• Journal of Korean Neurosurgical Society
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• v.67 no.5
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• pp.493-509
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• 2024

In neurointensive care units (NICUs), particularly in cases involving traumatic brain injury (TBI), swift and accurate decision-making is critical because of rapidly changing patient conditions and the risk of secondary brain injury. The use of artificial intelligence (AI) in NICU can enhance clinical decision support and provide valuable assistance in these complex scenarios. This article aims to provide a comprehensive review of the current status and future prospects of AI utilization in the NICU, along with the challenges that must be overcome to realize this. Presently, the primary application of AI in NICU is outcome prediction through the analysis of preadmission and high-resolution data during admission. Recent applications include augmented neuromonitoring via signal quality control and real-time event prediction. In addition, AI can integrate data gathered from various measures and support minimally invasive neuromonitoring to increase patient safety. However, despite the recent surge in AI adoption within the NICU, the majority of AI applications have been limited to simple classification tasks, thus leaving the true potential of AI largely untapped. Emerging AI technologies, such as generalist medical AI and digital twins, harbor immense potential for enhancing advanced neurocritical care through broader AI applications. If challenges such as acquiring high-quality data and ethical issues are overcome, these new AI technologies can be clinically utilized in the actual NICU environment. Emphasizing the need for continuous research and development to maximize the potential of AI in the NICU, we anticipate that this will further enhance the efficiency and accuracy of TBI treatment within the NICU.

• BioChip Journal
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• v.12 no.4
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• pp.280-286
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• 2018

Hydrogel scaffolds composed of multiple components are promising platform in tissue engineering as a transplantation materials or artificial organs. Here, we present a new fabrication method for implementing multi-layered macroscopic hydrogel scaffold composed of multiple components by controlling height of hydrogel layer through precise control of ultraviolet (UV) energy density. Through the repetition of the photolithography process with energy control, we can form several layers of hydrogel with different height. We characterized UV energy-dependent profiles with single-layered PEGDA posts photocrosslinked by the modular methodology and examined the optical effect on the fabrication of multi-layered, macroscopic hydrogel structure. Finally, we successfully demonstrated the potential applicability of our approach by fabricating various macroscopic hydrogel constructs composed of multiple hydrogel layers.

• Journal of Biomedical Engineering Research
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• v.27 no.6
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• pp.418-426
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• 2006

In the artificial heart application, productivity and hemodynamic properties of artificial heart valves are crucial in successiful application to long term in vivo trials. This paper is about manufacture and assessment of trileaflet polymer heart valves using vacuum forming process(VFP). The VFP has many advantages such as reduced fabrication time, reproducibility due to relatively easy and simple process for manufacturing. Prior to VFP of trileaflet polymer heart valves, polyurethane(Pellethane 2363 80AE, Dow Chemical) sheet was prepared by extrusion. The sheets were heated and formed to mold shape by vacuum pressure. The vacuum formed trileaflet polymer heart valves fabrication is composed of two step method, first, leaflet forming and second, conduit forming. This two-step forming process made the leaflet-conduit bonding stable with any organic solvents. Hydrodynamic properties and hemocompatibility of the vacuum formed trileaflet polymer heart valves was compared with sorin bicarbon bileaflet heart valve. The percent effective orifice area of vacuum formed trileaflet polymer heart valves was inferior to bileaflet heart valve, but the increase of plasma free hemoglobin level which reflect blood damage was superior in vacuum formed trileaflet polymer heart valves Vacuum formed trileaflet polymer heart valves has high productivity, and superior hemodynamic property than bileaflet heart valves. Low manufacturing cost and blood compatible trileaflet polymer heart valves shows the advantages of vacuum forming process, and these results give feasibility in in vivo animal trials in near future, and the clinical artificial heart development program.

• Korean Journal of Applied Biomechanics
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• v.34 no.2
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• pp.71-80
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• 2024

Objective: This study aimed to explore the brain connectivity between brain and biomechanical variables by exploring motion recognition through FFT (fast fourier transform) analysis and AI (artificial intelligence) focusing on quiet standing movement patterns. Method: Participants included 12 young adult males, comprising university students (n=6) and elite gymnasts (n=6). The first experiment involved FFT of biomechanical signals (f_CoP, f_AJtorque and f_EEG), and the second experiment explored the optimization of AI-based GRU (gated recurrent unit) using f_EEG data. Results: Significant differences (p<.05) were observed in frequency bands and maximum power based on group and posture types in the first experiment. The second study improved motion prediction accuracy through GRU performance metrics derived from brain signals. Conclusion: This study delved into the movement pattern of upright standing posture through the analysis of bio-signals linking the cerebral cortex to motor performance, culminating in the attainment of motion recognition prediction performance.

• Clinical and Experimental Pediatrics
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• v.52 no.8
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• pp.856-861
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• 2009

Cardiopulmonary arrest has long been accepted as an unquestionable definition of death. An advent of cardiopulmonary resuscitation and artificial ventilation along with the development of organ transplantation has prompted the emergence of the concept of brain death. The criteria for brain death are based mainly on the clinical examination of coma, apnea and total loss of brain stem function. Although organ transplantation by donor brain death has increased in Korea over recent years, there is still a substantial shortage of donor organs compared to the demand. Improvement of government policies and changes of social culture for organ donation are needed for the activation of organ transplantation by donor brain death. Pediatricians have an important role for the search of potential donors in cases of brain death and optimal medical care for successful organ transplantation.

• ETRI Journal
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• v.46 no.2
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• pp.263-276
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• 2024

Different diseases occur in the brain. For instance, hereditary and progressive diseases affect and degenerate the white matter. Although addressing, diagnosing, and treating complex abnormalities in the brain is challenging, different strategies have been presented with significant advances in medical research. With state-of-art developments in artificial intelligence, new techniques are being applied to brain magnetic resonance images. Deep learning has been recently used for the segmentation and classification of brain images. In this study, we classified normal and pathological brain images using pretrained deep models through transfer learning. The EfficientNet-B5 model reached the highest accuracy of 98.39% on real data, 91.96% on augmented data, and 100% on pathological data. To verify the reliability of the model, fivefold cross-validation and a two-tier cross-test were applied. The results suggest that the proposed method performs reasonably on the classification of brain magnetic resonance images.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.24-27
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• 2004

This paper describes the method how to control an autonomous robot remotely using Internet. The autonomous robot that has an artificial brain is called "Tarou". (1) It is able to move along the line on the floor based on processing the image data obtained from two CCD cameras. (2) It is able to understand dialogs between human being and it and is able to take actions such as turn right and lefts, go forward 1m and go backward 0.5m, etc. (3) It is able to recognize patterns of objects. (4) It is able to recognize human faces. (5) It is able to communicate human being and to speak according to contents written in the program. We show the techniques to control the autonomous robot "Tarou" remotely by personal computer and/or portable Phone via Internet. The techniques developed in our research could dramatically increase their performance for..the need of artificial life robot as the next generation robot and national homeland security needs.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.143-146
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• 1990

The human brain has the most powerful capabilities in thinking, interpreting, remembering, and problem-solving. Artificial neural network is appeared by scientists who have tried to simulate such a human brain. The artificial neural network has the capability of learning, massive parallelism capability and robustness for disturbance which are necessary for power system application. In this paper, We reviewed the typical topologies and learning algorithms of artifical neural networks which can be used for pattern classification. And we surveyed for the applications of artifical neural network to the power system.

• Investigative Magnetic Resonance Imaging
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• v.26 no.1
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• pp.1-9
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• 2022

Artificial intelligence (AI) powered by deep learning (DL) has shown remarkable progress in image recognition tasks. Over the past decade, AI has proven its feasibility for applications in medical imaging. Various aspects of clinical practice in neuroimaging can be improved with the help of AI. For example, AI can aid in detecting brain metastases, predicting treatment response of brain tumors, generating a parametric map of dynamic contrast-enhanced MRI, and enhancing radiomics research by extracting salient features from input images. In addition, image quality can be improved via AI-based image reconstruction or motion artifact reduction. In this review, we summarize recent clinical applications of DL in various aspects of neuroimaging.

• Annual Conference of KIPS
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• 2023.05a
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• pp.496-498
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• 2023

Brain tumor segmentation problem has challenges in the tumor diversity of location, imbalance, and morphology. Attention mechanisms have recently been used widely to tackle medical segmentation problems efficiently by focusing on essential regions. In contrast, the fusion approaches enhance performance by merging mutual benefits from many models. In this study, we proposed a 3D dual fusion attention network to combine the advantages of fusion approaches and attention mechanisms by residual self-attention and local blocks. Compared to fusion approaches and related works, our proposed method has shown promising results on the BraTS 2018 dataset.