Objective: Cone beam computed tomography (CBCT) images are being increasingly used to acquire three-dimensional (3D) models of the skull for additive manufacturing purposes. However, the accuracy of such models remains a challenge, especially in the orbital area. The aim of this study is to assess the impact of four different CBCT imaging positions on the accuracy of the resulting 3D models in the orbital area.
Methods: An anthropomorphic head phantom was manufactured by submerging a dry human skull in silicon to mimic the soft tissue attenuation and scattering properties of the human head. The phantom was scanned on a ProMax 3D MAX CBCT scanner using 90 and 120 kV for four different field of view positions: standard; elevated; backwards tilted; and forward tilted. All CBCT images were subsequently converted into 3D models and geometrically compared with a "gold-standard" optical scan of the dry skull.
Results: Mean absolute deviations of the 3D models ranged between 0.15 ± 0.11 mm and 0.56 ± 0.28 mm. The elevated imaging position in combination with 120 kV tube voltage resulted in an improved representation of the orbital walls in the resulting 3D model without compromising the accuracy.
Conclusions: Head positioning during CBCT imaging can influence the accuracy of the resulting 3D model. The accuracy of such models may be improved by positioning the region of interest ( the orbital area) in the focal plane (Figure 2a) of the CBCT X-ray beam.
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http://dx.doi.org/10.1259/dmfr.20220104 | DOI Listing |
Front Oncol
December 2024
Department of Radiology, Huadong Hospital, Fudan University, Shanghai, China.
Background: This study aimed to develop and validate a multiregional radiomic-based composite model to predict symptomatic radiation pneumonitis (SRP) in non-small cell lung cancer (NSCLC) patients treated with stereotactic body radiation therapy (SBRT).
Materials And Methods: 189 patients from two institutions were allocated into training, internal validation and external testing cohorts. The associations between the SRP and clinic-dosimetric factors were analyzed using univariate and multivariate regression.
IEEE Robot Autom Lett
February 2025
Department of Mechanical Engineering, Columbia University in the City of New York, NY, USA.
Intent inferral on a hand orthosis for stroke patients is challenging due to the difficulty of data collection. Additionally, EMG signals exhibit significant variations across different conditions, sessions, and subjects, making it hard for classifiers to generalize. Traditional approaches require a large labeled dataset from the new condition, session, or subject to train intent classifiers; however, this data collection process is burdensome and time-consuming.
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December 2024
Department of Data and Information, The Children's Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Objective: While current multimodal approaches in the diagnosis and severity assessment of pneumonia demonstrate remarkable performance, they frequently overlook the issue of modality absence-a common challenge in clinical practice. Thus, we present the (RMT) model, crafted to bridge this gap. The RMT model aims to enhance diagnosis and severity assessment accuracy in situations with incomplete data, thereby ensuring it meets the complex needs of real-world clinical settings.
View Article and Find Full Text PDFDigit Health
December 2024
School of Computer Science, University of Birmingham, Birmingham, UK.
Objective: The study aims to present an active learning approach that automatically extracts clinical concepts from unstructured data and classifies them into explicit categories such as Problem, Treatment, and Test while preserving high precision and recall and demonstrating the approach through experiments using i2b2 public datasets.
Methods: Initially labeled data are acquired from a lexical-based approach in sufficient amounts to perform an active learning process. A contextual word embedding similarity approach is adopted using BERT base variant models such as ClinicalBERT, DistilBERT, and SCIBERT to automatically classify the unlabeled clinical concept into explicit categories.
Ready-to-use supplemental foods (RUSF) are energy-dense meals formulated to prevent and treat moderate and severe childhood acute malnutrition (MAM and SAM) in high-risk settings. Although lifesaving, the degree and durability of weight recovery with RUSF is unpredictable. We examined whether environmental enteric dysfunction (EED) and gut microbiota perturbations are risk factors for RUSF failure in a birth cohort of 416 rural Pakistani children followed for growth, common childhood illnesses, and biomarkers from blood, urine, and stool.
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