{"id":"W4401868171","doi":"10.3389/fncom.2024.1434421","title":"Deep learning for detecting prenatal alcohol exposure in pediatric brain MRI: a transfer learning approach with explainability insights","year":2024,"lang":"en","type":"article","venue":"Frontiers in Computational Neuroscience","topic":"Neonatal and fetal brain pathology","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; Alberta Children's Hospital Research Institute; Kids Brain Health Network; Canadian Institutes of Health Research; Alberta Innovates","keywords":"Prenatal alcohol exposure; Transfer of learning; Prenatal exposure; Deep learning; Computer science; Artificial intelligence; Neuroimaging; Neuroscience; Psychology; Alcohol; Pregnancy; Chemistry; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004578433,0.0001792084,0.0002625127,0.0005056943,0.0001588793,0.00005265518,0.00013523,0.00008109181,0.000001191153],"category_scores_gemma":[0.0004720078,0.0001531428,0.00006641854,0.001132674,0.0001822366,0.000289587,0.00003820574,0.0006871419,7.860688e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009098842,"about_ca_system_score_gemma":0.0001539757,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003357021,"about_ca_topic_score_gemma":0.000004251921,"domain_scores_codex":[0.9980899,0.0001502643,0.0003057791,0.0007601682,0.0003455284,0.0003483456],"domain_scores_gemma":[0.999325,0.0004190808,0.00003112015,0.00008244769,0.00005483413,0.00008747437],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004384479,0.0001670497,0.1345703,0.0007994325,0.000005284056,0.0003937705,0.003639824,0.839743,0.00057662,0.0003195574,0.00002193737,0.01932479],"study_design_scores_gemma":[0.001337431,0.0007903003,0.09312321,0.00007010335,0.00001438607,0.0002698881,0.0004643091,0.902149,0.0000787325,0.001160655,0.0003491419,0.0001928999],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4856626,0.0005201379,0.5126933,0.000244263,0.0002593858,0.0004686527,0.000001340329,0.00006578701,0.00008455943],"genre_scores_gemma":[0.9731078,0.00001323238,0.02631076,0.0001982958,0.00008146502,0.0001203111,0.000024289,0.00002356844,0.0001203038],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4874452,"threshold_uncertainty_score":0.6244983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01474016794008214,"score_gpt":0.2517821544881344,"score_spread":0.2370419865480523,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}