Purpose: This study evaluates serial radiographic changes in the maxillary sinus of patients with oral cancer after an inferior maxillectomy and a soft tissue free flap reconstruction.
Methods: Fifty-six patients were evaluated between Oct 2005 and Mar 2017 from an institutional database. Preoperative and surveillance imaging was reviewed at set time-points.
Introduction: In the era of precision preventive medicine, susceptible genetic markers for oro-/hypopharyngeal squamous cell carcinoma (OPSCC) have been investigated for genome-wide associations.
Materials And Methods: A case-control study including 659 male head and neck squamous cell carcinoma (HNSCC) patients, including 331 oropharyngeal cancer, treated between March 1996 and December 2016 and 2400 normal controls was performed. A single-nucleotide polymorphism (SNP) array was used to determine genetic loci that increase susceptibility to OPSCC.
By collecting the magnetic field information of each spatial point, we can build a magnetic field fingerprint map. When the user is positioning, the magnetic field measured by the sensor is matched with the magnetic field fingerprint map to identify the user's location. However, since the magnetic field is easily affected by external magnetic fields and magnetic storms, which can lead to "local temporal-spatial variation", it is difficult to construct a stable and accurate magnetic field fingerprint map for indoor positioning.
View Article and Find Full Text PDFSmart toothbrushes equipped with inertial sensors are emerging as high-tech oral health products in personalized health care. The real-time signal processing of nine-axis inertial sensing and toothbrush posture recognition requires high computational resources. This paper proposes a recurrent probabilistic neural network (RPNN) for toothbrush posture recognition that demonstrates the advantages of low computational resources as a requirement, along with high recognition accuracy and efficiency.
View Article and Find Full Text PDFStereo vision utilizes two cameras to acquire two respective images, and then determines the depth map by calculating the disparity between two images. In general, object segmentation and stereo matching are some of the important technologies that are often used in establishing stereo vision systems. In this study, we implement a highly efficient self-organizing map (SOM) neural network hardware accelerator as unsupervised color segmentation for real-time stereo imaging.
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