.png){kind=icon}
Return
Automatic rib fracture detection on postmortem CT data using deep learning
.png){kind=icon}
OA
AI
DOI：10.1007/s00414-025-03669-x.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
ObjectivesTo assess the performance of automatic rib fracture detection of an existing deep learning (DL) model, nnDetection, on a postmortem (PM) CT scan dataset, and to identify the main factors of domain shift between clinical and PM CT imaging.BackgroundRib fracture detection and classification in forensic investigations is a time-consuming yet crucial task that can contribute to determine the cause of death. DL models are a promising tool, as recent research shows that radiologists using DL models can detect rib fractures in clinical CT scans at higher sensitivity and in shorter time.MethodsA dataset of 50 PMCT scans (24% women; age: mean 61, range 19 - 96 years) was retrospectively collected and annotated, and used to train a first instance of the model, nnDetPM. Another instance of the model, nnDetClin, was trained on data from another dataset, RibFrac, consisting of 660 clinical CT scans (36% women; age: mean 55, range 21 - 94 years).ResultsOn the PM testing set, nnDetPM achieved an average sensitivity of 70.2% and an average precision (at 0.1 intersection over union) of 78.1%, whereas nnDetClin fell far behind at 19.8% average sensitivity and 25.5% average precision, indicating a substantial impact of the domain shift from clinical to PM CT data. Further inspection of the results showed that the main factors of this domain shift were the position of the arms and the presence of medical ware in the image acquisition area of the PMCT scans.ConclusionThe performance of nnDetPM, with an average sensitivity of 70.2%, was notable and comparable to that of radiologists. However, more advanced techniques must be explored to decide if DL models can overcome the domain shift factors.
Keywords:
Rib
Fracture
Detection
CT
Postmortem
Deep learning
Journal
I
IF:
2.3
Papers:
256
Citations:
6.5K
Organization
Cited Papers
How scan parameter choice affects deep learning-based coronary artery disease assessment from computed tomography.png)
SCIENTIFIC REPORTS
OAAI
SCIENTIFIC REPORTSIF3.9
OAAIShareSaveShareSaveDeep-learning-assisted detection and segmentation of rib fractures from CT scans: Development and validation of FracNetEBIOMEDICINEOAAI
EBIOMEDICINEIF10.8
OAAIShareSave
View more