TY  - JOUR
AU  - Jaiswal, Aditi
AU  - Kruiper, Ruben
AU  - Rasool, Abdur
AU  - Nandkeolyar, Aayush
AU  - Wall, Dennis P
AU  - Washington, Peter
PY  - 2024
DA  - 2024/2/8
TI  - Digitally Diagnosing Multiple Developmental Delays Using Crowdsourcing Fused With Machine Learning: Protocol
for a Human-in-the-Loop Machine Learning Study
JO  - JMIR Res Protoc
SP  - e52205
VL  - 13
KW  - machine learning
KW  - crowdsourcing
KW  - autism spectrum disorder
KW  - ASD
KW  - attention-deficit/hyperactivity disorder
KW  - ADHD
KW  - precision health
AB  - Background: A considerable number of minors in the United States are diagnosed with developmental or psychiatric conditions, potentially influenced by underdiagnosis factors such as cost, distance, and clinician availability. Despite the potential of digital phenotyping tools with machine learning (ML) approaches to expedite diagnoses and enhance diagnostic services for pediatric psychiatric conditions, existing methods face limitations because they use a limited set of social features for prediction tasks and focus on a single binary prediction, resulting in uncertain accuracies. Objective: This study aims to propose the development of a gamified web system for data collection, followed by a fusion of novel crowdsourcing algorithms with ML behavioral feature extraction approaches to simultaneously predict diagnoses of autism spectrum disorder and attention-deficit/hyperactivity disorder in a precise and specific manner. Methods: The proposed pipeline will consist of (1) gamified web applications to curate videos of social interactions adaptively based on the needs of the diagnostic system, (2) behavioral feature extraction techniques consisting of automated ML methods and novel crowdsourcing algorithms, and (3) the development of ML models that classify several conditions simultaneously and that adaptively request additional information based on uncertainties about the data. Results: A preliminary version of the web interface has been implemented, and a prior feature selection method has highlighted a core set of behavioral features that can be targeted through the proposed gamified approach. Conclusions: The prospect for high reward stems from the possibility of creating the first artificial intelligence–powered tool that can identify complex social behaviors well enough to distinguish conditions with nuanced differentiators such as autism spectrum disorder and attention-deficit/hyperactivity disorder. International Registered Report Identifier (IRRID): PRR1-10.2196/52205 
SN  - 1929-0748
UR  - https://www.researchprotocols.org/2024/1/e52205
UR  - https://doi.org/10.2196/52205
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38329783
DO  - 10.2196/52205
ID  - info:doi/10.2196/52205
ER  -