Computer vision tools for low-cost and noninvasive measurement of autism-related behaviors in infants.

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated which promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests that behavioral signs can be observed late in the first year of life. Many of these studies involve extensive frame-by-frame video observation and analysis of a child's natural behavior. Although nonintrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are burdensome for clinical and large population research purposes. This work is a first milestone in a long-term project on non-invasive early observation of children in order to aid in risk detection and research of neurodevelopmental disorders. We focus on providing low-cost computer vision tools to measure and identify ASD behavioral signs based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure responses to general ASD risk assessment tasks and activities outlined by the AOSI which assess visual attention by tracking facial features. We show results, including comparisons with expert and nonexpert clinicians, which demonstrate that the proposed computer vision tools can capture critical behavioral observations and potentially augment the clinician's behavioral observations obtained from real in-clinic assessments.

Computer vision tools for the non-invasive assessment of autism-related behavioral markers

The early detection of developmental disorders is key to child outcome, allowing interventions to be initiated that promote development and improve prognosis. Research on autism spectrum disorder (ASD) suggests behavioral markers can be observed late in the first year of life. Many of these studies involved extensive frame-by-frame video observation and analysis of a child's natural behavior. Although non-intrusive, these methods are extremely time-intensive and require a high level of observer training; thus, they are impractical for clinical and large population research purposes. Diagnostic measures for ASD are available for infants but are only accurate when used by specialists experienced in early diagnosis. This work is a first milestone in a long-term multidisciplinary project that aims at helping clinicians and general practitioners accomplish this early detection/measurement task automatically. We focus on providing computer vision tools to measure and identify ASD behavioral markers based on components of the Autism Observation Scale for Infants (AOSI). In particular, we develop algorithms to measure three critical AOSI activities that assess visual attention. We augment these AOSI activities with an additional test that analyzes asymmetrical patterns in unsupported gait. The first set of algorithms involves assessing head motion by tracking facial features, while the gait analysis relies on joint foreground segmentation and 2D body pose estimation in video. We show results that provide insightful knowledge to augment the clinician's behavioral observations obtained from real in-clinic assessments.

Patterns of de novo allo B cells and antibody formation in chronic cardiac allograft rejection after alemtuzumab treatment.

Even though the etiology of chronic rejection (CR) is multifactorial, donor specific antibody (DSA) is considered to have a causal effect on CR development. Currently the antibody-mediated mechanisms during CR are poorly understood due to lack of proper animal models and tools. In a clinical setting, we previously demonstrated that induction therapy by lymphocyte depletion, using alemtuzumab (anti-human CD52), is associated with an increased incidence of serum alloantibody, C4d deposition and antibody-mediated rejection in human patients. In this study, the effects of T cell depletion in the development of antibody-mediated rejection were examined using human CD52 transgenic (CD52Tg) mice treated with alemtuzumab. Fully mismatched cardiac allografts were transplanted into alemtuzumab treated CD52Tg mice and showed no acute rejection while untreated recipients acutely rejected their grafts. However, approximately half of long-term recipients showed increased degree of vasculopathy, fibrosis and perivascular C3d depositions at posttransplant day 100. The development of CR correlated with DSA and C3d deposition in the graft. Using novel tracking tools to monitor donor-specific B cells, alloreactive B cells were shown to increase in accordance with DSA detection. The current animal model could provide a means of testing strategies to understand mechanisms and developing therapeutic approaches to prevent chronic rejection.