{"doi":"10.3390/a18120764","title":"Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder","abstract":"Early detection of Autism Spectrum Disorder (ASD), a neurodevelopmental condition characterized by social communication challenges, is essential for timely intervention. Naturalistic home videos collected via mobile applications offer scalable opportunities for digital diagnostics. We leveraged GuessWhat, a mobile game designed to engage parents and children, which has generated over 3000 structured videos from 382 children. From this collection, we curated a final analytic sample of 688 feature-rich videos centered on a single dyad, enabling more consistent modeling. We developed a two-step pipeline: (1) filtering to isolate high-quality videos, and (2) feature engineering to extract interpretable behavioral signals. Unimodal LSTM-based models trained on eye gaze, head position, and facial expression achieved test AUCs of 86% (95% CI: 0.79–0.92), 78% (95% CI: 0.69–0.86), and 67% (95% CI: 0.55–0.78), respectively. Late-stage fusion of unimodal outputs significantly improved predictive performance, yielding a test AUC of 90% (95% CI: 0.84–0.95). Our findings demonstrate the complementary value of distinct behavioral channels and support the feasibility of using mobile-captured videos for detecting clinically relevant signals. While further work is needed to improve generalizability and inclusivity, this study highlights the promise of real-time, scalable autism phenotyping for early interventions.","journal":"Algorithms","year":2025,"id":586201,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9161,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2025-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":323280,"name":"Aaron Kline","orcid":"0000-0002-0077-5485","position":1,"is_corresponding":false},{"id":901762,"name":"Saimourya Surabhi","orcid":"0000-0002-1707-0537","position":2,"is_corresponding":false},{"id":323278,"name":"Kaitlyn Dunlap","orcid":"0000-0003-4423-5269","position":3,"is_corresponding":false},{"id":813575,"name":"Onur Cezmi Mutlu","orcid":"0000-0002-9263-9332","position":4,"is_corresponding":false},{"id":1174818,"name":"Mohammadmahdi Honarmand","orcid":"0000-0002-5778-6054","position":5,"is_corresponding":false},{"id":1441313,"name":"Parnian Azizian","orcid":null,"position":6,"is_corresponding":false},{"id":323276,"name":"Peter Washington","orcid":"0000-0003-3276-4411","position":7,"is_corresponding":false},{"id":80565,"name":"Dennis P. Wall","orcid":"0000-0002-7889-9146","position":8,"is_corresponding":false},{"id":1500255,"name":"Marie Amale Huynh","orcid":"0009-0003-0994-1296","position":0,"is_corresponding":true}],"reference_count":48,"raw_metadata":null,"created_at":"2026-07-19T02:59:28.666390Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}