Statement Of Problem: Transparent facial orthoses (TFOs) are commonly used for the treatment of craniomaxillofacial trauma and burns to prevent hypertrophic and keloid scarring. A TFO is typically customized to the patient's facial contours and relies on a precise fit to ensure good rehabilitative performance. A smart method of TFO design and manufacture is needed which does not require an experienced prosthetist, allowing for rapidly produced, well-fitting TFOs. Whether the rapid application reduces the final level of patient scarring is unclear.
Purpose: The purpose of this clinical study was to determine whether a scalable, automated design-through-manufacture pipeline for patient specific TFO fabrication would be successful.
Material And Methods: The automated pipeline received a 3-dimensional (3D) facial scan captured from a depth sensitive mobile phone camera. The scan was cleaned, aligned, and fit to a template mesh, with a known connectivity. The resultant fitted scan was passed into an automated design pipeline, outputting a 3D printable model of a custom TFO. The TFOs were fabricated with 3D printing and were both physically and digitally evaluated to test the fidelity of a digital fit testing system.
Results: A total of 10 individuals were scanned with 5 different scanning technologies (STs). All scans were passed through an automated fitting pipeline and categorized into 2 groups. Each ST was digitally fitted to a ground truth scan. In this manner, a Euclidean distance map was built to the actual facial geometry for each scan. Heatmaps of 3D Euclidean distances were made for all participant faces.
Conclusions: The ability to automatically design and manufacture a custom fitted TFO using commercially available 3D scanning and 3D printing technology was successfully demonstrated. After considering equipment size and operational personnel requirements, vat polymerization (VP) technology was found to be the most promising route to TFO manufacture.
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http://dx.doi.org/10.1016/j.prosdent.2022.08.012 | DOI Listing |
J Orthop Surg Res
January 2025
Department of Mechanical Engineering, Centre for Mechanical Technology & Automation (TEMA), University of Aveiro, Aveiro, 3810-193, Portugal.
Background: Bone fractures represent a global public health issue. Over the past few decades, a sustained increase in the number of incidents and prevalent cases have been reported, as well as in the years lived with disability. Current monitoring techniques predominantly rely on imaging methods, which can result in subjective assessments, and expose patients to unnecessary cumulative doses of radiation.
View Article and Find Full Text PDFBMC Cancer
January 2025
Department of Urology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, South Korea.
Background: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.
Materials And Methods: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas.
Sci Data
January 2025
Shanghai Artificial Intelligence Research Institute Co., Ltd., Shanghai, 200240, China.
Academic data processing is crucial in scientometrics and bibliometrics, such as research trending analysis and citation recommendation. Existing datasets in this domain have predominantly concentrated on textual data, overlooking the importance of visual elements. To bridge this gap, we introduce a multidisciplinary multimodal aligned dataset (MMAD) specifically designed for academic data processing.
View Article and Find Full Text PDFNat Biotechnol
January 2025
Department of Automation, Tsinghua University, Beijing, China.
Super-resolution (SR) neural networks transform low-resolution optical microscopy images into SR images. Application of single-image SR (SISR) methods to long-term imaging has not exploited the temporal dependencies between neighboring frames and has been subject to inference uncertainty that is difficult to quantify. Here, by building a large-scale fluorescence microscopy dataset and evaluating the propagation and alignment components of neural network models, we devise a deformable phase-space alignment (DPA) time-lapse image SR (TISR) neural network.
View Article and Find Full Text PDFSci Rep
January 2025
School of Mechanical & Electrical Engineering, Guizhou Normal University, Guiyang, China.
Understanding the mechanical properties of Rosa sterilis S.D. Shi is important for the design and improvement of related mechanical equipment for planting, picking, processing, and transporting Rosa sterilis S.
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