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Related Experiment Video

Updated: Feb 19, 2026

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Deep learning and firearm wound classification: a pilot study.

Giuseppe Delogu1, Nicola Di Fazio2, Gabriele Licciardello3

  • 1Department of Anatomical, Histological, Forensic and Orthopedic Science, Sapienza University, Rome, Italy.

Frontiers in Medicine
|February 18, 2026
PubMed
Summary
This summary is machine-generated.

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Deep learning shows promise in forensic pathology for classifying gunshot wounds (GSW), potentially exceeding human accuracy in pattern recognition. Further multicenter research is recommended to develop a robust artificial intelligence model for forensic applications.

Area of Science:

  • Forensic pathology
  • Artificial intelligence
  • Medical imaging analysis

Background:

  • Deep learning (DL) applications in forensic medicine are emerging, particularly for visual analysis tasks.
  • Previous studies demonstrated AI potential in predicting shooting distance from GSW images with high accuracy.
  • Forensic pathology can benefit from AI tools to enhance visual analysis of evidence.

Purpose of the Study:

  • To explore the application of deep learning techniques for classifying gunshot wounds (GSW).
  • To evaluate the performance of AI in GSW pattern recognition compared to existing benchmarks.
  • To assess the feasibility of AI in forensic pathology for wound analysis.

Main Methods:

  • Utilized Lobe AI software for a 4-phase study: training, validation, testing, and data analysis.
Keywords:
artificial intelligencedeep learningdiagnosisfirearmforensicsgunshot woundpathologypattern recognition

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  • Classified GSWs into four categories: GSWs, entrance/exit wounds, range of fire, and ammunition type.
  • Trained the AI model using images from a forensic atlas and tested with case history photos and intact skin controls.

    • Main Results:

    • Observed encouraging data, with numerous parameters exceeding human performance thresholds.
    • Achieved high predictive values in specific areas, surpassing previous scientific evidence.
    • AI demonstrated strong potential in classifying GSW characteristics.

    Conclusions:

    • Limited data availability was an intrinsic limitation, necessitating further algorithm development.
    • The study highlights the potential of AI in forensic pathology, with strengths in category analysis and control usage.
    • Future multicenter research with larger sample sizes is crucial for developing a generalizable forensic AI model.