Abstract
Background: Fetal alcohol spectrum disorders (FASD) is one of the most common causes of developmental disabilities and neurobehavioral deficits. Despite the high-prevalence of FASD, the current diagnostic process is challenging and time- and money- consuming, with underreported profiles of the neurocognitive and neurobehavioral impairments because of limited clinical capacity. We assessed children/youth with FASD from a multimodal perspective and developed a high-performing, low-cost screening protocol using a machine learning framework.Methods and Findings: Participants with FASD and age-matched typically developing controls completed up to six assessments, including saccadic eye movement tasks (prosaccade, antisaccade, and memory-guided saccade), free viewing of videos, psychometric tests, and neuroimaging of the corpus callosum. We comparatively investigated new machine learning methods applied to these data, toward the acquisition of a quantitative signature of the neurodevelopmental deficits, and the development of an objective, high-throughput screening tool to identify children/youth with FASD. Our method provides a comprehensive profile of distinct measures in domains including sensorimotor and visuospatial control, visual perception, attention, inhibition, working memory, academic functions, and brain structure. We also showed that a combination of four to six assessments yields the best FASD vs. control classification accuracy; however, this protocol is expensive and time consuming. We conducted a cost/benefit analysis of the six assessments and developed a high-performing, low-cost screening protocol based on a subset of eye movement and psychometric tests that approached the best result under a range of constraints (time, cost, participant age, required administration, and access to neuroimaging facility). Using insights from the theory of value of information, we proposed an optimal annual screening procedure for children at risk of FASD.Conclusions: We developed a high-capacity, low-cost screening procedure under constrains, with high expected monetary benefit, substantial impact of the referral and diagnostic process, and expected maximized long-term benefits to the tested individuals and to society. This annual screening procedure for children/youth at risk of FASD can be easily and widely deployed for early identification, potentially leading to earlier intervention and treatment. This is crucial for neurodevelopmental disorders, to mitigate the severity of the disorder and/or frequency of secondary comorbidities.
Highlights
Fetal alcohol spectrum disorder (FASD) is the most common preventable developmental disorder, resulting from prenatal alcohol exposure [1]
Costs associated with FASD, in areas such as health care, special education, and social services, can run into billions of dollars annually, which places a large burden on both families and society [5, 6]
The diagnostic process may take up to two full days requiring a multidisciplinary team comprised of a physician, psychologist, facial dysmorphologist and occupational therapist, and the result can vary from clinic to clinic because of the wide spectrum of deficits [9]
Summary
Fetal alcohol spectrum disorder (FASD) is the most common preventable developmental disorder, resulting from prenatal alcohol exposure [1]. Despite the high prevalence of FASD, the clinical diagnosis can be both challenging and time consuming. It currently requires a confirmed history of the prenatal alcohol exposure and a comprehensive profile of central nervous system and neurobehavioral deficits, which are often difficult to obtain. Fetal alcohol spectrum disorders (FASD) is one of the most common causes of developmental disabilities and neurobehavioral deficits. Despite the high-prevalence of FASD, the current diagnostic process is challenging and time- and money- consuming, with underreported profiles of the neurocognitive and neurobehavioral impairments because of limited clinical capacity. We assessed children/youth with FASD from a multimodal perspective and developed a high-performing, low-cost screening protocol using a machine learning framework
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