Estimating the age at death for forensic cases using quantitative computed tomography

Kondou, Hiroki; Bandou, Risa; Idota, Hiroaki Ichioka Nozomi; Matsunari, Ryota; Kawamoto, Masataka; Ikegaya, Hiroshi

doi:10.1016/j.forsciint.2022.111367

Cited by 1 publication

(1 citation statement)

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“…VP Grade 2 implies that the putrefied gas occupies at least 1/3 but no more than half of the vertebral body. Finally, VP Grade 3 implies that the gas occupies more than half of the vertebral body [ 14 ]. In this study, we estimated the VP grade from the 1st thoracic vertebrae to 5th lumbar vertebrae, and we set the highest VP grade as the VP grade of the cadaver.…”

Section: Methodsmentioning

confidence: 99%

Deep Neural Networks-Based Age Estimation of Cadavers Using CT Imaging of Vertebrae

Kondou

Morohashi

Ichioka

et al. 2023

IJERPH

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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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Section: Methodsmentioning

confidence: 99%