Background: Face transplant teams have an ethical responsibility to restore the donor's likeness after allograft procurement. This has been achieved with masks constructed from facial impressions and three-dimensional printing. The authors compare the accuracy of conventional impression and three-dimensional printing technology.
Methods: For three subjects, a three-dimensionally-printed mask was created using advanced three-dimensional imaging and PolyJet technology. Three silicone masks were made using an impression technique; a mold requiring direct contact with each subject's face was reinforced by plaster bands and filled with silicone. Digital models of the face and both masks of each subject were acquired with Vectra H1 Imaging or Artec scanners. Each digital mask model was overlaid onto its corresponding digital face model using a seven-landmark coregistration; part comparison was performed. The absolute deviation between each digital mask and digital face model was compared with the Mann-Whitney U test.
Results: The absolute deviation (in millimeters) of each digitally printed mask model relative to the digital face model was significantly smaller than that of the digital silicone mask model (subject 1, 0.61 versus 1.29, p < 0.001; subject 2, 2.59 versus 2.87, p < 0.001; subject 3, 1.77 versus 4.20, p < 0.001). Mean cost and production times were $720 and 40.2 hours for three-dimensionally printed masks, and $735 and 11 hours for silicone masks.
Conclusions: Surface analysis shows that three-dimensionally-printed masks offer greater surface accuracy than silicone masks. Greater donor resemblance without additional risk to the allograft may make three-dimensionally-printed masks the superior choice for face transplant teams.
Clinical Question/level Of Evidence: Therapeutic, V.
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Brief Bioinform
November 2024
Center for Genomics and Biotechnology, Fujian Provincial Key Laboratory of Haixia Applied Plant Systems Biology, Haixia Institute of Science and Technology, Fujian Agriculture and Forestry University, No. 15 Shangxiadian Road, Cangshan District, Fuzhou 350002, China.
Spatial transcriptomics (ST) technologies enable dissecting the tissue architecture in spatial context. To perceive the global contextual information of gene expression patterns in tissue, the spatial dependence of cells must be fully considered by integrating both local and non-local features by means of spatial-context-aware. However, the current ST integration algorithm ignores for ST dropouts, which impedes the spatial-aware of ST features, resulting in challenges in the accuracy and robustness of microenvironmental heterogeneity detecting, spatial domain clustering, and batch-effects correction.
View Article and Find Full Text PDFFront Plant Sci
December 2024
School of Astronautics, Beihang University, Beijing, China.
Hyperspectral image classification in remote sensing often encounters challenges due to limited annotated data. Semi-supervised learning methods present a promising solution. However, their performance is heavily influenced by the quality of pseudo labels.
View Article and Find Full Text PDFPeerJ
January 2025
State Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, West China Hospital of Stomatology, Sichuan University, Chengdu, China.
Objective: The study aims to develop a diagnostic model using intraoral photographs to accurately detect and classify early detection of enamel demineralization on tooth surfaces.
Methods: A retrospective analysis was conducted with 208 patients aged 14 to 44. A total of 624 high-quality digital images captured under standardized conditions were used to construct a deep learning model based on the Mask region-based convolutional neural network (Mask R-CNN).
Sci Rep
January 2025
ADAPT Research Centre, School of Computer Science, University of Galway, Galway, Ireland.
This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements.
View Article and Find Full Text PDFMach Learn Clin Neuroimaging (2024)
December 2024
Stanford University, Stanford, CA 94305, USA.
Deep learning can help uncover patterns in resting-state functional Magnetic Resonance Imaging (rs-fMRI) associated with psychiatric disorders and personal traits. Yet the problem of interpreting deep learning findings is rarely more evident than in fMRI analyses, as the data is sensitive to scanning effects and inherently difficult to visualize. We propose a simple approach to mitigate these challenges grounded on sparsification and self-supervision.
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