Developing an automated skeletal phenotyping pipeline to leverage biobank-level medical imaging databases

Collecting skeletal measurements from medical imaging databases remains a tedious task, limiting the research utility of biobank-level data. Here we present an automated phenotyping pipeline for obtaining skeletal measurements from DXA scans and compare its performance to manually collected measurements.

A pipeline that extends the Advanced Normalization Tools (ANTs) framework was developed on 341 whole-body DXA scans of UK Biobank South Asian participants. A set of 10 measurements throughout the skeleton was automatically obtained via this process, and the performance of the method was tested on 20 additional DXA images by calculating percent error and concordance correlation coefficients (CCC) for manual and automated measurements. Stature was then regressed on the automated femoral and tibia lengths and compared to published stature regressions to further assess the reliability of the automated measurements.

Based on percent error and CCC, the performance of the automated measurements falls into three categories: poor (sacral and acetabular breadths), variable (trunk length, upper thoracic breadth, and innominate height), and high (maximum pelvic aperture breadth, bi-iliac breadth, femoral maximum length, and tibia length). Stature regression plots indicate that the automated measurements reflect realistic body proportions and appear consistent with published data reflecting these relationships in South Asian populations.

Based on the performance of this pipeline, a subset of measurements can be reliably extracted from DXA scans, greatly expanding the utility of biobank-level data for biological anthropologists and medical researchers.