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Application Of Deep Learning For Detecting Implants In Computed Tomography Scout Images With Multi-institution And Multi-vendor For Personal Identification.

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Application Of Deep Learning For Detecting Implants In Computed Tomography Scout Images With Multi-institution And Multi-vendor For Personal Identification.

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Application of deep learning for detecting implants in computed tomography scout images with multi-institution and

Yeji Kim¹, Yosuke Usumoto², Jin-Haeng Heo³

¹Department of Multidisciplinary Radiological Science, The Graduate School of Dongseo University, 47 Jurye-ro, Sasang-gu, Busan 47011, Republic of Korea.

Science & Justice : Journal of the Forensic Science Society

|September 10, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

Deep learning models can automatically detect metallic implants in CT scout images for forensic identification when traditional methods fail. This technology aids in identifying deceased individuals by locating medical devices.

Keywords:

Computed tomography Deep learning Metallic implants Multi-vendor Personal identification Post-mortem imaging

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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

Forensic Science
Medical Imaging
Artificial Intelligence

Background:

Accurate identification of deceased individuals is crucial in forensic investigations.
Primary identification methods (odontology, fingerprint, DNA) may be unavailable.
Implanted medical devices offer a supplementary identification resource.

Purpose of the Study:

To develop deep learning models for automatic metallic implant detection in CT scout images.
To evaluate model generalizability across diverse, multi-institutional, and multi-vendor datasets.
To enhance forensic identification capabilities using medical device markers.

Main Methods:

Utilized computed tomography (CT) scout images.
Trained and evaluated two object detection models: RetinaNet and Faster R-CNN.
Employed a multi-institutional and multi-vendor dataset to ensure robust performance.

Main Results:

Achieved improved performance in detecting various metallic implants.
Significantly reduced false positive rates in implant detection.
Demonstrated enhanced classification consistency across different imaging conditions.

Conclusions:

CT scout image analysis is a practical tool for forensic identification.
Deep learning models efficiently detect implanted medical devices for identification support.
This approach strengthens identification methods when primary techniques are not feasible.