Human root and canal number and morphology are highly variable, and internal root canal form and count does not necessarily co-vary directly with external morphology. While several typologies and classifications have been developed to address individual components of teeth, there is a need for a comprehensive system, that captures internal and external root features across all teeth. Using CT scans, the external and internal root morphologies of a global sample of humans are analysed (n = 945). From this analysis a method of classification that captures external and internal root morphology in a way that is intuitive, reproducible, and defines the human phenotypic set is developed. Results provide a robust definition of modern human tooth root phenotypic diversity. The method is modular in nature, allowing for incorporation of past and future classification systems. Additionally, it provides a basis for analysing hominin root morphology in evolutionary, ecological, genetic, and developmental contexts.

Descriptive morphology of tooth roots traditionally focuses on number of canals and roots. However, how or if canal and root number are related is poorly understood. While it is often assumed that canal number is concomitant with root number and morphology, in practice canal number and morphology do not always covary with external root features. To investigate the relationship between canal and root number, fully developed, adult post-canine teeth were examined and quantified from medical computerized tomography scans from a global sample of 945 modern humans. We tested the hypotheses that root and canal number do not follow a 1:1 ratio, that canal to root ratios differ between teeth, and that canal to root ratios differ across populations. Results indicate that not only is root number dependent on canal number, but that this relationship become more variable as canal number increases, varies both between individual teeth and by population, and changes as populations increase in distance from Sub-Saharan Africa. These results show that the ratio of canal number to root number is an important indicator of variation in dental phenotypes.

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Archaeologists have long used stone tools (lithics) to reconstruct the behaviour of prehistoric hominins. While techniques have become more quantitative, there still remain barriers to optimizing data retrieval (Andrefsky,William, 2012). Machine learning and computer vision approaches can be developed to extract quantitative and trait data from lithics, photographs and drawings. PyLithics has been developed to capture data from 2D line drawings, focusing on the size, shape and technological attributes of flakes. The problems addressed in the software are: one, capturing data in a form that can be quantified, and information maximized; two, solving the challenges of data that is not a simple linear sequence of bases but complex 3D objects or 2D image representations; and three, transforming and exporting these into systematic data for analysis. The goal is to enhance the size and quality of lithic databases for analysing ancient technology and human behaviour.