Radiology has demonstrated great utility for age estimation, but most of the studies are based on metrical and morphological methods in order to perform an identification profile. A simple image analysis-based method is presented, aimed to correlate the bony tissue ultrastructure with several variables obtained from the grey-level histogram (GLH) of computed tomography (CT) sagittal sections of the pubic symphysis surface and the pubic body, and relating them with age. The CT sample consisted of 169 hospital Digital Imaging and Communications in Medicine (DICOM) archives of known sex and age. The calculated multiple regression models showed a maximum R (2) of 0.533 for females and 0.726 for males, with a high intra- and inter-observer agreement. The method suggested is considered not only useful for performing an identification profile during virtopsy, but also for application in further studies in order to attach a quantitative correlation for tissue ultrastructure characteristics, without complex and expensive methods beyond image analysis.

Radiology has demonstrated great utility for sex determination, but most studies are based in metrical and morphological methods in order to perform an identification profile. It is presented an easy image analysis-based method aimed to demonstrate the relationship between the bony tissue ultrastructure and sex by using several grey level histogram variables obtained from computed tomography images using sagittal sections of the pubic symphysis surface and the body of the pubis. The CT sample consisted of 169 hospital DICOM archives of known sex and age. The multivariate binary logistic regression models calculated showed reliabilities for sex determination between 87.4 and 95.8 %, with a high intra and inter-observer agreement. The suggested method is not only considered useful to perform an identification profile during Virtopsy, but even for application in further studies in order to attach a quantitative correlation for tissue ultrastructure characteristics without complex and expensive methods beyond image analysis.

RESUMEN Los animales pueden actuar como predadores de los restos humanos y las lesiones postmortales que infligen en los cadáveres pueden dar lugar a errores de interpretación de los hallazgos en las autopsias forenses. Entre la fauna cadavérica, las hormigas constituyen un caso excepcional al ser los únicos insectos que pueden iniciar su actividad predadora antes de instaurarse la putrefacción. Las hormigas suelen producir lesiones que por su asiento y morfología resultan típicas. Se presenta un caso en el que se evidencia la presencia de lesiones de morfología típica por la acción lesiva postmortal de hormigas, pero con una extensión y localización inhabituales. Palabras clave: Hormigas; depredación postmortal por animales; piel; autopsia forense.

When investigating a death, post-mortem identification provides with results of great legal and humanitarian significance. The effectiveness of the methods used to estimate age depends on the reference population, considering variables such as sex and ancestry. The aim of this study was to validate the Iscan method to estimate age in a Spanish forensic population, comparing the estimates obtained in dry bones and 3D reconstructions created with a surface scanner. We carried out a cross-sectional study on 109 autopsied corpses (67% male), scanning the sternal end of the right fourth rib in a 3D mesh, using an EinScan-Pro® surface scanner (precision: 0.05 mm). Two observers estimated the phases in dry bones and 3D images according to the Iscan method and to the sex of the subject. The mean age was 57.73 years (SD = 19.12 years;18–93 years). The intra-observer agreement was almost perfect in bones (κ = 0.877–0.960) and 3D images (κ = 0.954), while the inter-observer agreement was almost perfect in bones (κ = 0.813) and substantial in 3D images (κ = 0.727). The correlation with the Iscan phases was very strong in bones (Rho = 0.794–0.820; p < 0.001) and strong in 3D images (Rho = 0.690–0.691; p < 0.001). Both sex-adjusted linear regression models were significant (dry bones: R2 = 0.65; SEE =  ± 11.264 years; 3D images: R2 = 0.50; SEE =  ± 13.537 years) from phase 4 onwards. An overestimation of age was observed in the first phases, and an underestimation in the later ones. Virtual analysis using a surface scanner in the fourth rib is a valid means of estimating age. However, the error values and confidence intervals were considerable, so the joint use of different methods and anatomical sites is recommended.