The accurate age at death assessment of unidentified adult skeletal individuals is a critical research task in forensic anthropology, being a key feature for the determination of biological profiles of individual skeletal remains. We have previously shown that the age-related decrease of bone mineral density (BMD) in the proximal femur could be used to assess age at death in women (Navega et al., 2018). The present study aims to generate models for age estimation in both sexes through bone densitometry of the femur and radiogrammetry of the second metacarpal. The training sample comprised 224 adults (120 females, 104 males) from the «Coimbra Identified Skeletal Collection», and different models were generated through least squares regression and general regression neural networks (GRNN). The models were operationalized in a user-friendly online interface at osteomics.com/DXAGE2/. The mean absolute difference between the known and estimated age at death ranges from 9.39 to 13.18 years among women, and from 10.33 to 15.76 among men with the least squares regression models. For the GRNN models, the mean absolute difference between documented and projected age ranges from 8.44 to 12.58 years in women; and from 10.56 to 16.18 years in men. DXAGE 2.0 enables age estimation in incomplete and / or fragmentary skeletal remains, using alternative skeletal regions, with reliable results

**Key-words:**
dual x-ray absorptiometry; radiogrammetry; biological profile; forensic anthropology; bioarcheology

Francisco Curate, David Navega, Eugénia Cunha, João d'Oliveira Coelho

**MAE**
- mean absolute error;
**RMAE**
- relative mean absolute error;
**MAPE**
- mean absolute percentage error;
**RMSE**
- root mean squared error;
**RRMSE**
- relative root mean squared error;
**RSquared**
- R-Squared;
**AdjRSquared**
- Adjusted R-Squared;
**PIWidth**
- Predicted Interval Mean Width;
**%C**
- Coverage.

Use the map of data points above to see if the values you inserted make sense. If your measurements fall outside the range of points in our database, it is likely that the results provided by the least square models will be suboptimal, or even wrong.

**MAE**
- mean absolute error;
**RMAE**
- relative mean absolute error;
**MAPE**
- mean absolute percentage error;
**RMSE**
- root mean squared error;
**RRMSE**
- relative root mean squared error;
**RSquared**
- R-Squared;
**AdjRSquared**
- Adjusted R-Squared;