Digital Reconstructions Using Linear Regression: How Well Can It Estimate Missing Shape Data from Small Damaged Areas?

Simple Summary Paleontologists, anthropologists and forensic scientists work with skeletal evidence that is often damaged or fragmented. Inferring what the original morphology of the bones was like is important for reconstructing fossils or identifying individuals. In this paper, we evaluate how accurate a statistical method (linear regression) is for estimating missing shape data. For this purpose, we worked with 3D models of complete human zygomatics (a face bone) that were altered to simulate damage, and reconstructed them using this method. We then evaluated how closely the original morphology resembled the reconstructed one. We conclude that this method can faithfully estimate the original anatomical data, especially when the damage is small, but the error increases significantly with increasing damage size. Skeletal remains analyzed by anthropologists, paleontologists and forensic scientists are usually found fragmented or incomplete. Accurate estimations of the original morphologies are a challenge for which several digital reconstruction methods have been proposed. In this study, the accuracy of reconstructing bones based on multiple linear regression (RM) was tested. A total of 150 digital models from complete zygomatics from recent past populations (European and African American) were studied using high-density geometric morphometrics. Some landmarks (i.e., 2, 3 and 6) were coded as missing to simulate incomplete zygomatics and the missing landmarks were estimated with RM. In the zygomatics, this simulated damage affects a few square centimeters or less. Finally, the predicted and original shape data were compared. The results indicate that the predicted landmark coordinates were significantly different from the original ones, although this difference was less than the difference between the original zygomatic and the mean zygomatic in the sample. The performance of the method was affected by the location and the number of missing landmarks, with decreasing accuracy with increasing damaged area. We conclude that RM can accurately estimate the original appearance of the zygomatics when the damage is small.

Automated summary

This study evaluates the accuracy of a statistical method for estimating missing shape data. The results indicate that the method can accurately estimate the original anatomical data, especially when the damage is small, but the error increases significantly with increasing damage size.