Automatic rib fracture detection on postmortem CT data using deep learning.

Area of Science:

• Medical Imaging
• Artificial Intelligence
• Forensic Science

Background:

• Rib fracture detection is crucial but time-consuming in forensic investigations.
• Deep learning (DL) models can enhance radiologist performance in detecting rib fractures on clinical CT scans.

Purpose of the Study:

• Assess the performance of a DL model (nnDetection) for rib fracture detection on postmortem (PM) CT scans.
• Identify domain shift factors between clinical and PM CT imaging.

Main Methods:

• Trained two instances of the nnDetection model: nnDetPM on 50 PMCT scans and nnDetClin on 660 clinical CT scans.
• Evaluated model performance on a PMCT testing set.

Main Results:

• nnDetPM achieved 70.2% sensitivity and 78.1% precision on PMCT scans.
• nnDetClin performed poorly (19.8% sensitivity, 25.5% precision) on PMCT scans, highlighting domain shift.
• Arm position and medical ware were identified as key domain shift factors.

Conclusions:

• The nnDetPM model demonstrated notable performance in PMCT rib fracture detection, comparable to radiologists.
• Further research is needed to explore advanced techniques to overcome domain shift challenges for DL models in this application.