My Bots Weblog

«The brain activity of 2-year-olds with autism as they listen to words predicts thinking and language skills up to 4 years later. The finding hints that brain measurements may help to anticipate future abilities in children with autism and allow for early, personalized interventions. Autism spectrum disorders are a group of related brain conditions that affect about 1 in 88 children nationwide. Children with autism don't follow typical milestones for social and communication skills. They may avoid eye contact, have trouble with words and language and become preoccupied with certain objects. Because these children can have widely varying outcomes, scientists have been searching for reliable methods to predict a child's likely developmental path. Dr. Patricia K. Kuhl of the University of Washington in Seattle has been exploring differences in brain brain activity between children with and without autism.» [...] «In followup studies 2 and 4 years later, the children with autism were again assessed. The researchers found that the children’s brain activity patterns in response to known words at age 2 predicted their developmental abilities at ages 4 and 6. The group with less social impairments had greater improvements in language skills, thinking and adaptive behaviors compared to the group with more social impairments. “We've shown that the brain’s indicator of word learning in 2-year-olds already diagnosed with autism predicts their eventual skills on a broad set of cognitive and linguistic abilities and adaptive behaviors,” says Kuhl. “This line of work may lead to new interventions applied early in development, when the brain shows its highest level of neural plasticity.” »...
Source: http://www.nih.gov | Source Status Categories: TED,EarlyDiagnosis

«survey About Our Survey Autism can be diagnosed through the use of a behavioral exam named the "Autism Diagnostic Interview - Revised" or ADI-R. However, because this survey is long - it has 93 questions and can take up to 2.5 hours to complete - the diagnosis process can be prohibitive. We have designed a substantially shorter exam of 7 questions that we believe may be as effective as the ADI-R at diagnosing autistic children. Should this prove to be true, our exam would help healthcare providers diagnose autistic children more rapidly and enable children to receive valuable therapy that is more in tune with the timing of their development and more likely to have a positive outcome. One big way to determine if our abbreviated exam is effective is to get your help. If you are a care provider for a person with autism, your answers to the few questions in the following survey will tell us whether our exam actually works. It will take no more than 10 minutes of your time.»...
Source: http://autworks.hms.harvard.edu | Source Status Categories: TED,EarlyDiagnosis

«Researchers at Harvard Medical School have significantly reduced from hours to minutes the time it takes to accurately detect autism in young children. The process of diagnosing autism is complex, subjective, and often limited to only a segment of the population in need. With the recent rise in incidence to 1 in 88 children, the need for accurate and widely deployable methods for screening and diagnosis is substantial. Dennis Wall, associate professor of pathology and director of computational biology initiative at the Center for Biomedical Informatics at Harvard Medical School, has been working to address this problem and has discovered a highly accurate strategy that could significantly reduce the complexity and time of the diagnostic process. Wall has been developing algorithms and associated deployment mechanisms to detect autism rapidly and with high accuracy. The algorithms are designed to work within a mobile architecture, combining a small set of questions and a short home video of the subject, to enable rapid online assessments. This procedure could reduce the time for autism diagnosis by nearly 95 percent, from hours to minutes, and could be easily integrated into routine child screening practices to enable a dramatic increase in reach to the population at risk. "We believe this approach will make it possible for more children to be accurately diagnosed during the early critical period when behavioral therapies are most effective," said Wall. This research was published online in Nature Translational Psychiatry. Autism is diagnosed through a careful analysis of an individual's behavior. When children are evaluated for autism, they typically take the Autism Diagnostic Interview, Revised, known as the ADI-R, a 93-question questionnaire, and/or the Autism Diagnostic Observation Schedule, known as the ADOS exam, which measures several behaviors in children. Together these evaluations can take up to three hours to complete and must be administered by a trained clinician. Often, there is a delay of more than a year between initial warning signs and diagnosis because of the waiting times to see a clinical professional who can administer the tests and deliver the formal diagnosis, Wall said. Using machine learning techniques, an artificial intelligence method where machines are trained to make decisions, Wall and his team studied results of the ADI-R from the Autism Genetic Research Exchange for more than 800 individuals diagnosed with autism to find redundancies across the exam. They found that only seven questions were sufficient to diagnose autism with nearly 100 percent accuracy, equivalent to the full 93-question exam. They validated the accuracy of the seven question survey against answer sets from more than 1,600 individuals from the Simons Foundation and more than 300 individuals from the Boston Autism Consortium.»...
Source: http://www.medicalnewstoday.com | Source Status Categories: TED,EarlyDiagnosis

«The Autism Diagnostic Observation Schedule-Generic (ADOS) is one of the most widely used instruments for behavioral evaluation of autism spectrum disorders. It is composed of four modules, each tailored for a specific group of individuals based on their language and developmental level. On average, a module takes between 30 and 60...‰min to deliver. We used a series of machine-learning algorithms to study the complete set of scores from Module 1 of the ADOS available at the Autism Genetic Resource Exchange (AGRE) for 612 individuals with a classification of autism and 15 non-spectrum individuals from both AGRE and the Boston Autism Consortium (AC). Our analysis indicated that 8 of the 29 items contained in Module 1 of the ADOS were sufficient to classify autism with 100% accuracy. We further validated the accuracy of this eight-item classifier against complete sets of scores from two independent sources, a collection of 110 individuals with autism from AC and a collection of 336 individuals with autism from the Simons Foundation. In both cases, our classifier performed with nearly 100% sensitivity, correctly classifying all but two of the individuals from these two resources with a diagnosis of autism, and with 94% specificity on a collection of observed and simulated non-spectrum controls. The classifier contained several elements found in the ADOS algorithm, demonstrating high test validity, and also resulted in a quantitative score that measures classification confidence and extremeness of the phenotype. With incidence rates rising, the ability to classify autism effectively and quickly requires careful design of assessment and diagnostic tools. Given the brevity, accuracy and quantitative nature of the classifier, results from this study may prove valuable in the development of mobile tools for preliminary evaluation and clinical prioritization-in particular those focused on assessment of short home videos of children-that speed the pace of initial evaluation and broaden the reach to a significantly larger percentage of the population at risk.» [...] «Conclusions Currently, autism spectrum disorder is diagnosed through behavioral exams and questionnaires that require significant time investment for both parents and clinicians. In our study, we performed a data-driven approach to select a reduced set of questions from one of the most widely used instruments for behavioral diagnosis, the ADOS. Using machine-learning algorithms, we found the ADTree to perform with almost perfect sensitivity, specificity and accuracy in distinguishing individuals with autism from individuals without autism. The ADTree classifier consisted of eight questions, 72.4% less than the complete ADOS Module 1, and performed with >99% accuracy when applied to independent populations of individuals with autism, misclassifying only 2 out of 446 cases. Given this reduction in the number of items without appreciable loss in accuracy, our findings may help to guide future efforts, chiefly including mobile health approaches, to shorten the evaluation and diagnosis process overall such that families can receive care earlier than under current diagnostic modalities. »...
Source: http://www.nature.com | Source Status Categories: TED,EarlyDiagnosis