Virtual anthropology databases provide substantial benefits for research, offering large-scale, diverse samples that facilitate methodological advancements in research and allow for testing of theoretical models that may otherwise not be possible. As technology evolves, it is crucial to develop methods that can effectively be utilized in new forms of imaging and visualization. Here, we introduce a craniometric data collection module within the medical imaging software, Amira, developed to enhance, improve, and expedite large-scale data collection in virtual anthropology. This module's capabilities include 3D visualization of computed tomography (CT) scans and AI-driven assistance in landmark plotting. An output file contains x, y, and z coordinate data of all plotted landmarks and interlandmark distances. Additionally, researchers can plot floating and endocranial landmarks, enhancing the flexibility and comprehensiveness of data collection. A key advantage of this module is its complete customizability. Researchers can tailor the module to fit their objectives. The module's efficiency represents a marked improvement over previous virtual craniometrics data collection methods. In its current form, the module allows researchers to load input files, register the sample skull, and plot 104 cranial landmarks (both ectocranial and endocranial) in approximately 40 min. This is considerably faster than prior approaches. The module demonstrates the potential of leveraging AI and CT scans for the advancement of biological and forensic anthropology. Its integration with Amira's powerful tools provides researchers with a new and valuable resource that sets a new standard for data collection in virtual anthropology. (Publisher abstract provided.)
A Newly Developed AI-Assisted Tool for the Collection of Cranial Landmark Data
NCJ Number
310759
Journal
American Journal of Biological Anthropology Volume: 187 Issue: 4 Dated: 2025
Date Published
August 2025
Abstract
Date Published: August 1, 2025