Magnetic resonance imaging for multi-factorial age estimation using Bayes' rule: a validation study in two independent samples.

OBJECTIVES

Multi-factorial age estimation (MFA) models have been developed based on Bayes' rule, combining MRI data of the third molars (T), left wrist (W) and/or both clavicles (C). Internal cross-validated performance was reported, but external validation is needed before bringing the approach into practice. This study aimed to validate these MFA models in two independent samples.

METHODS

In the Ghent sample, W + C MRI was prospectively conducted in 108 healthy Caucasian volunteers (52 males, 56 females) aged 16 to 21 years. In the Graz sample, T + W + C MRI was prospectively conducted in 335 healthy Caucasian males aged 13 to 24 years. Development was staged and checked for intra-observer reliability, and age estimation performances were tested.

RESULTS

Staging clavicles was most prone to intra-observer variability. Applying the W + C model to Ghent males rendered a mean absolute error of 1.55 years, a root mean square error of 1.90 years, 70.6% correctly categorised adults and 94.4% correctly categorised minors. In females, the results were 1.49 years, 1.83 years, 92.1% and 66.7%, respectively. Regarding the Graz sample, the W + C results were 1.66 years, 2.08 years, 94.7% and 80.7%, respectively. For the T + W + C model, the results were 1.41 years, 1.80 years, 95.2% and 81.5%, respectively.

CONCLUSION

The T + W + C and W + C models rendered a similar accuracy of the point prediction of age in both validation samples. However, they bared a larger risk of wrongfully categorising a minor as an adult than reported for internal validation, stressing the importance of the prediction interval for age estimation in practice.