{"id":"W3010232800","doi":"10.1007/s11548-020-02131-0","title":"Recursive multiresolution convolutional neural networks for 3D aortic valve annulus planimetry","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Cardiac Valve Diseases and Treatments","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University Health Centre; University of Ottawa","funders":"","keywords":"Convolutional neural network; Aortic valve replacement; Stenosis; Artificial intelligence; Aortic valve; Cardiac skeleton; Medicine; Orientation (vector space); Aortic valve stenosis; Computer science; Valve replacement; Radiology; Computer vision; Pattern recognition (psychology); Cardiology; Mathematics; Geometry","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":[0.0005085582,0.000657874,0.0005902449,0.0005686856,0.0002278458,0.0006756345,0.000984467,0.001039281,0.002188542],"category_scores_gemma":[0.001375998,0.0007182135,0.0007987155,0.0005032672,0.0002533295,0.0005583157,0.0009751465,0.001164292,0.0009571256],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006638998,"about_ca_system_score_gemma":0.0009006208,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0138707,"about_ca_topic_score_gemma":0.02074951,"domain_scores_codex":[0.9997717,0.00003635569,0.00001384174,0.00006668964,0.0000761592,0.00003531202],"domain_scores_gemma":[0.9996058,0.0001780571,0.00004002579,0.00005872008,0.00009906781,0.00001836508],"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":[0.000126545,0.00005585715,0.001324535,0.00008540461,0.00009065629,0.00009561561,0.00006855166,0.5682039,0.01591436,0.003666858,0.002896128,0.4074716],"study_design_scores_gemma":[0.000001634345,0.000006346096,0.0001918457,0.000004756008,0.000005589307,0.00001926983,0.000002632259,0.9972703,0.001562652,0.0005922489,0.0003387941,0.000003915866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01877245,0.0006075127,0.9771678,0.0001488489,0.00003642463,0.00002934901,0.000228313,0.001945853,0.001063528],"genre_scores_gemma":[0.473011,0.0007982554,0.5189428,0.00023177,0.0000595906,0.00009964512,0.0009435664,0.0003387068,0.005574578],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0138707,"threshold_uncertainty_score":0.0275799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02443783787942991,"score_gpt":0.3133272082637731,"score_spread":0.2888893703843431,"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."}}