Evidence map›Paper›PMID 32991003›Full record

ArticleJournal of forensic sciences2021

Bruise dating using deep learning.

Jhonatan Tirado, David Mauricio

Abstract read
In one paragraph

Article in Journal of forensic sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

The trial behind it

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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Age prediction of hematoma using hyperspectral imaging (HSI).Forensic science, medicine, and pathology · 2025
    Article
  6. Article
  7. Article
  8. Article
  9. Model Quality in AI-based Bruise Detection: Rethinking IoU and Confidence Thresholds.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024
    Article
  10. Article
  11. Article
  12. Bruise dating using deep learning.Journal of forensic sciences · 2021
    Article
  13. Trauma Forensics in Blunt and Sharp Force Injuries.Journal of the West African College of Surgeons
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Jhonatan TiradoDepartment of Systems Engineering, Universidad Nacional Mayor de San Marcos, Lima, Peru.ORCID https://orcid.org/0000-0003-4482-4610
David MauricioDepartment of Systems Engineering, Universidad Nacional Mayor de San Marcos, Lima, Peru.ORCID https://orcid.org/0000-0001-9262-626X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The bruise dating can have important medicolegal implications in family violence and violence against women cases. However, studies show that the medical specialist has 50% accuracy in classifying a bruise by age, mainly due to the variability of the images and the color of the bruise. This research proposes a model, based on deep convolutional neural networks, for bruise dating using only images, by age ranges, ranging from 0-2 days to 17-30 days, and images of healthy skin. A 2140 experimental bruise photograph dataset was constructed, for which a data capture protocol and a preprocessing procedure are proposed. Similarly, 20 classification models were trained with the Inception V3, Resnet50, MobileNet, and MnasNet architectures, where combinations of learning transfer, cross-validation, and data augmentation were used. Numerical experiments show that classification models based on MnasNet have better results, reaching 97.00% precision and sensitivity, and 99.50% specificity, exceeding 40% precision reported in the literature. Also, it was observed that the precision of the model decreases with the age of the bruise.

Indexed as

Deep LearningNeural Networks, ComputerAdultAgedContusionsDatasets as TopicFemaleForensic MedicineHumansImage Processing, Computer-AssistedMalePhotographySensitivity and SpecificityTime FactorsYoung Adultbruise datingconvolutional neural networkdeep learningMasNet

Identifiers

PMID32991003
PMCPMC7821214

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Registered trials

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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.