Influences of patient positioning and general anesthesia on condylar position and surgical accuracy in orthognathic surgery.

Objectives: To assess the effect of patient positioning and general anesthesia on the condylar position in orthognathic surgery.

Materials And Methods: This prospective study included patients undergoing orthognathic surgery between 2019 and 2020. Four weeks prior to surgery (T0) cone-beam computed tomography (CBCT) scans and intra-oral scans (IOS) were acquired in an upright position.

Evaluating the Effectiveness of Transtibial Prosthetic Socket Shape Design Using Artificial Intelligence: A Clinical Comparison With Traditional Plaster Cast Socket Designs.

Objective: 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.

Automated condylar seating assessment using a deep learning-based three-step approach.

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.

Fully automated landmarking and facial segmentation on 3D photographs.

Three-dimensional facial stereophotogrammetry provides a detailed representation of craniofacial soft tissue without the use of ionizing radiation. While manual annotation of landmarks serves as the current gold standard for cephalometric analysis, it is a time-consuming process and is prone to human error. The aim in this study was to develop and evaluate an automated cephalometric annotation method using a deep learning-based approach.