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Identifying Fatal Head Injuries on Postmortem Computed Tomography Using Convolutional Neural Network/Deep Learning: A

Jack Garland¹, Benjamin Ondruschka², Simon Stables³

¹Forensic and Analytical Science Service, 480 Weeroona Rd, Lidcombe, NSW, 2141, Australia.

Journal of Forensic Sciences

|July 9, 2020

Summary

This summary is machine-generated.

Artificial intelligence (AI) shows promise in analyzing postmortem CT scans to detect fatal head injuries. This deep learning approach achieved 70-92.5% accuracy, aiding forensic pathology diagnostics.

Keywords:

SAH autopsy convoluted neural network deep learning forensic radiology head injuries postmortem computed tomography subarachnoid hemorrhage traumatic brain injury

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Published on: September 27, 2020

Area of Science:

Forensic pathology
Medical imaging
Artificial intelligence

Background:

Postmortem computed tomography (PMCT) is an emerging tool in forensic pathology.
Artificial intelligence (AI) is increasingly explored for medical imaging applications, including diagnostics.

Purpose of the Study:

To investigate the feasibility of using a convolutional neural network (CNN), a type of deep learning AI, to differentiate fatal head injuries from controls using PMCT head imaging.

Main Methods:

A CNN was developed using Keras and trained on PMCT head images from 25 fatal head injury cases and 25 controls.
The dataset was divided into training and testing sets for model evaluation.

Main Results:

The CNN achieved an accuracy of 70% to 92.5% in differentiating head injuries.
Challenges were noted in identifying subarachnoid hemorrhage and distinguishing congested vessels/prominent falx from actual injury.

Conclusions:

AI, specifically deep learning CNNs, demonstrates potential as a screening tool or for computer-assisted diagnostics in forensic analysis of PMCT head scans.
Further development is needed to address current limitations for improved accuracy in identifying specific injury indicators.