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

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

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)

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?

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

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

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

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

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

Short Palpebral Fissures

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

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

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.