ArticleInternational journal of environmental research and public health2023
Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae.
Article in International journal of environmental research and public health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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3 citing papers in PubMed, 6 citations in OpenAlex.
- Performance of DRNNAGE for macroscopic age-at-death estimation in contemporary Brazilian identified skeletons.Journal of forensic sciences · 2026Article
- Development of an age estimation method for the coxal bone and lumbar vertebrae obtained from post-mortem computed tomography images using a convolutional neural network.International journal of legal medicine · 2026Article
- Evaluation of Convolutional Neural Network-Based Posture Identification Model of Older Adults: From Silhouette of Sagittal Photographs.Geriatrics (Basel, Switzerland) · 2025Article
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10 authors at 1 institution in 1 country.
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Abstract
Although age estimation upon death is important in the identification of unknown cadavers for forensic scientists, to the best of our knowledge, no study has examined the utility of deep neural network (DNN) models for age estimation among cadavers. We performed a postmortem computed tomography (CT) examination of 1000 and 500 male and female cadavers, respectively. These CT slices were converted into 3-dimensional images, and only the thoracolumbar region was extracted. Eighty percent of them were categorized as training datasets and the others as test datasets for both sexes. We fine-tuned the ResNet152 models using the training datasets. We conducted 4-fold cross-validation, and the mean absolute error (MAE) of the test datasets was calculated using the ensemble learning of four ResNet152 models. Consequently, the MAE of the male and female models was 7.25 and 7.16, respectively. Our study shows that DNN models can be useful tools in the field of forensic medicine.
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