The study empirically analyzed the differences in industry distribution and innovation activity performance in the metropolitan and non-metropolitan areas of Korea's bio companies, which are highlighted as future growth engines. The main innovation activities of the bio industry, which are focused on science and technology and expressed with high uncertainty, were analyzed, centering on human resources, technology cooperation, and investment promotion. As a result of the analysis, the biomedical industry in the metropolitan area was found to have a high proportion, and bio foods, bio-based chemicals, and energy industries in the non-metropolitan area, respectively. Moreover, the innovation activity performances differed between the two regions. In particular, the notable characteristics included human resources, investment promotion, and technical cooperation with medical institutions in the metropolitan area with a high proportion of biomedical industries, and technology personnel exchange and cooperation with private research institutions in the non-metropolitan area, which has a high proportion of bio foods, bio-based chemicals, and energy industries. This study is significant in that it is the first study to compare and analyze the performance of innovative activities based on the distribution of industries in the bio-industry, focusing on human resources, technology cooperation, and investment promotion. In addition, after investigating the distribution status and competitiveness of the domestic bio-industry by region, it will analyze the status and characteristics of the domestic bio-industry and present policy implications to implement relevant promotion policy more efficiently.

Although upper respiratory tract infections (URTIs) are common diseases, there have been no studies of their relationship with the indices obtained from tongue image analyses. The purpose of this study was to identify a tongue index through an analysis of tongue images (TIs) showing significant changes before and after treatment in patients with URTIs. A computerized tongue image acquisition system was developed to acquire TIs from subjects in the same environment. An image was taken from each of 39 URTI patients and 39 healthy controls. For the patients, images were acquired before and after treatment to identify changes. The tongue area was classified into a tongue body and a tongue coating, and the coating ratio between the two areas, the average value of the colour of each area, and teeth marks were calculated. No significant difference was observed in age or sex between the URTI patients and control participants. Heart rates were slightly different. The analysis of TIs showed that the luminance of the tongue coating and the coating area ratio were decreased, while the reddish value of the tongue body at the centre area increased as the treatment progressed. Tongue coating and body in URTIs had different colour and shape from those in the normal. It is expected that this result will contribute not only to the objectification of traditional Chinese medicine but also to diagnostic methods that do not involve face-to-face physician visit during the pandemic.

Clinical ultrasound (US) is a widely used imaging modality with various clinical applications. However, capturing a large field of view often requires specialized transducers which have limitations for specific clinical scenarios. Panoramic imaging offers an alternative approach by sequentially aligning image sections acquired from freehand sweeps using a standard transducer. To reconstruct a 3D volume from these 2D sections, an external device can be employed to track the transducer's motion accurately. However, the presence of optical or electrical interferences in a clinical setting often leads to incorrect measurements from such sensors. In this paper, we propose a deep learning (DL) framework that enables the prediction of scan trajectories using only US data, eliminating the need for an external tracking device. Our approach incorporates diverse data types, including correlation volume, optical flow, B-mode images, and rawer data (IQ data). We develop a DL network capable of effectively handling these data types and introduce an attention technique to emphasize crucial local areas for precise trajectory prediction. Through extensive experimentation, we demonstrate the superiority of our proposed method over other DL-based approaches in terms of long trajectory prediction performance. Our findings highlight the potential of employing DL techniques for trajectory estimation in clinical ultrasound, offering a promising alternative for panoramic imaging.

Purpose: The objective is to extract insights that can contribute to the formulation of harmonized international policies and support measures for innovative medical devices and management systems. This study aims to propose effective strategies for future medical device innovation and healthcare delivery. Results: It investigates technological advancements, regulatory approval systems, insurance policies, and successful commercialization cases in South Korea, the United States, and the European Union. In 2018, the FDA implemented insurance coverage for Software as a Medical Device (SaMD) and recognized insurance coverage for Digital Therapeutics (DTx). Germany is a country that ensures permanent reimbursement for healthcare applications since 2020, making it the first country to provide legal health insurance coverage for fostering a digital ecosystem. Conclusion: The findings of this research highlight the importance of cultivating a supportive regulatory and environmental framework to facilitate the adoption of innovative medical devices. Continuous support for research and development (R&D) efforts by companies, along with the validation of clinical effectiveness, is crucial.

The demand for skincare has increased due to the end of the COVID-19 pandemic, leading to a focus on skincare devices and technologies designed to improve the delivery of cosmetics. Among these technologies, skincare medical devices that utilize plasma therapy (Plasma) and sonophoresis (Sono) are commonly used in dermatology clinics. However, there is still a lack of quantitative analysis for transdermal absorption effects of Plasma and Sono skincare medical devices. In this study, we quantified enhanced transdermal absorption effects of Plasma and Sono devices through in-silico and ex-vivo studies. The Sono treatment demonstrated an increased transdermal absorption effect, showing a 10~13% difference in penetration compared to the control group in the in-silico experiment, and 159% and 184% increase in the ex-vivo experiment. The Plasma treatment revealed increased transdermal absorption effects, with a 1.0~2.5% penetration difference in the in-silico experiment, and a 124% increase in the ex-vivo experiment compared to the control group. We also observed a synergistic effect from the combined treatment of Plasma and Sono, as indicated by the highest increases of 197% and 242% in penetration. Furthermore, we have determined the optimal device settings and treatment conditions for Plasma-Sono skincare medical devices. Notably, higher on/off durations (Intensity levels) and longer Sono treatments resulted in greater transdermal absorption effects.

This study attempted to observe muscle activity and muscle fatigue among isotonic exercise of Biceps Brachii and Deltoidus Medius, which correspond to upper limb muscles, using metronome. For the experiment, 13 adult men participated to evaluate biosignals (ECG, EMG). For quantitative evaluation, 1RM and MVIC test were carried out and a constant pace isotonic excise session was conducted. Five sets of exercise were performed, and each set proceeded to the failure point while the speed condition (30bpm) was assigned. As a result of the experiment, muscle activity in both muscles was significantly reduced in fifth set compared to first set. Muscle fatigue has been confirmed to occur at a significant level within the set, but there was only a significant difference in both muscles in the first and second sets between sets. This is similar to the results of previous studies that gives the same rest time(2min), but further research is needed to see if the conditions for the number of repetitions are affected. Based on the recent increase of interest in muscle strength exercise, this study was conducted to observe the results by varying the conditions of common exercise.

Scoliosis is a three-dimensional deformation of the spine that is a deformity induced by physical or disease-related causes as the spine is rotated abnormally. Early detection has a significant influence on the possibility of nonsurgical treatment. To train a deep learning model with preprocessed images and to evaluate the results with and without data augmentation to enable the diagnosis of scoliosis based only on a chest X-ray image. The preprocessed images in which only the spine, rib contours, and some hard tissues were left from the original chest image, were used for learning along with the original images, and three CNN(Convolutional Neural Networks) models (VGG16, ResNet152, and EfficientNet) were selected to proceed with training. The results obtained by training with the preprocessed images showed a superior accuracy to those obtained by training with the original image. When the scoliosis image was added through data augmentation, the accuracy was further improved, ultimately achieving a classification accuracy of 93.56% with the ResNet152 model using test data. Through supplementation with future research, the method proposed herein is expected to allow the early diagnosis of scoliosis as well as cost reduction by reducing the burden of additional radiographic imaging for disease detection.