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Diagnostically relevant facial gestalt information from ordinary photos.

Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP, Zisserman A, Nellåker C - Elife (2014)

Bottom Line: The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses.Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders.Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering Science, University of Oxford, Oxford, United Kingdom Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.

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Distortion graphs representing the characteristic deformation of syndrome faces relative to the average control face.Each line reflects whether the distance is extended or contracted compared with the control face. White—the distance is similar to controls, blue—shorter relative to controls, and red—extended in patients relative to controls.DOI:http://dx.doi.org/10.7554/eLife.02020.009
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fig2s1: Distortion graphs representing the characteristic deformation of syndrome faces relative to the average control face.Each line reflects whether the distance is extended or contracted compared with the control face. White—the distance is similar to controls, blue—shorter relative to controls, and red—extended in patients relative to controls.DOI:http://dx.doi.org/10.7554/eLife.02020.009

Mentions: We used an Active Appearance Model ('Materials and methods') to calculate an average face within any set of images, representing consistent shape and appearance features within the group (Figure 1B and animated morphs in Figure 2). The average faces for each syndrome show that the algorithm effectively captures characteristic features of dysmorphic syndromes (Figure 2—figure supplement 1). For each feature point, the algorithm extracts a feature vector describing appearance of the surrounding patch. The algorithm then constructs a feature vector describing shape based on the relative pairwise distances between all feature points ('Materials and methods'). We next sought to compare the syndrome relevant information content of the feature descriptors to previous studies (Hammond et al., 2005; Boehringer et al., 2006; Hammond, 2007; Vollmar et al., 2008). We found that classification analysis based on support vector machines provided similar accuracies to previous work, despite disparities in image variability (average classification accuracy 94.4%, see Figure 4—figure supplement 1, Figure 4—figure supplement 2 and 'Materials and methods').10.7554/eLife.02020.008Figure 2.Animated morphs of average faces from controls to syndromes.


Diagnostically relevant facial gestalt information from ordinary photos.

Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP, Zisserman A, Nellåker C - Elife (2014)

Distortion graphs representing the characteristic deformation of syndrome faces relative to the average control face.Each line reflects whether the distance is extended or contracted compared with the control face. White—the distance is similar to controls, blue—shorter relative to controls, and red—extended in patients relative to controls.DOI:http://dx.doi.org/10.7554/eLife.02020.009
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4067075&req=5

fig2s1: Distortion graphs representing the characteristic deformation of syndrome faces relative to the average control face.Each line reflects whether the distance is extended or contracted compared with the control face. White—the distance is similar to controls, blue—shorter relative to controls, and red—extended in patients relative to controls.DOI:http://dx.doi.org/10.7554/eLife.02020.009
Mentions: We used an Active Appearance Model ('Materials and methods') to calculate an average face within any set of images, representing consistent shape and appearance features within the group (Figure 1B and animated morphs in Figure 2). The average faces for each syndrome show that the algorithm effectively captures characteristic features of dysmorphic syndromes (Figure 2—figure supplement 1). For each feature point, the algorithm extracts a feature vector describing appearance of the surrounding patch. The algorithm then constructs a feature vector describing shape based on the relative pairwise distances between all feature points ('Materials and methods'). We next sought to compare the syndrome relevant information content of the feature descriptors to previous studies (Hammond et al., 2005; Boehringer et al., 2006; Hammond, 2007; Vollmar et al., 2008). We found that classification analysis based on support vector machines provided similar accuracies to previous work, despite disparities in image variability (average classification accuracy 94.4%, see Figure 4—figure supplement 1, Figure 4—figure supplement 2 and 'Materials and methods').10.7554/eLife.02020.008Figure 2.Animated morphs of average faces from controls to syndromes.

Bottom Line: The space locates patients in the context of known syndromes and thereby facilitates the generation of diagnostic hypotheses.Consequently, the approach will aid clinicians by greatly narrowing (by 27.6-fold) the search space of potential diagnoses for patients with suspected developmental disorders.Furthermore, this Clinical Face Phenotype Space allows the clustering of patients by phenotype even when no known syndrome diagnosis exists, thereby aiding disease identification.

View Article: PubMed Central - PubMed

Affiliation: Department of Engineering Science, University of Oxford, Oxford, United Kingdom Medical Research Council Functional Genomics Unit, Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom.

Show MeSH
Related in: MedlinePlus