Title |
3-Dimensional Facial Analysis—Facing Precision Public Health
|
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Published in |
Frontiers in Public Health, April 2017
|
DOI | 10.3389/fpubh.2017.00031 |
Pubmed ID | |
Authors |
Gareth Baynam, Alicia Bauskis, Nicholas Pachter, Lyn Schofield, Hedwig Verhoef, Richard L. Palmer, Stefanie Kung, Petra Helmholz, Michael Ridout, Caroline E. Walker, Anne Hawkins, Jack Goldblatt, Tarun S. Weeramanthri, Hugh J. S. Dawkins, Caron M. Molster |
Abstract |
Precision public health is a new field driven by technological advances that enable more precise descriptions and analyses of individuals and population groups, with a view to improving the overall health of populations. This promises to lead to more precise clinical and public health practices, across the continuum of prevention, screening, diagnosis, and treatment. A phenotype is the set of observable characteristics of an individual resulting from the interaction of a genotype with the environment. Precision (deep) phenotyping applies innovative technologies to exhaustively and more precisely examine the discrete components of a phenotype and goes beyond the information usually included in medical charts. This form of phenotyping is a critical component of more precise diagnostic capability and 3-dimensional facial analysis (3DFA) is a key technological enabler in this domain. In this paper, we examine the potential of 3DFA as a public health tool, by viewing it against the 10 essential public health services of the "public health wheel," developed by the US Centers for Disease Control. This provides an illustrative framework to gage current and emergent applications of genomic technologies for implementing precision public health. |
X Demographics
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Switzerland | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 48 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Doctoral Student | 8 | 17% |
Student > Master | 5 | 10% |
Other | 4 | 8% |
Researcher | 4 | 8% |
Student > Bachelor | 4 | 8% |
Other | 12 | 25% |
Unknown | 11 | 23% |
Readers by discipline | Count | As % |
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Medicine and Dentistry | 10 | 21% |
Biochemistry, Genetics and Molecular Biology | 4 | 8% |
Computer Science | 4 | 8% |
Agricultural and Biological Sciences | 3 | 6% |
Psychology | 3 | 6% |
Other | 11 | 23% |
Unknown | 13 | 27% |