{"id":"W4225587561","doi":"10.1109/jbhi.2022.3164848","title":"A Deep Invertible 3-D Facial Shape Model for Interpretable Genetic Syndrome Diagnosis","year":2022,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Face recognition and analysis","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Alberta Children's Hospital; University of Calgary","funders":"National Institute of Dental and Craniofacial Research; National Institutes of Health; Canada Research Chairs; Calgary Foundation","keywords":"Interpretability; Artificial intelligence; Computer science; Machine learning; Sensitivity (control systems); Deep learning; Pattern recognition (psychology)","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":[0.0009048622,0.000091675,0.000318326,0.0003305375,0.0003195987,0.00007722558,0.0004048363,0.00003731274,0.0000554912],"category_scores_gemma":[0.00003153953,0.00007438458,0.0001182715,0.0003335383,0.00004946066,0.0003493472,0.0001283218,0.0002406828,0.000003470446],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008964402,"about_ca_system_score_gemma":0.0003688159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007285179,"about_ca_topic_score_gemma":0.000002965761,"domain_scores_codex":[0.9981315,0.00003470123,0.001008218,0.00006669902,0.0004874142,0.0002714959],"domain_scores_gemma":[0.9988329,0.0000665702,0.0005215625,0.00009416088,0.00008925423,0.0003955699],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003667111,0.0004145314,0.0006258918,0.000781858,0.0001487553,0.00002500674,0.01189616,0.01497187,0.00001203651,0.0003604532,0.02234206,0.9483847],"study_design_scores_gemma":[0.0005722363,0.0007248478,0.00005801416,0.00004139808,0.00001549298,0.0003442807,0.0002600182,0.9886556,0.000005951579,0.001392199,0.007846396,0.00008362205],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03192678,0.0003495274,0.9608986,0.006220079,0.0004342197,0.0001257062,0.00001758555,0.0000141637,0.00001337383],"genre_scores_gemma":[0.817706,0.001275271,0.1684066,0.01242221,0.00008127867,0.00004781335,0.000005776115,0.00000805658,0.0000469679],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9736837,"threshold_uncertainty_score":0.3033315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04084293900262106,"score_gpt":0.2944524396245819,"score_spread":0.2536095006219609,"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."}}