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Updated: Dec 7, 2025

Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans

Published on: September 27, 2020

Potential use of deep learning techniques for postmortem imaging.

Akos Dobay^1,2, Jonathan Ford³, Summer Decker³

¹Zurich Institute of Forensic Medicine, University of Zurich, Winterthurerstrasse 190/52, CH-8057, Zurich, Switzerland. akos.dobay@uzh.ch.

Forensic Science, Medicine, and Pathology

|September 29, 2020

Summary

This summary is machine-generated.

Deep learning can automate diagnoses from postmortem CT scans, aiding forensic medicine. This review explains key concepts for experts to evaluate AI

Keywords:

Computed tomography Convolutional neural networks Deep learning Forensic sciences PMCT

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Last Updated: Dec 7, 2025

Image Rendering Techniques in Postmortem Computed Tomography: Evaluation of Biological Health and Profile in Stranded Cetaceans

Published on: September 27, 2020

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Published on: May 19, 2015

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Published on: April 28, 2022

Area of Science:

Forensic Radiology
Medical Imaging
Artificial Intelligence

Background:

Postmortem computed tomography (PMCT) is a standard forensic tool alongside conventional autopsy.
Increasing case numbers and data volume necessitate efficient diagnostic solutions.
A shortage of postmortem radiology experts presents a bottleneck in PMCT interpretation.

Purpose of the Study:

To introduce forensic radiology experts to deep learning (DL) concepts.
To enable experts to assess the potential impact of DL on postmortem CT analysis.
To bridge the knowledge gap between DL techniques and practical forensic radiology applications.

Main Methods:

Review of deep learning methodologies applied to medical image analysis.
Discussion of image analysis and mathematical optimization principles relevant to DL.
Focus on the application of DL to postmortem computed tomography datasets.

Main Results:

Deep learning offers potential for automating diagnostic tasks in postmortem radiology.
Understanding DL fundamentals is crucial for evaluating its utility in forensic medicine.
AI-driven solutions can address challenges of large datasets and expert shortages.

Conclusions:

Deep learning techniques show promise for enhancing postmortem CT interpretation.
This review equips forensic radiologists with foundational knowledge to explore AI integration.
AI has the potential to significantly impact the efficiency and accuracy of forensic diagnoses.