Quantifying the potential of morphological parameters for human dental identification: part 1-proof of concept

Abstract

In forensic identification, lack of eccentric characteristics of intact dentitions hinders correct ante-mortem/post-mortem (AM/PM) matching. It remains unclear which morphological dental parameters hold strong potential as identifiers. This study aimed to establish a method to quantify and rank the identifying potential of one (or a combination of) continuous morphological parameter(s), and to provide a proof of concept. First, a statistic was defined that quantifies the identifying potential: the mean potential set (MPS). The MPS is derived from inter-observer agreement data and it indicates the percentage of subjects in the AM reference dataset who at least need to be considered to detect the correct PM subject. This was calculated in a univariate and a multivariate setting. Second, the method was validated on maxillary first molar crowns of 82 3D-digitally scanned cast models. Standardized measurements were registered using 3D modeling software (3-Matic Medical 12.0, Materialise N.V., Leuven, Belgium): tooth depth, angles between cusps, distances between cusps, distances between the cusps, and the mesial pit. A random sample of 40 first molars was measured by a second examiner. Quantifying and ranking the parameters allowed selecting those with the strongest identifying potential. This was found for the tooth depth (1 measurement, MPS = 17.1%, ICC = 0.879) in the univariate setting, and the angles between cusps (4 measurements, MPS = 3.9%) in the multivariate setting. As expected, the multivariate approach held significantly stronger identifying potential, but more measurements were needed (i.e., more time-consuming). Our method allows quantifying and ranking the potential of dental morphological parameters as identifiers using a clear-cut statistic.