Mcdanielrichard0208

The purpose of this study was to develop and validate a positioning method with hand-guiding and contact position feedback of robot based on a human-robot collaborative dental implant system (HRCDIS) for robotic guided dental implant surgery.

An HRCDIS was developed based on a light-weight cooperative robot arm, UR5. A three-dimensional (3D) virtual partially edentulous mandibular bone was reconstructed using the cone bone computed tomography images. After designing the preoperative virtual implant planning using the computer software, a fixation guide worn on teeth for linking and fixing positioning marker was fabricated by 3D printing. The fixation guide with the positioning marker and a resin model mimicking the oral tissues were assembled on a head phantom. The planned implant positions were derived by the coordinate information of the positioning marker. The drilling process using the HRCDIS was conducted after mimicking the experimental set-up and planning the drilling trajectory. Deviations between actual and planned implant positions were measured and analyzed.

The head phantom experiments results showed that the error value of the central deviation at hex (refers to the center of the platform level of the implant) was 0.79±0.17mm, central deviation at the apex was 1.26±0.27mm, horizontal deviation at the hex was 0.61±0.19mm, horizontal deviation at the apex was 0.91±0.55mm, vertical deviation at the hex was 0.38±0.17mm, vertical deviation at the apex was 0.37±0.20mm, and angular deviation was 3.77±1.57°.

The results from this study preliminarily validate the feasibility of the accurate navigation method of the HRCDIS.

The results from this study preliminarily validate the feasibility of the accurate navigation method of the HRCDIS.

It is important to subdivide Parkinson's disease (PD) into subtypes, enabling potentially earlier disease recognition and tailored treatment strategies. We aimed to identify reproducible PD subtypes robust to variations in the number of patients and features.

We applied multiple feature-reduction and cluster-analysis methods to cross-sectional and timeless data, extracted from longitudinal datasets (years 0, 1, 2 & 4; Parkinson's Progressive Marker Initiative; 885 PD/163 healthy-control visits; 35 datasets with combinations of non-imaging, conventional-imaging, and radiomics features from DAT-SPECT images). Hybrid machine-learning systems were constructed invoking 16 feature-reduction algorithms, 8 clustering algorithms, and 16 classifiers (C-index clustering evaluation used on each trajectory). selleck products We subsequently performed i) identification of optimal subtypes, ii) multiple independent tests to assess reproducibility, iii) further confirmation by a statistical approach, iv) test of reproducibility to thT images (segmented using MRI). The PD subtypes provided were robust to the number of the subjects, and features.Fundus photography is commonly used for screening, diagnosis, and monitoring of various diseases affecting the eye. In addition, it has shown promise in the diagnosis of brain diseases and evaluation of cardiovascular risk factors. Good image quality is important if diagnosis is to be accurate and timely. Here, we propose a method that automatically grades image quality on a continuous scale which is more flexible than binary quality classification. The method utilizes random forest regression models trained on image features discovered automatically by combining basic image filters using simulated annealing as well as features extracted with the discrete Fourier transform. The method was developed and tested on images from two different fundus camera models. The quality of those images was rated on a continuous scale from 0.0 to 1.0 by five experts. In addition, the method was tested on DRIMDB, a publicly available dataset with binary quality ratings. On the DRIMDB dataset the method achieves an accuracy of 0.981, sensitivity of 0.993 and specificity of 0.958 which is consistent with the state of the art. When evaluating image quality on a continuous scale the method outperforms human raters.Salivary gland ultrasonography (SGUS) has proven to be a promising tool for diagnosing various diseases manifesting with abnormalities in salivary glands (SGs), including primary Sjögren's syndrome (pSS). At present, the major obstacle for establishing SUGS as a standardized tool for pSS diagnosis is its low inter/intra observer reliability. The aim of this study was to address this problem by proposing a robust deep learning-based solution for the automated segmentation of SGUS images. For these purposes, four architectures were considered a fully convolutional neural network, fully convolutional "DenseNets" (FCN-DenseNet) network, U-Net, and LinkNet. During the course of the study, the growing HarmonicSS cohort included 1184 annotated SGUS images. Accordingly, the algorithms were trained using a transfer learning approach. With regard to the intersection-over-union (IoU), the top-performing FCN-DenseNet (IoU = 0.85) network showed a considerable margin above the inter-observer agreement (IoU = 0.76) and slightly above the intra-observer agreement (IoU = 0.84) between clinical experts. Considering its accuracy and speed (24.5 frames per second), it was concluded that the FCN-DenseNet could have wider applications in clinical practice. Further work on the topic will consider the integration of methods for pSS scoring, with the end goal of establishing SGUS as an effective noninvasive pSS diagnostic tool. To aid this progress, we created inference (frozen models) files for the developed models, and made them publicly available.The growing interest in the auricular anatomy is due to two different strands of research 1) in the medical field it is associated with autologous ear reconstruction, a surgery adopted following trauma or congenital malformations; 2) in surveillance and law enforcement the ear is used for human detection and recognition. Alternative systems of ear analysis can be differentiated for the type of input data (two-dimensional, three-dimensional or both), for the type of acquisition tools (3D scanner, photographs, video surveillance, etc.) and finally for the adopted algorithms. Although the segmentation and recognition of the ear from the face is a widely discussed topic in literature, the detection and recognition of individual anatomical elements has not yet been studied in depth. To this end, this work lays the foundation for the identification of the auricular elements through image processing algorithms. The proposed algorithm automatically identifies the contours of the main anatomical elements by processing depth map images.