Automatic virtual reduction of unilateral zygomatic fractures based on ICP algorithm: A preliminary study.

Objective: To establish an automatic reduction method for unilateral zygomatic fractures based on Iterative Closes Point (ICP) algorithm.

Material And Methods: 60 patients with unilateral type B zygomatic fractures were included. After acquiring CT images, zygomatic fragments were segmented using self-developed software MICSys.

Is Convolutional Neural Network Accurate for Automatic Detection of Zygomatic Fractures on Computed Tomography?

Purpose: Zygomatic fractures involve complex anatomical structures of the mid-face and the diagnosis can be challenging and labor-consuming. This research aimed to evaluate the performance of an automatic algorithm for the detection of zygomatic fractures based on convolutional neural network (CNN) on spiral computed tomography (CT).

Materials And Methods: We designed a cross-sectional retrospective diagnostic trial study.

Detection and classification of mandibular fracture on CT scan using deep convolutional neural network.

Objectives: This study aimed to evaluate the accuracy and reliability of convolutional neural networks (CNNs) for the detection and classification of mandibular fracture on spiral computed tomography (CT).

Materials And Methods: Between January 2013 and July 2020, 686 patients with mandibular fractures who underwent CT scan were classified and annotated by three experienced maxillofacial surgeons serving as the ground truth. An algorithm including two convolutional neural networks (U-Net and ResNet) was trained, validated, and tested using 222, 56, and 408 CT scans, respectively.