Nowadays, teenagers and children have many problems with poor posture. In order to timely and accurately identify the health problems of teenagers' posture, this study proposes to use the bottom-up Openpose bone point marking technology based on deep learning in human posture recognition technology to take human posture images and combine them with the evaluation criteria of poor posture to quickly identify whether there are high and low shoulders, high and low hips, Genu varum Genu valgum, head position deviation and other posture problems, and give the corresponding severity level. Manual correction is made by referring to the human skeleton joint points used for posture evaluation specified in the national standard. According to the posture recognition evaluation standard, the angle value required for actual abnormal body diagnosis is measured by simulating manual measurement, and the consistency between the angle value calculated by the bone point marker model and the angle value obtained by manual measurement is analyzed, Four out of the five Cronbach's Alpha consistency coefficients obtained are all greater than 0.8, indicating high consistency. And through manual measurement, the actual severity level of various abnormal postures was obtained, which was compared with the severity level of abnormal postures calculated based on the output of bone key point coordinates from the bone point marker model, with an accuracy rate of over 93%. Therefore, bone point labeling technology can be well applied to the diagnosis of adolescent physical health problems, which is of great significance for rapid and convenient diagnosis of adolescent physical health problems and timely correction and treatment. It breaks away from the limitations of traditional manual measurement methods and improves the efficiency and accuracy of abnormal body detection.
Tongue segmentation is a key step to realize the intelligent tongue diagnosis of traditional Chinese medicine. The segmentation based on deep neural networks has achieved good results with a large amount of data with pixel-level annotation. But it is difficult to obtain a large amount of pixel-level labeled real data. In this work, we propose a simple and effective tongue segmentation method using synthetic data with rich pixel-level annotations. Firstly, we adopt the Fourier transform and its inverse transform to reduce the distribution difference between the target and source domains. Then we construct fold atrous spatial pyramid pooling (FASPP) to get multi-scale information and enhance the correlation among local features for tongue data with different sizes. Finally, the experimental results show the effectiveness of our approach.
Multimodal magnetic resonance imaging (MRI) contains complementary information in anatomical and functional images that help the accurate diagnosis and treatment evaluation of lung cancers. Accurately segmenting tumor regions in each modality can help to obtain comprehensive and precise information. Existing multimodal segmentation methods are mostly used for images with strict registration, but it is difficult to achieve it for lung MRI images. It is challenging to obtain accurate tumor segmentation in lung anatomical and functional MRI images simultaneously. In this paper, we propose a method for lung tumor segmentation in weakly paired anatomical and functional MRI images. Firstly, we use domain adaptation to narrow the gap in features across different modalities, enabling the segmentation network to adapt to different modality data simultaneously. At the same time, we explore a two-stage multimodal co-attention mechanism to help the extraction and effective fusion of multi-modal information. We evaluate the proposed method on lung tumor segmentation with a clinical dataset of 90 chest MRI scans of non-small cell lung cancer (NSCLC). The results show that this method effectively improves the segmentation accuracy of each modality, the DSC of anatomical MRI is increased to 0.81±0.19, and the DSC of functional MRI is increased to 0.77±0.23, which is significantly improved compared with several multimodal tumor segmentation methods (p <0.05).
Snakebite is a major global public health problem, with more than 5 million snakebite victims recorded each year. Timely diagnosis and appropriate treatment of snakebite are the keys to preventing serious complications and improving the patient’s prognosis. However, most medical institutions lack experience in snakebite treatment, which is prone to misdiagnosis or even missed diagnosis, and serious cases endanger the lives of patients. Artificial intelligence is widely used in computer-aided diagnosis systems (CAD) and plays a vital role in snakebite recognition. In recent years, research has tended to build models, ignoring data preparation and analysis. Therefore, there is an urgent need for a rapid and accurate diagnostic method for snakebite to assist clinical diagnostic decision-making. We propose a snakebite auxiliary diagnosis system combining edge computing and machine learning technology. The system has the advantages of simple deployment, strong flexibility, and simple operation and is not limited by the equipment conditions of some grassroots hospitals. Experimental results show that this system can quickly and accurately diagnose the types of venomous snakes, overcome the limitations of traditional snakebite diagnosis methods in various aspects, and provide reliable real-time service tools for medical professionals, especially those in remote areas, allowing rapid diagnosis and treatment of snakebite cases, ultimately reducing morbidity and mortality.
For detecting sleeping and sitting postures, a system based on unrestrained flexible pressure sensor is designed and proposed. The large-area capacitive pressure sensor, which composited by 64-row and 32-column, is designed to capture posture pressure signals. A total of 30 participants were recruited for 10 postures data acquisition, and the acquired images were normalized and bilinearly interpolated. The processed data images were randomly grouped by 80% of the training set and 20% of the test set. And the deep learning neural network (YOLOv5) was used for training. The final recognition accuracy was 99.3%. Accurate division in the posture categories of sleeping and sitting postures is achieved, which is important for realizing posture monitoring.
