{"id":"W4414576575","doi":"10.1002/dneu.23001","title":"Early Prediction and Risk Analysis Using Hybrid Deep Learning Techniques in Multimodal Biomedical Image","year":2025,"lang":"en","type":"article","venue":"Developmental Neurobiology","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Interpretability; Deep learning; Medical imaging; Feature extraction; Feature (linguistics); Pattern recognition (psychology); Smoothing; Process (computing)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001390254,0.001085147,0.0009988111,0.001445234,0.0002417721,0.0008546013,0.0009437424,0.0009562729,0.0007938475],"category_scores_gemma":[0.002395956,0.0003760737,0.0009459322,0.0007434307,0.0003600719,0.0009767453,0.001082894,0.001043665,0.0002476349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007734722,"about_ca_system_score_gemma":0.00083215,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006178755,"about_ca_topic_score_gemma":0.005977706,"domain_scores_codex":[0.9994934,0.0001173558,0.00003030779,0.0001292223,0.0001428342,0.00008694246],"domain_scores_gemma":[0.9994544,0.0002155953,0.00008656372,0.00005281471,0.0001520922,0.00003842245],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.0002955598,0.0002275624,0.01063112,0.00009989047,0.0002069281,0.0003587073,0.00007688097,0.7062791,0.009108556,0.002809687,0.002528917,0.2673772],"study_design_scores_gemma":[0.000002976629,0.00002152247,0.0004977614,0.000005881906,0.00001349898,0.00002813244,0.000005602244,0.9970375,0.001099188,0.001121174,0.0001610385,0.000005602222],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1185107,0.001989398,0.8758206,0.0008156326,0.00006760417,0.00005066356,0.0002537363,0.001327879,0.001163867],"genre_scores_gemma":[0.9016283,0.0008914685,0.09430427,0.0003275872,0.00009204358,0.00007799385,0.0004721937,0.00007309912,0.002133144],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006178755,"threshold_uncertainty_score":0.01228559,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005056878578627496,"score_gpt":0.2672826414032313,"score_spread":0.2622257628246038,"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."}}