An observation-based classifier for rapid detection of autism risk

"Keep clear of the moors"

Among the many researchers and research groups admired on this blog for their contribution to the world of autism research, the name Dennis Wall is fast becoming a real favourite. Aside from mention of the words 'systems biology' in his profile at Stanford University, I'm particularly interested in the way the Wall research group are looking at trying to apply machine-learning approaches to things like autism assessment.

I've covered a few of their past research reports with regards to instruments like the Autism Diagnostic Interview (ADI) and the Autism Diagnostic Observation Schedule (ADOS) previously (see here and see here respectively). More recently was the work suggesting that YouTube videos and non-expert raters might be a useful resource for autism triage (see here). That last report certainly set the cat among [some] pigeons...

Today I'm talking about another paper from the Wall laboratory by Marlena Duda and colleagues [1] (open-access) and the suggestion that: "reductions in the process of detecting and monitoring autism are possible". The ADOS was once again the focus of the study following on from their previous 'preliminary' foray [2].

The paper is open-access but here are a few choice details:

• If I'm reading the paper correctly, this was a follow-up study to the previous Wall paper [2] testing the accuracy of the "observation-based classifier (OBC)" which I think was previously called/included the ADTree algorithm. This time around "a cohort of archival score sheets of over 2600 subjects, including more than 280 assessments of non-spectrum controls" were included in the study derived from ADOS and ADOS-2 algorithms. ADOS-2 by the way, represents the revised algorithms used to score ADOS reported by Gotham and colleagues [3]. I've talked about the Gotham paper before on this blog and how it seemed to 'predict' diagnosis of autism in DSM-5 (see here).
• The aim was to test whether a boiled down version of the ADOS / ADOS-2, the OBC, that: "presently contains eight behaviors... that are often impacted in children with autism, including eye contact, imaginative play and reciprocal communication" might be able to distinguish autism from not-autism and "shorten screening and diagnostic processes overall and potentially enabling more families to receive care far earlier and during timeframes when interventions have the most positive benefits".
• Results: "The OBC was significantly correlated with the ADOS-G (r=−0.814) and ADOS-2 (r=−0.779) and exhibited >97% sensitivity and >77% specificity in comparison to both ADOS algorithm scores". These figures aren't bad at all, if a little down on the previous Wall data [2]. The authors add: "Less than 5% of all tested cases were misclassified by the OBC and 78% of the misclassified individuals were given a low OBC score".

Obviously there is much more investigation needed in this area of autism research before one might start shortening ADOS assessments (or indeed doing away with trained ADOS raters altogether). The issue of comorbidity is, for example, something that needs to be included in any further study and whether that might interfere with any results obtained [4]. I might also add that ADOS is only part of the diagnostic assessment for autism and does not replace reasoned clinical opinion.

I am however drawn to the authors suggestion that: "use of the OBC as a web-based assessment in advance of a clinical visit may enable clinicians to quickly prioritize patients according to symptom severity, scheduling shorter, more immediate diagnostic appointments for individuals that can be clearly identified as on or off the autism spectrum, and allowing longer time periods for deeper evaluation of children that exhibit clinically challenging symptoms". Certainly with the numbers of children/adults seemingly coming through the various referral systems, this kind of triage might yet hold some usefulness. And it seems other groups are getting in on the computer-assisted act [5]...

Music to close and The Marcels with Blue Moon. "Keep clear of the moors" as we were once told...

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[1] Duda A. et al. Testing the accuracy of an observation-based classifier for rapid detection of autism risk. Translational Psychiatry. 2014; 4: e424.

[2] Wall DP. et al. Use of machine learning to shorten observation-based screening and diagnosis of autism. Transl Psychiatry. 2012 Apr 10;2:e100.

[3] Gotham K. et al. The Autism Diagnostic Observation Schedule: revised algorithms for improved diagnostic validity. J Autism Dev Disord. 2007 Apr;37(4):613-27.

[4] Leyfer OT. et al. Overlap between autism and specific language impairment: comparison of Autism Diagnostic Interview and Autism Diagnostic Observation Schedule scores. Autism Res. 2008 Oct;1(5):284-96.

[5] Hashemi J. et al. Computer Vision Tools for Low-Cost and Noninvasive Measurement of Autism-Related Behaviors in Infants. Autism Research and Treatment. 2014. 935686.

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M Duda, J A Kosmicki, & D P Wall (2014). Testing the accuracy of an observation-based classifier for rapid detection of autism risk Translational Psychiatry, 4 : 10.1038/tp.2014.65