Re-operations and revisions are often performed in patients who have undergone total shoulder arthroplasty (TSA) and reverse total shoulder arthroplasty (RTSA). This necessitates an accurate recognition of the implant model and manufacturer to set the correct apparatus and procedure according to the patient’s anatomy as personalized medicine. Owing to unavailability and ambiguity in the medical data of a patient, expert surgeons identify the implants through a visual comparison of X-ray images. False steps cause heedlessness, morbidity, extra monetary weight, and a waste of time. Despite significant advancements in pattern recognition and deep learning in the medical field, extremely limited research has been conducted on classifying shoulder implants. To overcome these problems, we propose a robust deep learning-based framework comprised of an ensemble of convolutional neural networks (CNNs) to classify shoulder implants in X-ray images of different patients. Through our rotational invariant augmentation, the size of the training dataset is increased 36-fold. The modified ResNet and DenseNet are then combined deeply to form a dense residual ensemble-network (DRE-Net). To evaluate DRE-Net, experiments were executed on a 10-fold cross-validation on the openly available shoulder implant X-ray dataset. The experimental results showed that DRE-Net achieved an accuracy, F1-score, precision, and recall of 85.92%, 84.69%, 85.33%, and 84.11%, respectively, which were higher than those of the state-of-the-art methods. Moreover, we confirmed the generalization capability of our network by testing it in an open-world configuration, and the effectiveness of rotational invariant augmentation.

Human action recognition using a camera-based surveillance system remains a challenging task. In particular, action recognition is difficult when a human is not visible in an image captured in a dark environment. The existing studies have utilized near-infrared (NIR) and thermal cameras to solve this problem. Compared to NIR cameras, thermal cameras enable long-and short-distance objects to be visible without an additional illuminator. However, thermal cameras have two major disadvantages: a halo effect and a temperature similarity. A halo effect occurs around an object with a high temperature. In a human object, such a halo effect is similar to a shadow under the body area. It is more difficult to segment a human area from an image with a halo effect. Moreover, if the background and human object have similar temperatures, it becomes more difficult to segment the human area. These disadvantages influence not only the accuracy of the segmentation of the human area but also the performance of human action recognition. Unfortunately, no studies have considered these issues. To address these problems, this study proposes the cycle-consistent generative adversarial network (CycleGAN)-based methods for removing halo effects from thermal images and restoring the areas of the human bodies. In addition, this study also considered a method for creating a skeleton image from a thermal image to analyze body movements. To extract more spatial and temporal features from skeleton image sequences thus created, a method for human action recognition that combines a convolutional neural network (CNN) and long short-term memory (LSTM) was proposed. In an experiment using an open database (Dongguk activities & actions database (DA&A-DB2)), the proposed method demonstrated a better performance than the existing methods.INDEX TERMS Human action recognition, halo effect, image restoration and skeleton generation, thermal camera, CNN stacked LSTM, and CycleGAN.

Our study aimed to investigate the effect of bone morphogenetic protein-2 (BMP-2) bound to silk fibroin and β-tricalcium phosphate (SF/β-TCP) hybrid on the healing of critical-size radial defects in rabbits. A 15-mm critical-size defect was induced at mid-diaphysis in the left radius of 20 New Zealand white rabbits (average age, 3.5 months; weight, 2.5-3.0 kg). The animals were randomized into Group 1 (SF/β-TCP combined with BMP-2), Group 2 (SF/β-TCP alone), and Group 3 (nothing implanted). Radiographs were obtained every 2 weeks and euthanasia was performed after 8 weeks for visual, radiological, micro-computed tomography (micro-CT), and histological studies. Eight weeks after implantation (SF/β-TCP combined with BMP-2), radiographs showed that new bone formed on the surface of the implant and had bridged the defect in Group 1. Micro-CT imaging also confirmed the formation of new bone around the implant, and the newly formed bone was quantified. Histological examination revealed newly formed bone in the implanted area. Meanwhile, there was no formation of new bone in Group 3. Among the groups, most active formation of new bones was found in Group 1, while there was no difference between Group 2 and Group 3. Based on these results, we concluded that BMP-2-SF/β-TCP showed significant improvement in healing of critical-size defects. Therefore, the combination of BMP-2 and SF/β-TCP would be useful in the field of bone tissue engineering.

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