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.
IntroductionNanoparticles (NPs) are small entities that consist of a hydroxyapatite core, which can bind ions, proteins, and other organic molecules from the surrounding environment. These small conglomerations can influence environmental calcium levels and have the potential to modulate calcium homeostasis in vivo. Nanoparticles have been associated with various calcium-mediated disease processes, such as atherosclerosis and kidney stone formation. We hypothesized that nanoparticles could have an effect on other calcium-regulated processes, such as wound healing. In the present study, we synthesized pH-sensitive calcium-based nanoparticles and investigated their ability to enhance cutaneous wound repair.MethodsDifferent populations of nanoparticles were synthesized on collagen-coated plates under various growth conditions. Bilateral dorsal cutaneous wounds were made on 8-week-old female Balb/c mice. Nanoparticles were then either administered intravenously or applied topically to the wound bed. The rate of wound closure was quantified. Intravenously injected nanoparticles were tracked using a FLAG detection system. The effect of nanoparticles on fibroblast contraction and proliferation was assessed.ResultsA population of pH-sensitive calcium-based nanoparticles was identified. When intravenously administered, these nanoparticles acutely increased the rate of wound healing. Intravenously administered nanoparticles were localized to the wound site, as evidenced by FLAG staining. Nanoparticles increased fibroblast calcium uptake in vitro and caused contracture of a fibroblast populated collagen lattice in a dose-dependent manner. Nanoparticles also increased the rate of fibroblast proliferation.ConclusionIntravenously administered, calcium-based nanoparticles can acutely decrease open wound size via contracture. We hypothesize that their contraction effect is mediated by the release of ionized calcium into the wound bed, which occurs when the pH-sensitive nanoparticles disintegrate in the acidic wound microenvironment. This is the first study to demonstrate that calcium-based nanoparticles can have a therapeutic benefit, which has important implications for the treatment of wounds.
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.
Cephalometric analysis has long been one of the most helpful approaches in evaluating cranio-maxillo-facial skeletal profile. To perform this, locating anatomical landmarks on an X-ray image is a crucial step, demanding time and expertise. An automated cephalogram analyzer, if developed, will be a great help for practitioners. Artificial intelligence, including machine learning is emerging these days. Deep learning is one of the most developing techniques in data science field. The authors attempted to enhance the accuracy of an automated landmark predicting system utilizing multi-phase deep learning and voting. To guarantee objectivity, an open-to-the-public dataset, cephalometric images accompanied with coordinate values of 19 landmarks, were used. A regressional system was developed, consisted with convolutional neural networks of three phases. First phase network was to determine approximate position of each landmark, inputting whole area of compressed original images. Five secondary networks were to narrow down the area, based on the first phase prediction. Third phase networks were trained by small areas around respective landmarks, with original resolution. Third phase prediction with voting was done inputting 81 shifted areas. Successful detection rates improved as the phase advances. Voting in third phase improved successful detection rate. In comparison with previously reported benchmarks, using the same dataset, proposed system marked better results. Within the physical limitation of memory and computation, multi-phase deep learning may be a solution to deal with large images.
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.
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