Deep Learning to Transform Clinician Autism Diagnostic Assessments and More

Deep Learning to Transform Clinician Autism Diagnostic Assessments and More

Award Information
Agency: Department of Health and Human Services
Branch: N/A
Contract: 1R44MH115523-01
Agency Tracking Number: R44MH115523
Amount: $149,124.00
Phase: Phase I
Program: SBIR
Awards Year: 2018
Solicitation Year: 2016
Solicitation Topic Code: 103
Solicitation Number: PA16-302
Small Business Information
413 W IDAHO ST STE 301, Boise, ID, 83702-6066
DUNS: 808040302
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 RONALD OBERLEITNER
 (208) 629-8778
 admin@talkautism.org
Business Contact
 RONALD OBERLEITNER
Phone: (609) 306-9181
Email: ron@behaviorimaging.com
Research Institution
N/A
Abstract
NODA Telehealth system improves access to an autism diagnostic assessment by guiding families to share video clips of their child at homeso diagnostic clinicians can directly observe andtagvideo of any atypical behaviorand if warrantedrender a diagnosisThis system is evidence based and has been commercializedwith several published studies to discuss the benefitsWe now propose to improve this service by developing a DeepmachineLearning capability in a software product calledNODA DL Classifierto help clinicians more quickly identify and better quantify typical and atypical behaviors on videos they receive from familiesIf successfulthis NODA DL feature within the NODA system will have a profound impact in the time to reach a firm diagnosisand then the capability could be used subsequently to effectively monitor treatment progress of individuals diagnosed with autismIn this projectwe will determine how much DL improves the diagnostic processIn Phase Iwe will test our use previously generated datasets to qualify and quantify potential benefitsIn Phase IIwe will conduct a clinical study to document time savings and other clinical benefitsOur proposed NODA DL innovation represents a large step change in identification and then the care for ASD individualsnot an incremental oneIt will lead to a significant improvement in both health outcomes and in reduced time required by clinicians or psychologists for office visits and for analyzing video dataThis reduced time can be translated into reduced costsWe anticipate that significant commercial benefits will result from the use of our innovative computer methodologies The proposed computerized Deep LearningDLfunction within our current NODA Telehealth System will have a profound impact in saving time to reach a firm diagnosis of individuals with ASDplus provide other important benefits

* Information listed above is at the time of submission. *

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