A Review on Automatic Personal Identification Using Panoramic Radiographs and Computed Tomography.

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Abstract

Identifying completely unknown individuals is a major challenge in forensic and emergency medicine. Radiology offers a promising solution by using unique anatomical features on medical images to identify both living and deceased persons. Although emergency or postmortem images could be matched against large clinical databases, such applications remain largely experimental. This review examines current methods in automatic radiology-based personal identification, evaluates their performance, and highlights potential applications in forensic and clinical settings.A narrative review of studies published from 2018 onwards was conducted using PubMed and Google Scholar. Included studies applied automated or semi-automated personal identification to panoramic radiographs (PR) or computed tomography (CT) using reference datasets. A narrative approach was used to synthesize results descriptively due to heterogeneity in study design, dataset size, and methodology.Of the 32 included studies, 15 focused on PR-to-PR, 8 on head CT-to-CT, 7 on body CT-to-CT, and 2 on CT-to-PR identification. The most commonly applied approach was descriptor-based computer vision (CV), used in 9 studies. Deep learning was applied in 8 studies for feature extraction, and in 2 studies each for classification and bone segmentation.Several methods perform well in controlled settings. Descriptor-based CV provides the most flexibility and strongest evidence, especially for large-database comparisons and postmortem applications. Deep learning approaches, including feature extraction, classification, and automatic bone segmentation, also show promise for cross-individual matching but require further validation. Automatic radiology-based personal identification holds significant potential for forensic and clinical use, yet the development of standardized large-scale reference databases and robust automated pipelines remains a key challenge. · Radiological images enable automated personal identification of unknown individuals.. · Descriptor-based computer vision is flexible and robust for large database matching.. · Deep learning shows promise for cross-individual matching, but requires further validation.. · Postmortem applications are feasible, yet under-investigated.. · Ethical frameworks are necessary for handling sensitive imaging data.. · Heinrich A. A Review on Automatic Personal Identification Using Panoramic Radiographs and Computed Tomography. Rofo 2026; DOI 10.1055/a-2808-8851.

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