ReviewForensic science, medicine, and pathology2020
Potential use of deep learning techniques for postmortem imaging.
Review in Forensic science, medicine, and pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep learning for acute rib fracture detection in CT data: a systematic review and meta-analysis.The British journal of radiology · 2024Pooled it
- Artificial intelligence based on forensic medical databases: construction, application, and prospects.Forensic science, medicine, and pathology · 2026Review
- Evaluation Framework for Bruise Detection: Systematic ALS/White-Light Training and Skin-Tone Balancing with Deep Learning.Sensors (Basel, Switzerland) · 2026Article
- Multi-dimensional deep learning-based segmentation and volumetric assessment of sphenoid sinus fluid on postmortem CT in drowning cases.Scientific reports · 2026Article
- Analysis of Forensic and Drowning Death Studies Using VOSviewer: A Bibliometric Study.Acta medica Philippina · 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
- Postmortem Temporal Changes in Liver and Spleen Stiffness: Evaluation with Shear Wave Elastography in a Rat Model.Diagnostics (Basel, Switzerland) · 2025Article
- Post-mortem Cardiac MRI in Sudden Cardiac Death: The Interesting Intertwining of Radiology and Histology to Diagnose Arrhythmic Death or Myocardial Infarction.Current medical imaging · 2025Article
- Diagnostic Challenges in Uncommon Firearm Injury Cases: A Multidisciplinary Approach.Diagnostics (Basel, Switzerland) · 2024Article
- Automated detection of fatal cerebral haemorrhage in postmortem CT data.International journal of legal medicine · 2024Article
- Raman spectroscopy for postmortem interval estimation of human skeletal remains: A scoping review.Journal of biophotonics · 2023Article
- Overcoming Fear, Uncertainty, and Doubt: Artificial Intelligence (AI) and the Value of Trust.Cureus · 2023Article
- Review
- Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae.International journal of environmental research and public health · 2023Article
- Application of postmortem imaging modalities in cases of sudden death due to cardiovascular diseases-current achievements and limitations from a pathology perspective : Endorsed by the Association for European Cardiovascular Pathology and by the International Society of Forensic Radiology and Imaging.Virchows Archiv : an international journal of pathology · 2023Article
- RiFNet: Automated rib fracture detection in postmortem computed tomography.Forensic science, medicine, and pathology · 2022Article
- Personal identification with artificial intelligence under COVID-19 crisis: a scoping review.Systematic reviews · 2022Article
- Potential use of deep learning techniques for postmortem imaging-moving beyond postmortem radiology.Forensic science, medicine, and pathology · 2021Article
- An algorithm for automatically generating gas, bone and foreign body visualizations from postmortem computed tomography data.Forensic science, medicine, and pathology · 2021Article
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Authors and funding
8 authors.
Funding
Abstract
The use of postmortem computed tomography in forensic medicine, in addition to conventional autopsy, is now a standard procedure in several countries. However, the large number of cases, the large amount of data, and the lack of postmortem radiology experts have pushed researchers to develop solutions that are able to automate diagnosis by applying deep learning techniques to postmortem computed tomography images. While deep learning techniques require a good understanding of image analysis and mathematical optimization, the goal of this review was to provide to the community of postmortem radiology experts the key concepts needed to assess the potential of such techniques and how they could impact their work.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.