Background: Cephalometric analysis has long been, and still is one of the most important tools in evaluating craniomaxillofacial skeletal profile. To perform this, manual tracing of x-ray film and plotting landmarks have been required. This procedure is time-consuming and demands expertise. In these days, computerized cephalometric systems have been introduced; however, tracing and plotting still have to be done on the monitor display. Artificial intelligence is developing rapidly. Deep learning is one of the most evolving areas in artificial intelligence. The authors made an automated landmark predicting system, based on a deep learning neural network. Methods: On a personal desktop computer, a convolutional network was built for regression analysis of cephalometric landmarks’ coordinate values. Lateral cephalogram images were gathered through the internet and 219 images were obtained. Ten skeletal cephalometric landmarks were manually plotted and coordinate values of them were listed. The images were randomly divided into 153 training images and 66 testing images. Training images were expanded 51 folds. The network was trained with the expanded training images. With the testing images, landmarks were predicted by the network. Prediction errors from manually plotted points were evaluated. Results: Average and median prediction errors were 17.02 and 16.22 pixels. Angles and lengths in cephalometric analysis, predicted by the neural network, were not statistically different from those calculated from manually plotted points. Conclusion: Despite the variety of image quality, using cephalogram images on the internet is a feasible approach for landmark prediction.

Wound healing process is a complex and highly orchestrated process that ultimately results in the formation of scar tissue. Hypertrophic scar contracture is considered to be a pathologic and exaggerated wound healing response that is known to be triggered by repetitive mechanical forces. We now show that Transient Receptor Potential (TRP) C3 regulates the expression of fibronectin, a key regulatory molecule involved in the wound healing process, in response to mechanical strain via the NFkB pathway. TRPC3 is highly expressed in human hypertrophic scar tissue and mechanical stimuli are known to upregulate TRPC3 expression in human skin fibroblasts in vitro. TRPC3 overexpressing fibroblasts subjected to repetitive stretching forces showed robust expression levels of fibronectin. Furthermore, mechanical stretching of TRPC3 overexpressing fibroblasts induced the activation of nuclear factor-kappa B (NFκB), a regulator fibronectin expression, which was able to be attenuated by pharmacologic blockade of either TRPC3 or NFκB. Finally, transplantation of TRPC3 overexpressing fibroblasts into mice promoted wound contraction and increased fibronectin levels in vivo. These observations demonstrate that mechanical stretching drives fibronectin expression via the TRPC3-NFkB axis, leading to intractable wound contracture. This model explains how mechanical strain on cutaneous wounds might contribute to pathologic scarring.

The growth factors were surely concentrated in PRF. This result can support basis of good clinical outcomes. For effective application of PRF, the knowledge that growth factors and cells are not equally distributed in PRF should be utilized.

Indocyanine green lymphography, displayed as infrared image, is very useful in identifying lymphatic vessels during surgeries. Surgeons refer the infrared image on the displays as they proceed the operation. Those displays are usually placed on the walls or besides the operation tables. The surgeons cannot watch the infrared image and the operation field simultaneously. They have to move their heads and visual lines. An augmented reality system was developed for simultaneous referring of the infrared image, overlaid on real operation field view. A surgeon wore a see-through eye-glasses type display during lymphatico-venous anastomosis surgery. Infrared image was transferred wirelessly to the display. The surgeon was able to recognize fluorescently shining lymphatic vessels projected on the glasses and dissect them out.

Currently, laser radiation is used routinely in medical applications. For infrared lasers, bone ablation and the healing process have been reported, but no laser systems are established and applied in clinical bone surgery. Furthermore, industrial laser applications utilize computer and robot assistance; medical laser radiations are still mostly conducted manually nowadays. The purpose of this study was to compare the histological appearance of bone ablation and healing response in rabbit radial bone osteotomy created by surgical saw and ytterbium-doped fiber laser controlled by a computer with use of nitrogen surface cooling spray. An Ytterbium (Yb)-doped fiber laser at a wavelength of 1,070 nm was guided by a computer-aided robotic system, with a spot size of 100 μm at a distance of approximately 80 mm from the surface. The output power of the laser was 60 W at the scanning speed of 20 mm/s scan using continuous wave system with nitrogen spray level 0.5 MPa (energy density, 3.8 × 10(4) W/cm(2)). Rabbits radial bone osteotomy was performed by an Yb-doped fiber laser and a surgical saw. Additionally, histological analyses of the osteotomy site were performed on day 0 and day 21. Yb-doped fiber laser osteotomy revealed a remarkable cutting efficiency. There were little signs of tissue damage to the muscle. Lased specimens have shown no delayed healing compared with the saw osteotomies. Computer-assisted robotic osteotomy with Yb-doped fiber laser was able to perform. In rabbit model, laser-induced osteotomy defects, compared to those by surgical saw, exhibited no delayed healing response.