Publications by authors named "G A de Jong"
Article Synopsis
- A knowledge gap exists regarding the integration of 3D printing and extended reality (XR) technologies into anatomy education, and this report details an elective course designed to address that gap.
- The 10-week course taught undergraduate students how to create, evaluate, and use 3D and XR models based on radiological data, with feedback gathered through a survey rating various aspects of the course experience.
- Student evaluations showed that the 3D and XR models were seen as insightful and motivating, suggesting a positive impact on engagement in anatomy education, while highlighting the need for future research on their educational effectiveness.
Introduction: Craniofacial microsomia (CFM) is classified using the subjective Orbit, Mandible, Ear, Nerve and Soft tissue (OMENS) tool. Digital stereophotogrammetry (i.e.
View Article and Find Full Text PDF: In the literature, relationships between being married and having prediabetes or diabetes are inconsistent. We aimed to investigate whether marriage is a protective or risk factor for prediabetes and to uncover new insights into its impact on prediabetes. : In this cross-sectional observational study, questionnaires were distributed by email to 1039 staff members who participated in an employee health check from a hospital affiliated with a medical university in Taiwan.
View Article and Find Full Text PDFObjective: To investigate the feasibility of creating an artificial intelligence (AI) algorithm to enhance prosthetic socket shapes for transtibial prostheses, aiming for a less operator-dependent, standardized approach.
Design: The study comprised 2 phases: first, developing an AI algorithm in a cross-sectional study to predict prosthetic socket shapes. Second, testing the AI-predicted digitally measured and standardized designed (DMSD) prosthetic socket against a manually measured and designed (MMD) prosthetic socket in a 2-week within-subject cross-sectional study.
Objectives: In orthognatic surgery, one of the primary determinants for reliable three-dimensional virtual surgery planning (3D VSP) and an accurate transfer of 3D VSP to the patient in the operation room is the condylar seating. Incorrectly seated condyles would primarily affect the accuracy of maxillary-first bimaxillary osteotomies as the maxillary repositioning is dependent on the positioning of the mandible in the cone-beam computed tomography (CBCT) scan. This study aimed to develop and validate a novel tool by utilizing a deep learning algorithm that automatically evaluates the condylar seating based on CBCT images as a proof of concept.
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