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Computed Tomography

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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Assessment of Bone Fracture Healing Using Micro-Computed Tomography

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Published on: December 9, 2022

Classification of rib fracture types from postmortem computed tomography images using deep learning.

Victor Ibanez¹, Dario Jucker², Lars C Ebert²

¹Forensic Machine Learning Technology Center, Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland.

Forensic Science, Medicine, and Pathology

|November 16, 2023

Summary

This study developed an AI model using deep learning to detect various fracture types in postmortem CT scans. The model shows promise in assisting forensic pathologists by improving diagnostic accuracy and efficiency.

Keywords:

Deep learning Forensic sciences Postmortem computed tomography Rib fracture classification

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Last Updated: Jul 11, 2025

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Published on: September 28, 2017

Area of Science:

Forensic Radiology
Artificial Intelligence in Medicine
Medical Image Analysis

Background:

Medical image diagnostics face resource limitations, challenging detailed analysis.
Artificial intelligence offers solutions to assist clinicians in medical image interpretation.
Developing automated systems for fracture detection in postmortem computed tomography (PMCT) is crucial.

Purpose of the Study:

To train a deep learning model for classifying diverse fracture types.
To detect fractures on 2D representations of volumetric PMCT data.
To evaluate model performance across different hierarchical taxonomic levels.

Main Methods:

Utilized a ResNet50 deep learning architecture pretrained on ImageNet.
Employed transfer learning to fine-tune the model for fracture detection.
Trained the model to differentiate between "displaced," "nondisplaced," and specific subtypes of displaced fractures.

Main Results:

High accuracy (95-99%) in predicting cases with no fractures.
Moderate accuracy for nondisplaced fractures (80-86%).
Variable accuracy for specific displaced fracture types, with "ad latus" showing lower prediction rates (17-18%) and others ranging from 64-75%.

Conclusions:

Deep learning models can reliably assist forensic pathologists in fracture detection.
Model performance is influenced by the hierarchical level of fracture classification.
AI offers a viable solution to reduce workload in forensic medical image analysis.