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Recent publications that use FaceBase data | FaceBase

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Recent publications that use FaceBase data

Published 12 December 2023

Privacy, bias and the clinical use of facial recognition technology: A survey of genetics professionals

Authors: Elias Aboujaoude, Janice Light, Julia E. H. Brown, W. John Boscardin, Benedikt Hallgrimsson, Ophir D. Klein

Journal: American Journal of Medical Genetics Part C: Seminars in Medical Genetics


Summary: Facial recognition technology (FRT) has been adopted as a precision medicine tool. The medical genetics field highlights both the clinical potential and privacy risks of this technology, putting the discipline at the forefront of a new digital privacy debate. Investigating how geneticists perceive the privacy concerns surrounding FRT can help shape the evolution and regulation of the field, and provide lessons for medicine and research more broadly. Five hundred and sixty‐two genetics clinicians and researchers were approached to fill out a survey, 105 responded, and 80% of these completed. The survey consisted of 48 questions covering demographics, relationship to new technologies, views on privacy, views on FRT, and views on regulation. Genetics professionals generally placed a high value on privacy, although specific views differed, were context‐specific, and covaried with demographic factors. Most respondents (88%) agreed that privacy is a basic human right, but only 37% placed greater weight on it than other values such as freedom of speech. Most respondents (80%) supported FRT use in genetics, but not necessarily for broader clinical use. A sizeable percentage (39%) were unaware of FRT’s lower accuracy rates in marginalized communities and of the mental health effects of privacy violations (62%), but most (76% and 75%, respectively) expressed concern when informed. Overall, women and those who self‐identified as politically progressive were more concerned about the lower accuracy rates in marginalized groups (88% vs. 64% and 83% vs. 63%, respectively). Younger geneticists were more wary than older geneticists about using FRT in genetics (28% compared to 56% “strongly” supported such use). There was an overall preference for more regulation, but respondents had low confidence in governments’ or technology companies’ ability to accomplish this. Privacy views are nuanced and context‐dependent. Support for privacy was high but not absolute, and clear deficits existed in awareness of crucial FRT‐related discrimination potential and mental health impacts. Education and professional guidelines may help to evolve views and practices within the field.

Comparing 2D and 3D representations for face-based genetic syndrome diagnosis

Authors: Jordan J. Bannister, Matthias Wilms, J. David Aponte, David C. Katz, Ophir D. Klein, Francois P. Bernier, Richard A. Spritz, Benedikt Hallgrímsson & Nils D. Forkert

Journal: European Journal of Human Genetics


Summary: Human genetic syndromes are often challenging to diagnose clinically. Facial phenotype is a key diagnostic indicator for hundreds of genetic syndromes and computer-assisted facial phenotyping is a promising approach to assist diagnosis. Most previous approaches to automated face-based syndrome diagnosis have analyzed different datasets of either 2D images or surface mesh-based 3D facial representations, making direct comparisons of performance challenging. In this work, we developed a set of subject-matched 2D and 3D facial representations, which we then analyzed with the aim of comparing the performance of 2D and 3D image-based approaches to computer-assisted syndrome diagnosis. This work represents the most comprehensive subject-matched analyses to date on this topic. In our analyses of 1907 subject faces representing 43 different genetic syndromes, 3D surface-based syndrome classification models significantly outperformed 2D image-based models trained and evaluated on the same subject faces. These results suggest that the clinical adoption of 3D facial scanning technology and continued collection of syndromic 3D facial scan data may substantially improve face-based syndrome diagnosis.