The original full text of the study is available from Translational Psychiatry HERE.
Conventionally, children are diagnosed using Autism Diagnostic Interview, Revised (ADI-R), a 93-question survey, and/or the Autism Diagnostic Observation Schedule (ADOS) which measures behavior. The two test can take up to 2.5 hours and must be administered by clinical professionals. Dennis Wall–the lead author of the paper and director of the computational biology initiative at Harvard Medical School–says that “with the rising incidence of Autism parents often have to wait more than a year after initial warning signs and an appointment with a professional for official diagnoses,” and the earlier behavioral interventions start, the better.
Wall and colleagues set out to speed up diagnoses. Using machine learning analysis of ADI-R they found that only 7/93 questions were needed for near accuracy. Applying the same techniques to ADOS, they used an ADTree machine learning algorithm to shorten the test from 29 to 8 steps.
Of course, autism diagnoses like most current psychiatric disorder diagnoses, are somewhat arbitrary in where they draw the line between Autism and Autism-like traits in neurotypical people. The algorithms may have to be adjusted next year with the release of the new psychiatric diagnostic manual, DSM-V.
Hopefully, these new tests will catch on within the psychiatric community increasing access to early diagnoses and early therapy when children’s brains are most plastic. Unfortunately the test only works for children two or older, but in the future functional imaging tests, eye-tracking, or genetic tests may allow earlier diagnoses.