ArticleInternational journal of legal medicine2021
Deep learning in forensic gunshot wound interpretation-a proof-of-concept study.
Article in International journal of legal medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- AI-Assisted Forensic Analysis of Hanging-Related Ligature Marks: A Pilot Study Using Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2026Article
- Evaluation Framework for Bruise Detection: Systematic ALS/White-Light Training and Skin-Tone Balancing with Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- The impact of forensic experience on postmortem CT interpretation in firearm deaths: an interobserver reliability study.La Radiologia medica · 2026Observational
- Automatic measuring of coronary atherosclerosis from medicolegal autopsy photographs based on deep learning techniques.Forensic science, medicine, and pathology · 2025Article
- Evaluating bias in forensic evidence: From expert analysis to AI-based decision tools.Forensic science international. Synergy · 2025Article
- From Description to Diagnostics: Assessing AI's Capabilities in Forensic Gunshot Wound Classification.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Deep learning-based human gunshot wounds classification.International journal of legal medicine · 2025Article
- The application of artificial intelligence in forensic pathology: a systematic literature review.Frontiers in medicine · 2025Review
- Deep learning and firearm wound classification: a pilot study.Frontiers in medicine · 2025Article
- Artificial intelligence for human gunshot wound classification.Journal of pathology informatics · 2024Article
- Automated wound segmentation and classification of seven common injuries in forensic medicine.Forensic science, medicine, and pathology · 2024Article
- Artificial intelligence in the practice of forensic medicine: a scoping review.International journal of legal medicine · 2024Article
- Interpol review of forensic firearm examination 2019-2022.Forensic science international. Synergy · 2023Review
- Applications of Neural Networks in Biomedical Data Analysis.Biomedicines · 2022Review
- Post-mortem computed tomography in forensic shooting distance estimation: a porcine cadaver study.BMC research notes · 2022Article
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Authors and funding
3 authors.
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Abstract
While the applications of deep learning are considered revolutionary within several medical specialties, forensic applications have been scarce despite the visual nature of the field. For example, a forensic pathologist may benefit from deep learning-based tools in gunshot wound interpretation. This proof-of-concept study aimed to test the hypothesis that trained neural network architectures have potential to predict shooting distance class on the basis of a simple photograph of the gunshot wound. A dataset of 204 gunshot wound images (60 negative controls, 50 contact shots, 49 close-range shots, and 45 distant shots) was constructed on the basis of nineteen piglet carcasses fired with a .22 Long Rifle pistol. The dataset was used to train, validate, and test the ability of neural net architectures to correctly classify images on the basis of shooting distance. Deep learning was performed using the AIDeveloper open-source software. Of the explored neural network architectures, a trained multilayer perceptron based model (MLP_24_16_24) reached the highest testing accuracy of 98%. Of the testing set, the trained model was able to correctly classify all negative controls, contact shots, and close-range shots, whereas one distant shot was misclassified. Our study clearly demonstrated that in the future, forensic pathologists may benefit from deep learning-based tools in gunshot wound interpretation. With these data, we seek to provide an initial impetus for larger-scale research on deep learning approaches in forensic wound interpretation.
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