1. Facial Imaging in FASD and Related Disabilities
Luther K Robinson, MD

2. Background
Many developmental disability disorders are diagnosed clinically
Training
Experience
Quantitative findings
FASD, a common disorder fits this paradigm
Early diagnosis improves outcomes
Under-served may not be readily diagnosed
Important to expand diagnostic capacity

3. FAS: Diagnostic Criteria
Growth deficiency (height or weight <10th centile)
Characteristic facial manifestations
Short palpebral fissures
Smooth philtrum/thin upper vermilion
Central nervous system abnormalities
Structural (e.g., small head, abnormal MRI)
Functional (e.g., learning disability)

4. Questions Which clinical measures correlate with diagnosis of FASD?
Do additional measures add to diagnostic accuracy or expand the FAS phenotype?
Do clinical measures compare with digital measures?
Can laser imaging techniques be applied to distinguish FAS from other syndromes?
Can laser technology be translated more broadly?

5. Goals and Objectives
Evaluate sensitivity and specificity of clinical and anthropometric measures in the diagnosis of FASD
Compare these measures with data from 3-D facial imaging

6. Methods and Protocol Case population – patients with FASD
Disease control – patients with Williams syndrome
Controls - Matched, unexposed subjects

7. Phenotypic Similarities in Three Disorders
Williams syndrome
Velocardiofacial syndrome
Fetal alcohol syndrome
Anticonvulsant embryopathies

8. Methods and Protocol Physical (morphological) examination
Height, weight, head circumference
Palpebral fissure length
Maxillary and mandibular arcs
Philtral assessment (Astley scores)
Non-craniofacial measures (e.g., flexion creases of hands)
Facial imaging

9. Facial Measurements Lengths Arcs Palpebral fissure
Inner canthal distance
Philtral length
Arcs
Maxillary
Mandibular

10. Short Palpebral Fissures

11. Smooth Philtrum, Thin Upper Lip
From Astley et al, 2000

12. Logistic Regression Modeling
The best clinical measures that predicted FAS compared to Controls were palpebral fissure length, mandibular arc, and campodactyly. The overall likelihood ratio statistic for the model was 70.30, p<0.001
The best clinical measure that predicted FAS compared to WS was palpebral fissure length. The overall likelihood ratio statistic for the model was 21.89, p<0.001

13. Logistic Regression Modeling
The best clinical measures that predicted Controls compared to WS were height centile, smooth philtrum, and camptodactyly. The overall likelihood ratio statistic for the model was 39.59, p<0.001.