and a nonreconstruction group (74 patients) according to whether 3D vascular reconstruction was performed before surgery. The clinical features, operation conditions, complications, pathological results and postoperative recovery of the two groups were collected and compared. A total of 146 patients with rectal cancer were included in the study, including 72 patients in the reconstruction group and 74 patients in the nonreconstruction group. There were 47 males and 25 females in the reconstruction group, aged (59.75 ± 6.2) years, with a body mass index (BMI) (24.1 ± 2.2) kg/m2, and 51 males and 23 females in the nonreconstruction group, aged (58.77 ± 6.1) years, with a BMI (23.6 ± 2.7) kg/m2. There was no significant difference in the baseline data between the two groups (P > 0.05). In the submesenteric artery reconstruction group, 35 patients were type I, 25 patients were type II, 11 patients were type III, and 1 patient was type IV. There were 37 type I patients, 24 type II patients, 12 type III patients, and 1 type IV patient in the nonreconstruction group. There was no significant difference in arterial typing between the two groups (P > 0.05). The operation time of the reconstruction group was 162.2 ± 10.8 min, and that of the nonreconstruction group was 197.9 ± 19.1 min. Compared with that of the reconstruction group, the operation time of the two groups was shorter, and the difference was statistically significant (t = 13.840, P < 0.05). The amount of intraoperative blood loss was 30.4 ± 20.0 mL in the reconstruction group and 61.2 ± 26.4 mL in the nonreconstruction group. The amount of blood loss in the reconstruction group was less than that in the control group, and the difference was statistically significant (t = -7.930, P < 0.05). The rates of anastomotic leakage (1.4% vs 1.4%, P = 0.984), anastomotic hemorrhage (2.8% vs 4.1%, P = 0.672), and postoperative hospital stay (6.8 ± 0.7 d vs 7.0 ± 0.7 d, P = 0.141) were not significantly different between the two groups. Preoperative 3D vascular reconstruction technology can shorten the operation time and reduce the amount of intraoperative blood loss. Preoperative 3D vascular reconstruction is recommended to provide an intraoperative reference for laparoscopic low anterior resection with LCA preservation.

Uterine disorders in reproductive life

The development of new medical diagnostic technologies and procedures and the drop in the birth rate observed in the last decades represent the main causes of increased incidence of uterine disorders. Endometriosis, adenomyosis and uterine fibroids are benign uterine disorders whose understanding have greatly increased and clinical management have improved in recent times. Molecular pathogenetic aspects, new imaging technologies (transvaginal ultrasound and magnetic resonance), biochemical markers, hormonal drugs, minimally invasive surgical technologies were studied for diagnosis and treatment of endometriosis, adenomyosis and uterine fibroids. Sex steroid hormones, inflammation and fibrosis are key pathogenetic mechanisms of uterine disorders. Endometriosis is characterized by endometrial cells migrating outside the uterus and implanting in the pelvis, associated with inflammation, neuroangiogenesis and fibrosis causing dysmenorrhea, pelvic pain, dysuria and dyschezia. The painful symptoms may activate central sensitization and stress responses. Adenomyosis is caused by the presence of endometrial cells within the myometrium, characterized by pelvic pain, abundant menstrual bleeding and infertility. Heavy menstrual bleeding can cause the reduction of iron reserves and consequently iron deficiency anemia. Uterine fibroids are the most common benign tumors of the uterus in women of reproductive age. They can be asymptomatic, but they frequently manifest with abnormal uterine bleeding, pelvic discomfort, and challenges with fertility. Submucosal or intramural fibroids can hinder embryo implantation and potentially lead to pregnancy-related complications such as miscarriage, placenta previa, preterm labor, or postpartum hemorrhage. The increased incidence of uterine disorders has a major clinical impact on women’s health and a very poor quality of life is often reported. Therefore, prompt diagnosis and effective management are mandatory.

Современные проблемы и возможности в лечении генито­уринарного менопаузального синдрома на этапе развития медицинской науки и технологии

Современные проблемы и возможности в лечении генито­уринарного менопаузального синдрома на этапе развития медицинской науки и технологии

Человеческий эмбрион как потенциальный субъект права

Nowadays, the search for the place of the human embryo in the dyad “subject and object” of law seems to be an urgent challenge for legal science due to the rapid development of medical technologies that make it possible to question the only previously known purpose of the human embryo – to be born as a child. By examining the understanding of legal subjects as developed in various legal paradigms, the author of this study seeks to delineate the place of the human embryo within the subject-object duality of law, justifying the legal status of the human embryo as a potential subject of law from the perspective of a communicative conception of law shared by him. The study employs formal-legal methods, as well as analytical, synthesis, comparative, and thought experiment methods, along with elements of an interdisciplinary approach (utilizing findings from medical research on the human embryo to support legal conclusions, thereby contributing to the scientific novelty of the study). In conclusion, the recognition of the human embryo as a potential subject of law necessitates a nuanced understanding of legal subjectivity and the evolving nature of human development. By engaging with interdisciplinary perspectives and communicative theories of law, we can pave the way for a more inclusive and ethically informed approach to legal discourse surrounding embryonic rights and protections.

Integrating blockchain and ZK-ROLLUP for efficient healthcare data privacy protection system via IPFS

With the rapid development of modern medical technology and the dramatic increase in the amount of medical data, traditional centralized medical information management is facing many challenges. In recent years blockchain, which is a peer-to-peer distributed database, has been increasingly accepted and adopted by different industries and use cases. Key areas of healthcare blockchain applications include electronic medical record (EMR) management, medical device supply chain management, remote condition monitoring, insurance claims and personal health data (PHD) management, among others. Even so, there are a number of challenges in applying blockchain concepts to healthcare and its data, including interoperability, data security privacy, scalability, TPS and so on. While these challenges may hinder the development of blockchain in healthcare scenarios, they can be improved with existing technologies In this paper, we propose a blockchain-based healthcare operations management framework that is combined with the Interplanetary File System (IPFS) for managing EMRs, protects data privacy through a distributed approach while ensuring that this medical ledger is tamper-proof. Doctors act as full nodes, patients can participate in network maintenance either as light nodes or as full nodes, and the hospital acts as the endpoint database of data, i.e., the IPFS node, which saves the arithmetic power of nodes and allows the data stored in the hospitals and departments to be shared with the other organizations that have uploaded the data. Therefore, the integration of blockchain and zero-knowledge proof proposed in this paper helps to protect data privacy and is efficient, better scalable, and more throughput.

Autoprobiotics in the Treatment of Patients with Colorectal Cancer in the Early Postoperative Period.

Despite great advances in the treatment of oncological diseases, the development of medical technologies to prevent or reduce complications of therapy, in particular, those associated with surgery and the introduction of antibiotics, remains relevant. The aim of this study is to evaluate the effectiveness of the use of autoprobiotics based on indigenous non-pathogenic strains of Enterococcus faecium and Enterococcus hirae as a personalized functional food product (PFFP) in the complex therapy of colorectal cancer (CRC) in the early postoperative period. A total of 36 patients diagnosed with CRC were enrolled in the study. Study group A comprised 24 CRC patients who received autoprobiotic therapy in the early postoperative period, while the control group C included 12 CRC patients without autoprobiotic therapy. Prior to surgery and between days 14 and 16 post-surgery, comprehensive evaluations were conducted on all patients, encompassing the following: stool and gastroenterological complaints analysis, examination of the gut microbiota (bacteriological study, quantitative polymerase chain reaction, metagenome analysis), and analysis of interleukins in the serum. Results: The use of autoprobiotics led to a decrease in dyspeptic complaints after surgery. It was also associated with the absence of postoperative complications, did not cause any side effects, and led to a decrease in the level of pro-inflammatory cytokines (IL-6 and IL-18) in the blood serum. The use of autoprobiotics led to positive changes in the structure of escherichia and enterococci populations, the elimination of Parvomonas micra and Fusobacterium nucleatum, and a decrease in the quantitative content of Clostridium perfringens and Akkermansia muciniphila. Metagenomic analysis (16S rRNA) revealed an increase in alpha diversity. Conclusion: The introduction of autoprobiotics in the postoperative period is a highly effective and safe approach in the complex treatment of CRC. Future studies will allow the discovery of additional fine mechanisms of autoprobiotic therapy and its impact on the digestive, immune, endocrine, and neural systems.

Evaluating the integration of traditional gong methods in smart home environment for the recovery of health functions of stroke patients

Since the 21st century, network and mobile communication technology are gradually entering the medical and health services field. Combining body area networks, broad-generation mobile communications, and cloud platforms has made various medical applications for large-scale populations possible. The development of digital medical technology, especially digital telemedicine, is increasingly proving to be an important means of significantly reducing the cost of medical care and access, changing the distribution of medical resources, and improving the overall level of care. To observe the effects of traditional Chinese medicine gongfu combined with rehabilitation therapy on mild depression, anxiety, and functional recovery of activities of daily living (ADL) in patients recovering from stroke, and to provide new treatment methods to improve the function and daily living ability of the group who develop mild depression and anxiety after stroke. In this paper, the digital medical engineering application combining information technology and medical treatment integrates various high-end information technologies such as body domain network and cloud computing to solve the difficulties in the current application one by one, to provide the national people with the system provides “timely”, “local” and “bottomless” remote digital health services to the Chinese people.

Reading practices as a component of preparing future teachers for personalized education of children in a hospital school

Studies show that in recent years the number of children undergoing long-term treatment has been increasing. This is due not only to the spread of cancer and other aggressive diseases among children, but also to the rapid pace of development of medical technologies, which allows more and more children to have favorable life prospects. The article substantiates the need for targeted training of specialists who are capable of psychological and pedagogical work with children in various institutional forms, in particular, in a medical hospital. The description of the university “Reading Club” model is given. The university “Reading Club” is considered a pedagogical tool that promotes the development of students’ reading competence as part of professional culture necessary to ensure the personalization of the educational process in hospital schools. The stages of work of the “Reading Club”, the structure of building the activities of its participants with the possibility of using reading practices with children in long-term treatment are given.

Application of artificial intelligence digital holography technology based on medical sensors in the development of medical image fusion

With the rapid development of artificial intelligence and medical technology, sensor technology, as an important tool to obtain medical data, provides a wealth of physiological parameter information, but the traditional medical image fusion methods are difficult to make full use of the information provided by sensors. Therefore, this paper aims to explore the application of artificial intelligence digital holographic technology based on medical sensor in medical image fusion, and improve the accuracy and effect of medical image diagnosis and treatment. Based on the physiological parameter data obtained by medical sensor, combined with deep learning and image processing algorithm, a medical image fusion method based on artificial intelligence digital holography technology was proposed. The method uses the physiological parameter information provided by the sensor and combines multiple medical image sources to realize holographic medical image presentation. The experimental results show that the artificial intelligence digital holographic technology based on medical sensor can effectively improve the resolution and information content of medical image fusion. Compared with traditional medical image fusion methods, this method has higher image quality and more accurate diagnosis results.

Optimization and Performance Evaluation of Deep Learning Algorithm in Medical Image Processing

In this paper, the optimization and performance evaluation of deep learning algorithm in medical image processing are studied. Firstly, the paper introduces the importance and challenges of medical image processing, and expounds the application prospect of deep learning in this field. Subsequently, this paper discusses the optimization methods of deep learning algorithm in detail, including model structure design, data preprocessing, super parameter adjustment and so on. In terms of performance evaluation, this study selected classic models such as U-Net, DeepLab and DenseNet, and compared them with ROC curve and AUC value to evaluate their predictive ability in medical image classification. The results show that the DenseNet model shows high performance in prediction accuracy, while the performance of U-Net and DeepLab models is slightly average. Finally, the advantages and disadvantages of each model are analyzed, and the future research direction is prospected. This study is of great significance to promote the development and application of medical image processing technology, and provides important theoretical and technical support for medical diagnosis and treatment.

ANALYSIS OF THE INTEGRAL EFFICIENCY OF MEDICAL AID FOR NEWBORN IN UKRAINE

Introduction. Preservation of life and quality of health of newborns is an urgent problem for formation of the future of society and mankind, defense capability, labor reserve and intellectual potential of the country. The fundamental basis for the formation and maintenance of health of newborns is the existence of eff ective health care systems. Objective information about the eff ectiveness of medical care of newborns and its dynamics is the basis for evaluation of achievement of planned results and development of necessary medical organizational technologies. The aim of the study is to analyze and evaluate the integrated eff ectiveness of medical care for newborns in Ukraine at the national and regional levels.Material and Methods. In order to carry out the research, statistical reporting materials on the activities of health care institutions providing medical care to mothers and newborns in all periods of their development for 2018, 2022 were used. The analysis of the integral eff ectiveness of medical care for newborns was carried out using a modifi ed model of the fi nal results of health care institutions. I with the calculations of coeffi cients of comprehensive assessment (CCA) and coeffi cients of achievement of normative values (CANV) of individual indicators with their gradation from high (0,8-1) to low (0,1-0,49) levels. The methods of system approach, bibliographic, statistical data processing and graphical representation are applied.Results. A comprehensive study of the integrated eff ectiveness of medical care for newborns at the national and regional levels conducted at the population level showed a suffi cient, above- average level of achievement of results CCA 0.75 in 2018 and 0.76 in 2022) without clear geological patterns in 2018 and signifi cant diff erences in 2022. – From a high level of CCA (0.82-0.94) in Volyn, Zakarpattia, Ivano- Frankivsk, Lviv, Mykolaiv, Ternopil, Kharkiv and Khmelnytsky regions to a low level of CCA (0.41 and 0.35) in Donetsk and Kherson regions. The study of the main indicative indicators of the eff ectiveness of medical care for newborns in 2022 proved a suffi cient achievement. The normative values of coverage of newborns by screening programs, BCG vaccination, survival and mortality rates of newborns, neonatal transfer index (CANV =0.96, 0.82, 0.94, 0.82 and 0.76, respectively). The most problematic indicators of medical care for newborns with low and below- average levels of the coeffi cient of achievement of results, which are signifi cantly associated with the deterioration of the health of pregnant women and the course of childbirth, are the frequency of low weight, sick newborns (CANV = 0.48, 0.51) and the frequency of transmission of HIV infection from mother to child (CANV = 0.4), which is associated with the fullapplication of an eff ective 4-component strategy for prevention of HIV transmission from mother to child.Conclusions. A toolkit for integral assessment of the eff ectiveness of medical care for newborns as a measure of viability and health quality of the off spring at all stages of ontogenesis has been developed. makes it possible to ensure on a unifi ed basis objective assessment and monitoring of it as a whole, by region and by individual indicators with the subsequent development of measures to infl uence the identifi ed problems and their implementation in the activities of maternal and child health care. The model of integral eff ectiveness of medical care of newborns is a pilot project and has dynamic properties, therefore, in the future, when the normative values of indicators of comprehensive assessment are reached, their revision is planned.

ScRNA‐seq determines the characteristics of T cell marker genes to predict the prognosis of esophageal cancer

AbstractEsophageal cancer (EC) mainly includes two histological subtypes, esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma, which is of high morbidity and mortality. With the continuous development of medical technology, the treatment of EC has been greatly improved, but its prognosis is still unfavorable. Recent single‐cell RNA‐sequencing (scRNA‐seq) is expected to bring breakthroughs in the treatment of EC. First, we identified T cell marker genes and generated signature by analyzing scRNA‐seq data from Expression Omnibus (GEO) database and TCGA database. Then, an immune prognostic model was constructed using the least absolute shrinkage and selection operator. We found that the survival rate of ESCC varied significantly among the low‐ and high‐risk groups. Two genes from T cell signature, DCPS and CYB5R3, were expressed in ESCC cells. Collectively, our study proposed a novel prognostic signature for ESCC patients based on T cell marker genes.

Discussion on Medical Device Adverse Event Surveillance

With the continuous development of medical technology and the wide application of medical devices, the risk of medical device adverse events is gradually increasing. In order to ensure the safety and effectiveness of medical devices, timely detection and treatment of adverse events of medical devices, and to protect the safety and rights of patients, the state continuously strengthens the monitoring of adverse events of medical devices. This paper firstly introduces the domestic and foreign medical device adverse event related regulations, it introduces the form of medical device adverse event monitoring work in China, compares and analyzes the data of medical device adverse events in recent years, and puts forward some problems in medical device adverse event monitoring work, in order to continuously improve the control and management of the risk level of medical devices, and ensure the clinical use of medical devices and patient safety.

A Survey on Transforming Healthcare with IoMT : The Power of Connected Medical Devices

This thorough analysis examines current developments in medical technology, with an emphasis on the Internet of Medical Things (IoMT) and its uses. It covers IoMT's contribution in creating wearable internet monitors for dispersed monitoring systems, addressing pregnancy-related issues, and is divided into four subject groups. Along with blockchain adaption, it also highlights privacy and dependability in IoMT-based health monitoring systems. The paper also emphasizes the contributions of IoMT to healthcare diagnosis and monitoring, including machine learning and edge-AI-based cardiovascular health monitoring and fall prediction. It also covers data science and healthcare informatics techniques, such as using lightweight cryptography to ensure real-time security in remote patient monitoring and identifying unusual user behavior. IoMT has the potential to transform patient care, diagnostics, and data-driven decision-making across a range of healthcare areas, as the paper highlights overall.

ПРОЄКТУВАННЯ АРХІТЕКТУРИ ПРОГРАМНОЇ СИСТЕМИ ДЛЯ СТВОРЕННЯ ЦИФРОВИХ ДВІЙНИКІВ МЕДИКО-БІОЛОГІЧНИХ ОБ'ЄКТІВ

The paper introduces an innovative and universal object-oriented architecture specifically tailored for the creation of digital twins pertaining to medico-biological en-tities, with otolaryngology serving as the primary exemplar. The very essence of this architecture is its profound utilization of polymorphism. By capitalizing on this tech-nique, the architecture offers a substantial reduction in the workload and complexity software developers might face when there's a necessity to adapt or modify the sys-tem for alternative medical specializations. The genesis of this architecture stems from a recognized need to establish a more modular and adaptable framework that can cater to the rapidly evolving land-scape of medical technology and digital twinning. As the medical realm continues to burgeon, having such a flexible and scalable software blueprint becomes paramount, especially when aiming to maintain relevance and efficacy in diverse medical sectors. The paper offers a comprehensive breakdown of the various component groups that constitute the system. Each group is dissected to provide clarity on its function, importance, and integration with the broader system. This hierarchical and systematic representation ensures that software developers, medical researchers, and other stakeholders have a clear roadmap when engaging with the architecture. In summation, this research stands as a pivotal contribution to the intersection of medical technology and software development. By providing a robust and adapt-able architectural blueprint for digital twinning in the medico-biological domain, it paves the way for future innovations and ensures that the medical community can harness the power of digital replication with increased accuracy, efficiency, and ver-satility.

Risk of cardiovascular death in patients with hepatocellular carcinoma based on the Fine-Gray model.

Hepatocellular carcinoma (HCC) is one of the most common types of cancers worldwide, ranking fifth among men and seventh among women, resulting in more than 7 million deaths annually. With the development of medical technology, the 5-year survival rate of HCC patients can be increased to 70%. However, HCC patients are often at increased risk of cardiovascular disease (CVD) death due to exposure to potentially cardiotoxic treatments compared with non-HCC patients. Moreover, CVD and cancer have become major disease burdens worldwide. Thus, further research is needed to lessen the risk of CVD death in HCC patient survivors. To determine the independent risk factors for CVD death in HCC patients and predict cardiovascular mortality (CVM) in HCC patients. This study was conducted on the basis of the Surveillance, Epidemiology, and End Results database and included HCC patients with a diagnosis period from 2010 to 2015. The independent risk factors were identified using the Fine-Gray model. A nomograph was constructed to predict the CVM in HCC patients. The nomograph performance was measured using Harrell's concordance index (C-index), calibration curve, receiver operating characteristic (ROC) curve, and area under the ROC curve (AUC) value. Moreover, the net benefit was estimated via decision curve analysis (DCA). The study included 21545 HCC patients, of whom 619 died of CVD. Age (< 60) [1.981 (1.573-2.496), P < 0.001], marital status (married) [unmarried: 1.370 (1.076-1.745), P = 0.011], alpha fetoprotein (normal) [0.778 (0.640-0.946), P = 0.012], tumor size (≤ 2 cm) [(2, 5] cm: 1.420 (1.060-1.903), P = 0.019; > 5 cm: 2.090 (1.543-2.830), P < 0.001], surgery (no) [0.376 (0.297-0.476), P < 0.001], and chemotherapy(none/unknown) [0.578 (0.472-0.709), P < 0.001] were independent risk factors for CVD death in HCC patients. The discrimination and calibration of the nomograph were better. The C-index values for the training and validation sets were 0.736 and 0.665, respectively. The AUC values of the ROC curves at 2, 4, and 6 years were 0.702, 0.725, 0.740 in the training set and 0.697, 0.710, 0.744 in the validation set, respectively. The calibration curves showed that the predicted probabilities of the CVM prediction model in the training set vs the validation set were largely consistent with the actual probabilities. DCA demonstrated that the prediction model has a high net benefit. Risk factors for CVD death in HCC patients were investigated for the first time. The nomograph served as an important reference tool for relevant clinical management decisions.