In Korean medicine, tongue diagnosis is one of the important diagnostic methods for diagnosing abnormalities in the body. Representative features that are used in the tongue diagnosis include color, shape, texture, cracks, and tooth marks. When diagnosing a patient through these features, the diagnosis criteria may be different for each oriental medical doctor, and even the same person may have different diagnosis results depending on time and work environment. In order to overcome this problem, recent studies to automate and standardize tongue diagnosis using machine learning are continuing and the basic process of such a machine learning-based tongue diagnosis system is tongue segmentation. In this paper, image data is augmented based on the main tongue features, and backbones of various famous deep learning architecture models are used for automatic tongue segmentation. The experimental results show that the proposed augmentation technique improves the accuracy of tongue segmentation, and that automatic tongue segmentation can be performed with a high accuracy of 99.12%.

Purpose: The purpose of this study is to present a way to alleviate motion sickness(MS) by stimulating acupoint through PEMFs, and to assess the effectiveness of PEMFs against stimulation previously used to stimulate acupoint using biosignal evaluations and surveys. Materials and Methods: Thirteen healthy men participated in the experiment. MS was induced in the participants, and MS relief stimulation was applied for 30 minutes. There were 4 types of MS relief stimulation, and Sham, Reliefband, Transcutaneous electrical nerve stimulation(TENS), and Pulsed electromagnetic fields stimulation(PEMFs) were used. The biosignals were measured during 30 minutes of applying MS relief stimulation, and the symptoms of MS were evaluated through a questionnaire survey. The measured biosignals are Electrocardiogram(ECG), Electrodermal activity(EDA), Respiration, Skin temperature(SKT), and Electrogastrogram(EGG). A one-way ANOVA test was performed for the rate of change by stimulation for MS relief over time. Results: Participants who were stimulated had a sharp decrease in MS symptoms. Biosignals were analyzed to evaluate autonomic nervous system activity, and the parasympathetic nervous system could be activated through stimulation. Conclusion: TENS and PEMFs were more effective in relieving MS symptoms than Reliefband. It is believed that PEMFs will be effective in consideration of the comfort of participants to be applied to actual vehicles, and studies to further verify the effects of PEMFs on MS should be conducted.

The purpose of this study was to investigate the effect on the activity of the upper trapezius and erector spinae according to the length difference of the backpack using electromyogram (EMG). We also conducted a study comparing the center of pressure (COP) and weight distribution before and after using Wii® balance board. Thirty individuals were randomly assigned to the experimental group (n=14) wearing a backpack with a short right strap length and a control group (n=16) wearing a backpack with the same strap length. Data were collected by dividing into groups, carring a 15% weight backpack and walking for 20 minutes on a treadmill. As a result of analyzing the EMG data, there was no significant change in the upper extremity muscle activity of the control group, but it was significantly decreased in the right upper trapezius activity of the experimental group (p<0.05). In addition, there was a significant change of COP in the experimental group (p<0.05), but there was no significant difference of the weight distribution in both groups. Recognizing mechanical changes in the body that may occur due to the asymmetry of the strap length is thought to be helpful for systematic ergonomic intervention according to changes in the external environment in the future.

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.

Recently, artificial intelligence for diagnosis system of obstetric diseases have been actively studied. Artificial intelligence diagnostic assist systems, which support medical diagnosis benefits of efficiency and accuracy, may experience problems of poor learning accuracy and reliability when inappropriate images are the model's input data. For this reason, before learning, We proposed an algorithm to exclude unread cervical imaging. 2,000 images of read cervical imaging and 257 images of unread cervical imaging were used for this study. Experiments were conducted based on the statistical method Radiomics to extract feature values of the entire images for classification of unread images from the entire images and to obtain a range of read threshold values. The degree to which brightness, blur, and cervical regions were photographed adequately in the image was determined as classification indicators. We compared the classification performance by learning read cervical imaging classified by the algorithm proposed in this paper and unread cervical imaging for deep learning classification model. We evaluate the classification accuracy for unread Cervical imaging of the algorithm by comparing the performance. Images for the algorithm showed higher accuracy of 91.6% on average. It is expected that the algorithm proposed in this paper will improve reliability by effectively excluding unread cervical imaging and ultimately reducing errors in artificial intelligence diagnosis.