@article{SUN2026, 
author = {Yaning SUN and Xiaoying YU and Yanliang SHENG and Lei WAN and Wentao XIA},
title = {Classification of Rib Fracture Formation Time Using Deep Learning Models Based on 2.5D Technology},
year = {2026},
journal = {Chinese Journal of Forensic Sciences},
volume = {2026},
number = {2},
pages = {46-52},
keywords = {forensic clinical medicine, rib fracture, injury formation time, deep learning, CT},
url = {https://www.sciopen.com/article/10.3969/j.issn.1671-2072.2026.02.006},
doi = {10.3969/j.issn.1671-2072.2026.02.006},
abstract = {ObjectiveBased on 2.5D deep learning technology, chest computed tomography (CT) images are used to assist forensic estimation of the time interval of rib fracture injury formation.MethodsA total of 331 patients with rib fractures accepted by the Academy of Forensic Science between 2017 and 2024 were enrolled, and 1 290 CT images of rib fractures obtained from the day of injury to 90 days post-injury were included in the dataset. Among them, 464 fractures had an injury formation time ≤21 days and 826 had an injury formation time > 21-90 days. The dataset was divided into training, validation, and test sets at an approximately ratio of 7∶2∶1. After ex-tracting rib fracture regions of interest (ROI) using a segmentation model, the slice with the largest area was taken as the center. Two adjacent slices anterior and posterior to this reference were selected to form a five-slice stack. These slices were merged into a five-channel ResNet-101 deep learning model. The model's performance on the classification task of rib fracture injury formation time interval was evaluated using indicators including area under the curve, accuracy.ResultsIn the test set, the model achieved an area under the curve of 0.88, an accuracy of 81.54%.ConclusionThe deep learning model based on 2.5D technology demonstrates good application potential in determining the time of rib fracture injury formation, effectively integrating multi-slice feature information, and can provide an objective and quantitative method for forensic clinical appraisal.}
}