Computerized Support System Research Articles

SOFTWARE-DEFINED SDR RADIO SYSTEM

The article presents software, which is one of the types of computer system support, along with technical (hardware), mathematical, informational, linguistic, organizational, methodological and legal support.

The Application of a Surgery Supporting Computer System Does Not Seem to Influence the Surgeons' Radiation Exposure: A Retrospective Analysis.

Since 2019, a surgery supporting system (SPM, surgical process manager; Johnson & Johnson, New Brunswick, New Jersey, Vereinigte Staaten) has been used in a Level I trauma center for common trauma surgery procedures (distal radius fractures, proximal femur fractures, ankle fractures, proximal humerus fractures, and spine fractures). The hypothesis of this study was that implementing standardized procedures (by using SPM) may reduce radiation exposure, especially for unexperienced surgeons.Workflows were developed for different surgical procedures (distal radius fractures, proximal femur fractures, ankle fractures, proximal humerus fractures, and spine fractures) and added into the SPM. Between October 2019 and June 2022, 90 surgeries using the SPM were included in the study. A control group was included with 107 surgeries using the same surgical technique. The values measured were the radiation exposure during the surgical procedure, the experience of the head surgeon, and whether or not the SPM was used. A statistical analysis was performed by using the chi square and Fischer exact tests, with significance set at a p value <0.05.SPM was applicated in 51 cases for the distal radius (control group 54 patients), 20 cases for distal fibula fractures (control group 21 patients), 9 cases for the proximal femur (control group 19 patients), 5 cases for vertebral fractures (control group 7 patients), and 5 cases for the proximal humerus (control group 6 patients). No difference concerning the median radiation exposure was observed by plating distal radius fractures with 5.7Gy/cm² in the SPM group and a median radiation exposure of 6.4Gy/cm² in the control group (p=0.96). The distal fibula fractures showed no significant difference in the intraoperative radiation (17.4Gy/cm² vs. 6.4Gy/cm², p value 0.53). Radiation exposure was lower when a consultant performed surgery without showing any significance, independent if SPM was used or not.In this study, no significant difference in the intraoperative radiation dose was observed when using a surgery supporting computer system. The experience of the surgeon showed no influence as well, regardless if SPM was used or not. Additional data should be collected questioning these findings.

Detection of Breast Cancer in Digital Mammograms by Using Artificial Intelligence

Abstract Background Daily reading of a large number of normal mammograms ni breast cancer screening programs is a tedious task reducing the attractiveness of the field. The advent of Artificial Intelligence (AI) in medical imaging could, however, provide means to improve the efficiency of mammography screening. Objective Our study aims to emphasize the role of AI system as an aid in detection of breast cancer in digital mammograms. Patients and Methods The main source of data for this study were the mammography studies on the picture archiving and communications system (PACS) of theradiology department, i n Shams university hospitals of the patients referred to the radiology department at Ain Shams university hospitals for screening mammography. Results Our study showed that the improvement in diagnosticperformance and reading times with AI support was observed for the radiologists who were least experienced with mammography while the Al is increased the reading times and had no effect on diagnostic performance on the more experienced radiologists. Conclusion Using the AI as an aid has proven to improve the overall performance of less experienced radiologists allowing them to reach expert like levels of diagnostic performance in the detection of breast cancer at mammography by using an AI computer system for support without the need for additional reading time.

New possibilities of artificial intelligence in medicine: a narrative review

The purpose of the narrative review is to provide a descriptive analysis of the emerging capabilities of artificial intelligence (AI) to improve the diagnosis, prevention and treatment of various diseases. The article discusses which modern AI tools can be used in clinical practice, healthcare organization and medical education. The paper considers various aspects of medical AI systems, which are mainly computer support systems for medical decision-making in the process of clinical work. Much attention is paid to the possibilities of generative AI in medicine. Potential applications of AI in clinical practice have been investigated, highlighting promising prospects for both practitioners and their patients. The limitations associated with the use of AI in various fields of medicine are described, and possible ways of solving them are suggested. The problems of information security and ethical constraints associated with the introduction of AI are outlined. The broad integration of AI into public health will enhance clinical and management decision support, speed up disease diagnosis, and improve the overall quality and accessibility of healthcare services.

A computerized support system for the process of certification of colleagues

A computerized support system for the process of certification of colleagues

First experiences with a surgery supporting computer system in regard to education, efficiency and complications

PurposeSince October 2019 a computer software named SPM (surgical process manager) is used in a Level I Trauma center. Workflows were developed for distinct surgical procedures (distal radius fractures, proximal femur fractures, ankle fractures, proximal humerus fractures and vertebral fractures). In addition, these workflows were separated in a shortened „Expert“-versions for consultants and a more detailed „Learner“- versions for residents.This investigation was intended to show, if and what kind of benefits in regard to efficiency (incision to suture and suture to incision time), education and complications a surgery supporting software can bring. MethodsSPM was used in 90 cases during October 2019 to June 2022. A control trial with 108 patients was developed, including patients of the same age, having same kind of injuries, receiving the same surgery technique without using the SPM.The software was installed on the computer in the operation room, projected on head monitors and operated by a foot pedal. Complications could also be documented using the pedal.Groups were divided in surgical procedures and fracture type, qualification of the surgeon, complications and surgery time. Surgery times were taken from the hospital computer system (SAP IS-H). A statistical analysis was performed by using the chi square and Fischer exact test with significance set at a P value <0.05. ResultsIn 51 cases the software was used for the distal radius (control group 54 patients), in 20 cases for Weber fractures (control group 21 patients), in 9 cases for the proximal femur (control group 19 patients), in 5 cases for vertebral fractures (control group 7 patients) and in 5 cases for the proximal humerus (control group 6 patients).Time from incision to closure was significant higher in the intervention group (49 vs 42 min, p- value 0,018) and wasn't significant lower in the “expert” group, fixing radius and ancle fractures (39 to 46 min, p value 0,186).Comparing the SPM and control group concerning closing to incision time, no difference could be observed (56 to 58,5 min, p value 0,828).The greatest time deviation between „Learners“und „Experts“was observed in reduction and fixation (p value 0,006) in ankle fractures. The “Expert“group also needed less time for the approach (p value 0,008) in case of distal radius fractures.Unexpected events were more often observed in the intervention group (5,5 vs 3,7 %). ConclusionsA surgery supporting computer system might be a good tool for detecting and optimizing workflows in the operation room and for improving and analyzing the training of residents and surgical assistants.In addition, it offers the opportunity to document intraoperative complications. However, a saving of time wasn't observed in this study. Further investigations with bigger number of cases and a longer follow-up are necessary to proof these findings statistically.

Automated Stabilization, Enhancement and Capillaries Segmentation in Videocapillaroscopy.

Oral capillaroscopy is a critical and non-invasive technique used to evaluate microcirculation. Its ability to observe small vessels in vivo has generated significant interest in the field. Capillaroscopy serves as an essential tool for diagnosing and prognosing various pathologies, with anatomic-pathological lesions playing a crucial role in their progression. Despite its importance, the utilization of videocapillaroscopy in the oral cavity encounters limitations due to the acquisition setup, encompassing spatial and temporal resolutions of the video camera, objective magnification, and physical probe dimensions. Moreover, the operator's influence during the acquisition process, particularly how the probe is maneuvered, further affects its effectiveness. This study aims to address these challenges and improve data reliability by developing a computerized support system for microcirculation analysis. The designed system performs stabilization, enhancement and automatic segmentation of capillaries in oral mucosal video sequences. The stabilization phase was performed by means of a method based on the coupling of seed points in a classification process. The enhancement process implemented was based on the temporal analysis of the capillaroscopic frames. Finally, an automatic segmentation phase of the capillaries was implemented with the additional objective of quantitatively assessing the signal improvement achieved through the developed techniques. Specifically, transfer learning of the renowned U-net deep network was implemented for this purpose. The proposed method underwent testing on a database with ground truth obtained from expert manual segmentation. The obtained results demonstrate an achieved Jaccard index of 90.1% and an accuracy of 96.2%, highlighting the effectiveness of the developed techniques in oral capillaroscopy. In conclusion, these promising outcomes encourage the utilization of this method to assist in the diagnosis and monitoring of conditions that impact microcirculation, such as rheumatologic or cardiovascular disorders.

Methodology for Constructing Academic Programs Using Computer Support Systems

Methodology for Constructing Academic Programs Using Computer Support Systems

Research and Practice on online evaluation model of teachers’ ethics and style in Higher Vocational Colleges in the New Era--Take e-commerce major as an example

The construction of online evaluation processing model of teachers’ ethics and style in higher vocational colleges must go deep into the front line of teaching and be scientifically formulated according to the cognitive status of teachers and students and the internal requirements of school development. Based on the questionnaire survey on the problems related to the construction of online evaluation model of teachers’ ethics and style for teachers and students majoring in e-commerce, this paper finds and analyzes the difficulties and puzzles existing in the current school teachers’ ethics and style evaluation model from the first-hand data, and constructs a model composed of positive list and negative list by determining the index content, negative list, parameter score, computer system support, etc Online evaluation and processing model system of teachers’ ethics and style, which is convenient for assessment, evaluation, publicity and education. Among them, the positive list is composed of patriotism and law-abiding, love and dedication, care for students, teaching and educating people, being a teacher, rigorous scholarship and serving the society. The negative list provides the “bottom line” for teachers from the aspects of ideology, politics, discipline, work and life style.

Comparison Of Machine Learning Algorithms In The Detection Of Alzheimer's Disease

Alzheimer's disease is a neurodegenerative disorder that causes loss of cognitive function and cognitive decline in individuals. Detection of the disease at an early stage is important to slow down the devastating effects of the disease. The use of an autonomous computerized support system that can assist specialist physicians in the diagnostic process saves time and helps reduce human error. For this reason, a high-accuracy classification study was aimed at utilizing different machine learning algorithms for early diagnosis of Alzheimer's disease. Within the scope of this study, an open source data set created with Electroencephalogram (EEG) signals from 24 healthy and 24 Alzheimer's patient volunteers was used. 28 features, including spectral and statistical features, were extracted from each channel of the EEG signals. The extracted features were evaluated to the feature importance algorithm and the five most significant features that could distinguish between Alzheimer's individuals and healthy individuals were determined. Four machine learning algorithms are trained with the determined features. 70% of the data was used for training and the algorithms were trained with a 10-fold cross-validation method. When the four machine learning algorithms were tested with the data reserved for testing, which the algorithms had not seen before, the highest accuracy was obtained with the Gradient Boosting Classifier (GBC) algorithm with 96.43%.

Experiences of using clinical pathways in hospitals: Perspectives of quality improvement personnel.

This study aimed to explore the experiences of quality improvement personnel in implementing clinical pathways (CPs) in Korean hospitals. A qualitative study using focus-group interviews was conducted with healthcare professionals in charge of CP development and management in hospitals. Sixteen quality improvement personnel from eight tertiary and seven general hospitals were recruited using purposive sampling. The verbatim transcribed data were analysed using qualitative content analysis. Three key themes emerged: (1) the primary focus of CP development on surgeries through concerted efforts between management and frontline healthcare professionals; (2) CP fidelity management using indicators and feedback to relevant staff or departments; and (3) positive outcomes, despite concerns about system safety. The factors affecting CP use included availability of clinical evidence, flexibility of CPs, top management and clinical leadership, physicians' perceptions of CPs, computerized support systems, and external policies and regulations.

Integration of industrial IoT architectures for dynamic scheduling

Technology advancement in smart sensors, cloud computing, and decision support systems is pressuring the industry to adopt more flexible data-based solutions. One of the areas that can benefit from this progress is dynamic scheduling, which can improve production efficiency by tracking resource availability, job changes, and user commands. Challenges include setting triggers through the simultaneous integration of information from cloud-based enterprise databases and operational sources in the Industrial Internet of Things (IIoT). This paper proposes an integrated Information Technology - Operational Technology solution to support dynamic scheduling and rescheduling operations in a personalized production environment. It presents an implementation to a real-world application: an analytical quality control laboratory in the pharmaceutical industry. The resulting integration of intelligent sensors and business events in a fog computing architecture allows the generation of rescheduling triggers to specific online events. This paper focus on the definition of the online events affecting the operations and the cloud-fog-edge IIoT architecture used to support the implementation, and on the rescheduling triggers. The optimized reschedule of the use case shows that moving the computation closer to the cloud improves the CPU run time for larger instances. However, the combined CPU run time with the data exchange and querying introduces a non-negligible communication delay for smaller instances. In situations where fast scheduling solutions are required, fog computing near the edge is the best approach. On the other hand, for larger-size instances, moving the computation closer to the cloud is the recommended approach.

Design-by-Analogy: Effects of Exploration-Based Approach on Analogical Retrievals and Design Outcomes

Abstract This paper presents VISION (Visual Interaction tool for Seeking Inspiration based on Nonnegative Matrix Factorization), a computational design-by-analogy (DbA) tool that enables designers to visually explore a space of analogical inspiration for creative idea generation. While many currently available DbA tools use a query-based approach for retrieving analogies (i.e., input keywords or functions to return a set of relevant results), VISION allows designers to retrieve a collection of design analogies that are related to topics of interest and explore a space of potential inspiration, the way one would gather books of particular topics from multiple shelves at the library to find potential resources. Two cognitive engineering design studies were conducted to evaluate the efficacy of VISION during the conceptual design process. In the first study, conducted in a controlled-lab setting, VISION was evaluated based on its effect on the quantity, quality, novelty, and direct physical similarity ratings of design outcomes. In the second study, conducted in a graduate engineering design class, VISION was evaluated based on designers’ abilities to retrieve analogies from different domains and analogies that are different from already existing design solutions. Studies show that VISION could provide an alternative to the query-based search that many DbA computational support systems use and open up new opportunities for designers to benefit from computationally supported analogies.

Отбор претендентов в условиях минимизации затрат на адаптацию к профессиональным требованиям

The problem of selection of applicants in conditions of minimizing the costs of their adaptation to professional requirements through special training is considered. The purpose of the study is the formalization of professional selection tasks and the development of mathematical methods for their solution. A set of optimization mathematical models has been developed that allows selection of applicants based on taking into account various professional requirements and the degree of compliance with them by an individual applicant. At the same time, not only the current level of development of professionally important qualities of each applicant is taken into account, but also the potential for its development after passing special training that adapts to the requirements of the profession. The problems of selecting one or more applicants who meet a narrow or extended set of professional requirements, using both a single and various training technologies, are considered. A method for formalizing the task of professional selection based on the construction and analysis of a three-dimensional matrix of costs for the education of applicants is proposed. The results presented can be used as a mathematical basis for various decision support tools in the field of human capital management, in particular, a computer system for information support of personnel management.

Multi-Label Chest X-Ray Classification via Deep Learning

In this era of pandemic, the future of healthcare industry has never been more exciting. Artificial intelligence and machine learning (AI & ML) present opportunities to develop solutions that cater for very specific needs within the industry. Deep learning in healthcare had become incredibly powerful for supporting clinics and in transforming patient care in general. Deep learning is increasingly being applied for the detection of clinically important features in the images beyond what can be perceived by the naked human eye. Chest X-ray images are one of the most common clinical method for diagnosing a number of diseases such as pneumonia, lung cancer and many other abnormalities like lesions and fractures. Proper diagnosis of a disease from X-ray images is often challenging task for even expert radiologists and there is a growing need for computerized support systems due to the large amount of information encoded in X-Ray images. The goal of this paper is to develop a lightweight solution to detect 14 different chest conditions from an X ray image. Given an X-ray image as input, our classifier outputs a label vector indicating which of 14 disease classes does the image fall into. Along with the image features, we are also going to use non-image features available in the data such as X-ray view type, age, gender etc. The original study conducted Stanford ML Group is our base line. Original study focuses on predicting 5 diseases. Our aim is to improve upon previous work, expand prediction to 14 diseases and provide insight for future chest radiography research.

A Inteligência Artificial e a Tomada de Decisão: um Caminho a Percorrer

This essay arises from a work developed in the discipline of Decision Support Computer Systems in the second year of the Technical Higher Professional Course in WEB Programming, Devices and Mobile Applications at the Jean Piaget School of Technology and Management of the Jean Piaget do Sul Polytechnic Institute. It aims to describe in a brief history the concept of Artificial Intelligence and its evolution over time, as well as to elaborate a dashboard using the Microsoft Power BI software for the analysis of the information presented as a Decision Support System. In this way, this essay served as an example of the use of two extremely current and important technologies for decision making that become increasingly faster and more effective. An example of building a dashboard with imported data was used, which was worked on and formatted in order to be used in Power BI. As a conclusion, we emphasize that the tools of Artificial Intelligence and Machine Learning are essential tools in our daily lives, mainly in the use of Big Data. Power BI proved to be versatile and efficient in the treatment and visualization of data. We do not know what the future holds in this matter, but we know that it will always be evolving and contributing more and more to a more technological society that facilitates tasks that are indispensable to human beings.

Quantifying emotionally grounded discursive knowledge with cognitive-affective maps

Belief systems matter for all kinds of human social interaction. People have individual cognitions and feelings concerning processes in their environment, which is why they may evaluate them differently. Belief systems can be visualized with cognitive-affective maps (CAMs; as reported by Thagard (in: McGregor (ed) EMPATHICA: A computer support system with visual representations for cognitive-affective mapping, AAAI Press, CA, 2010)). However, it is unclear whether CAMs can be constructed in an intersubjective way by different researchers attempting to map the beliefs of a third party based on qualitative text data. To scrutinize this question, we combined qualitative strategies and quantitative methods of text and network analysis in a case study examining belief networks about participation. Our data set consists of 10 sets of two empirical CAMs: the first CAM was created based on participants’ freely associating concepts related to participation in education (N = 10), the second one was created based on given text data which the participants represented as a CAM following a standardized instruction manual (N = 10). Both CAM-types were compared along three dimensions of similarity (network similarity, concept association similarity, affective similarity). On all dimensions of similarity, there was substantially higher intersubjective agreement in the text-based CAMs than in the free CAMs, supporting the viability of cognitive affective mapping as an intersubjective research method for studying the emotional coherence of belief systems and discursive knowledge. In addition, this study highlights the potential for identifying group-level differences based on how participants associate concepts.

Identification of Prognostic Factors and Predicting the Therapeutic Effect of Laser Photocoagulation for DME Treatment

Diabetic retinopathy is among the most severe complications of diabetes, most often leading to rapid and irreversible vision loss. The laser coagulation procedure, which consists of applying microburns to the fundus, has proven to be an effective method for treating diabetic retinopathy. Unfortunately, modern research does not pay enough attention to the study of the arrangement of microburns in the edema area—One of the key factors affecting the quality of therapy. The aim of this study was to propose a computational decision-making support system for retina laser photocoagulation based on the analysis of photocoagulation plans. Firstly, we investigated a set of prognostic factors based on 29 features describing the geometric arrangement of coagulates. Secondly, we designed a technology for the intelligent analysis of the photocoagulation plan that allows the effectiveness of the treatment to be predicted. The studies were carried out using a large database of fundus images from 108 patients collected in clinical trials. The results demonstrated a high classification accuracy at a level of over 85% using the proposed prognostic factors. Moreover, the designed technology proved the superiority of the proposed algorithms for the automatic arrangement of coagulates, predicting a 99% chance of a positive therapeutic effect.

Expert system for the diagnosis of hereditary metabolic diseases accompanied by mental disorders in children

Objective. To create a computer support system for pre-laboratory diagnosis of hereditary metabolic diseases with mental pathology. Material and methods. The authors used numerous literary sources to extract data for intelligent system. The initially formed database was supplemented by expert assessments for certain categories of age groups of children. Results. The experts determined the modality, confidence factors for the timing of manifestation and severity of signs for each clinical form at certain age periods. In the course of the study the authors built the models for a comprehensive assessment of signs and an integral assessment of diseases. The diagnostic algorithm is included in the database. It is used to compare the proposed hypotheses. The authors implemented a prototype of an expert diagnostic system based on a mucopolysaccharidosis model, which demonstrated 90% efficiency in a control sample of 20 patients.

Application Research on the Integration of Civil Engineering and Computer-aided Building System Based on the Development of BIM

Building informatization and industrialization are mutually advancing in the development process. After BIM technology is widely used in the construction industry, the modeling functions of BIM include standardized components for building building models, “error and leakage collision” inspection, and simulation simulation construction. These functions are in Building industrialization has great advantages in the construction process of prefabricated buildings, especially the standardized design process in the engineering project design stage, which helps to vigorously promote the standardized construction. There is a certain correlation between the improvement of civil engineering construction quality standards and the background of intelligent development. People are more concerned about the quality of engineering projects, and social publicity has been reached for the safety and green environmental protection of buildings. It is imperative to realize technological innovation in civil engineering. The fusion technology of civil engineering technology and computer-aided building system combines building technology, civil engineering technology, control technology, etc. with computer support systems to develop a new type of intelligent building technology. The integration and application of civil engineering and computer-aided building systems are highly valued by the country, which will lead the development direction of the wood construction industry. For colleges and universities, the research and study of the integration and application of civil engineering and computer-aided building systems not only puts forward new requirements for student learning, but also provides new ideas for the direction of employment, and at the same time has a deeper understanding of the relevant research system of universities change. The article mainly explains the working principle of BIM, based on the BIM development background and the integration advantages of civil engineering and computer-supported building systems for specific applications in construction projects, with the goal of promoting the further development and popularization of this technology.