• Clinical and Experimental Reproductive Medicine
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• v.49 no.4
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• pp.225-238
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• 2022

The ultimate goal of human assisted reproductive technology is to achieve a healthy pregnancy and birth, ideally from the selection and transfer of a single competent embryo. Recently, techniques for efficiently evaluating the state and quality of preimplantation embryos using time-lapse imaging systems have been applied. Artificial intelligence programs based on deep learning technology and big data analysis of time-lapse monitoring system during in vitro culture of preimplantation embryos have also been rapidly developed. In addition, several molecular markers of the secretome have been successfully analyzed in spent embryo culture media, which could easily be obtained during in vitro embryo culture. It is also possible to analyze small amounts of cell-free nucleic acids, mitochondrial nucleic acids, miRNA, and long non-coding RNA derived from embryos using real-time polymerase chain reaction (PCR) or digital PCR, as well as next-generation sequencing. Various efforts are being made to use non-invasive evaluation of embryo quality (NiEEQ) to select the embryo with the best developmental competence. However, each NiEEQ method has some limitations that should be evaluated case by case. Therefore, an integrated analysis strategy fusing several NiEEQ methods should be urgently developed and confirmed by proper clinical trials.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.76-83
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• 2023

Recently, the development of computer vision with deep learning has made object detection using images applicable to diverse fields, such as medical care, manufacturing, and transportation. The manufacturing industry is saving time and money by applying computer vision technology to detect defects or issues that may occur during the manufacturing and inspection process. Annotations of collected images and their location information are required for computer vision technology. However, manually labeling large amounts of images is time-consuming, expensive, and can vary among workers, which may affect annotation quality and cause inaccurate performance. This paper proposes a process that can automatically collect annotations and location information for images using eXplainable AI, without manual annotation. If applied to the manufacturing industry, this process is thought to save the time and cost required for image annotation collection and collect relatively high-quality annotation information.

• Electronics and Telecommunications Trends
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• v.38 no.3
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• pp.57-65
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• 2023

Emergency numbers, such as 112 and 119, are used in many countries to connect people in need with emergency services such as police, fire, and medical assistance. We describe development directions of intelligent response technology for emergency calls. The development of this technology refers to enhancing the efficiency and effectiveness of response systems by using advanced methods such as artificial intelligence, machine learning, and big data analytics. We focus on a system that assists the receptionist of an emergency call. In the future, the recognition rate and decision-making accuracy of intelligent response technologies should be improved considering characteristics of public safety and emergency domain data. Although the current technology remains at the level of assisting a receptionist, a fully autonomous response technology is expected to emerge in the future.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.349-353
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• 2021

This study is a review study attempted to analyze the current situation of surgical robots based on previous research on surgical robots in the era of the 4th revolution, and to forecast the future direction of surgical robots. Surgical robots have made full progress since the launch of the da Vinci and the surgical robot is playing a role of supporting the surgeries of the surgeons or the master-slave method reflecting the intention of the surgeons. Recently, technologies are being developed to combine artificial intelligence and big data with surgical robots, and to commercialize a universal platform rather than a platform dedicated to surgery. Moreover, technologies for automating surgical robots are being developed by generating 3D image data based on diagnostic image data, providing real-time images, and integrating image data into one system. For the development of surgical robots, cooperation with clinicians and engineers, safety management of surgical robot, and institutional support for the use of surgical robots will be required.

• Journal of Korean Neurosurgical Society
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• v.67 no.4
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• pp.385-396
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• 2024

Previously, we reported the concept of a cloud-based telemedicine platform for patients with intracerebral hemorrhage (ICH) at local emergency rooms in rural and medically underserved areas in Gangwon state by combining artificial intelligence and remote consultation with a neurosurgeon. Developing a telemedicine ICH treatment protocol exclusively for doctors with less ICH expertise working in emergency rooms should be part of establishing this system. Difficulties arise in providing appropriate early treatment for ICH in rural and underserved areas before the patient is transferred to a nearby hub hospital with stroke specialists. This has been an unmet medical need for decades. The available reporting ICH guidelines are realistically applicable in university hospitals with a well-equipped infrastructure. However, it is very difficult for doctors inexperienced with ICH treatment to appropriately select and deliver ICH treatment based on the guidelines. To address these issues, we developed an ICH telemedicine protocol. Neurosurgeons from four university hospitals in Gangwon state first wrote the guidelines, and professors with extensive ICH expertise across the country revised them. Guidelines and recommendations for ICH management were described as simply as possible to allow more doctors to use them easily. We hope that our effort in developing the telemedicine protocols will ultimately improve the quality of ICH treatment in local emergency rooms in rural and underserved areas in Gangwon state.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.1
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• pp.221-228
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• 2018

Big data is an important keyword in World's Fourth Industrial Revolution in public and private division including IoT(Internet of Things), AI(Artificial Intelligence) and Cloud system in the fields of science, technology, industry and society. Big data based on services are available in various fields such as transportation, weather, medical care, and marketing. In particular, in the field of sports, various types of bio-signals can be collected and managed by the appearance of a wearable device that can measure vital signs in training or rehabilitation for daily life rather than a hospital or a rehabilitation center. However, research on big data with vital signs from wearable devices for training and rehabilitation for baseball players have not yet been stimulated. Therefore, in this paper, we propose a system for baseball infield and outfield players, especially which can store and analyze the momentum measurement vital signals based on big data.

• Journal of Intelligence and Information Systems
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• v.16 no.2
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• pp.73-94
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• 2010

The attending system is a medical system that allows doctors in clinics to use the extra equipment in hospitals-beds, laboratory, operating room, etc-for their patient's care under a contract between the doctors and hospitals. Therefore, the system is very beneficial in terms of the efficiency of the usage of medical resources. However, it is necessary to develop a strong support system to strengthen its weaknesses and supplement its merits. If doctors use hospital beds under the attending system of hospitals, they would be able to check a patient's condition often and provide them with nursing care services. However, the current attending system lacks delivery and assistance support. Thus, for the successful performance of the attending system, a networking system should be developed to facilitate communication between the doctors and nurses. In particular, the nursing records in the attending system could help doctors monitor the patient's condition and provision of nursing care services. A nursing record is the formal documentation associated with nursing care. It is merely a data repository that helps nurses to track their activities; nursing records thus represent a resource of primary information that can be reused. In order to maximize their usefulness, nursing records have been introduced as part of computerized patient records. However, nursing records are internal data that are not disclosed by hospitals. Moreover, the lack of standardization of the record list makes it difficult to share nursing records. Under the attending system, nurses would want to minimize the amount of effort they have to put in for the maintenance of additional records. Hence, they would try to maintain the current level of nursing records in the form of record lists and record attributes, while doctors would require more detailed and real-time information about their patients in order to monitor their condition. Therefore, this study developed a system for assisting in the maintenance and sharing of the nursing records under the attending system. In contrast to previous research on the functionality of computer-based nursing records, we have emphasized the practical usefulness of nursing records from the viewpoint of the actual implementation of the attending system. We suggested that nurses could design a nursing record dictionary for their convenience, and that doctors and nurses could confirm the definitions that they looked up in the dictionary through negotiations with intelligent agents. Such an agent-based system could facilitate networking among medical institutes. Multi-agent systems are a widely accepted paradigm for the distribution and sharing of computation workloads in the scientific community. Agent-based systems have been developed with differences in functional cooperation, coordination, and negotiation. To increase such communication, a framework for a multi-agent based system is proposed in this study. The agent-based approach is useful for developing a system that promotes trade-offs between transactions involving multiple attributes. A brief summary of our contributions follows. First, we propose an efficient and accurate utility representation and acquisition mechanism based on a preference scale while minimizing user interactions with the agent. Trade-offs between various transaction attributes can also be easily computed. Second, by providing a multi-attribute negotiation framework based on the attribute utility evaluation mechanism, we allow both the doctors in charge and nurses to negotiate over various transaction attributes in the nursing record lists that are defined by the latter. Third, we have designed the architecture of the nursing record management server and a system of agents that provides support to the doctors and nurses with regard to the framework and mechanisms proposed above. A formal protocol has also been developed to create and control the communication required for negotiations. We verified the realization of the system by developing a web-based prototype. The system was implemented using ASP and IIS5.1.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2007.05a
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• pp.389-398
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• 2007

Case-based reasoning (CBR) is one of the most popular prediction techniques for medical diagnosis because it is easy to apply, has no possibility of overfitting, and provides a good explanation for the output. However, it has a critical limitation - its prediction performance is generally lower than other artificial intelligence techniques like artificial neural networks (ANNs). In order to obtain accurate results from CBR, effective retrieval and matching of useful prior cases for the problem is essential, but it is still a controversial issue to design a good matching and retrieval mechanism for CBR systems. In this study, we propose a novel approach to enhance the prediction performance of CBR. Our suggestion is the simultaneous optimization of feature weights, instance selection, and the number of neighbors that combine using genetic algorithms (GAs). Our model improves the prediction performance in three ways - (1) measuring similarity between cases more accurately by considering relative importance of each feature, (2) eliminating redundant or erroneous reference cases, and (3) combining several similar cases represent significant patterns. To validate the usefulness of our model, this study applied it to a real-world case for evaluating cytological features derived directly from a digital scan of breast fine needle aspirate (FNA) slides. Experimental results showed that the prediction accuracy of conventional CBR may be improved significantly by using our model. We also found that our proposed model outperformed all the other optimized models for CBR using GA.

• Journal of the Korea Society of Computer and Information
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• v.26 no.7
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• pp.9-17
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• 2021

In this study, we propose a part of the CDSS(Clinical Decision Support System) study, a system that can classify chemotherapy, one of the treatment methods for colorectal cancer patients. In the treatment of colorectal cancer, the selection of chemotherapy according to the patient's condition is very important because it is directly related to the patient's survival period. Therefore, in this study, chemotherapy was classified using a machine learning algorithm by creating a baseline model, a pathological model, and a combined model using both characteristics of the patient using the individual and pathological characteristics of colorectal cancer patients. As a result of comparing the prediction accuracy with Top-n Accuracy, ROC curve, and AUC, it was found that the combined model showed the best prediction accuracy, and that the LGBM algorithm had the best performance. In this study, a chemotherapy classification model suitable for the patient's condition was constructed by classifying the model by patient characteristics using a machine learning algorithm. Based on the results of this study in future studies, it will be helpful for CDSS research by creating a better performing chemotherapy classification model.

• Journal of Advanced Navigation Technology
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• v.25 no.1
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• pp.96-101
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• 2021

This paper presents a machine learning model that predicts stroke risks in atrial fibrillation patients using public big data. As the training data, 68 independent variables including demographic, medical history, health examination were collected from the Korean National Health Insurance Service. To predict stroke incidence in patients with atrial fibrillation, we applied deep neural network. We firstly verify the performance of conventional statistical models (CHADS2, CHA2DS2-VASc). Then we compared proposed model with the statistical models for various hyperparameters. Accuracy and area under the receiver operating characteristic (AUROC) were mainly used as indicators for performance evaluation. As a result, the model using batch normalization showed the highest performance, which recorded better performance than the statistical model.