{"id":"W4405014239","doi":"10.1016/j.jcmg.2024.09.010","title":"Leveraging a Vision Transformer Model to Improve Diagnostic Accuracy of Cardiac Amyloidosis With Cardiac Magnetic Resonance","year":2024,"lang":"en","type":"article","venue":"JACC. Cardiovascular imaging","topic":"Amyloidosis: Diagnosis, Treatment, Outcomes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Circle Cardiovascular Imaging; Kiniksa Pharmaceuticals; National Institutes of Health; Boston Scientific Corporation; Cleveland Clinic; National Heart, Lung, and Blood Institute; Alnylam Pharmaceuticals; Pfizer; Bristol-Myers Squibb","keywords":"Cardiac magnetic resonance; Cardiac amyloidosis; Magnetic resonance imaging; Transformer; Amyloidosis; Medicine; Cardiology; Internal medicine; Engineering; Radiology; Electrical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002051717,0.0007144865,0.0006275672,0.0008580761,0.0002503524,0.001423844,0.001216182,0.001057396,0.002560386],"category_scores_gemma":[0.0073164,0.0002439926,0.0006135151,0.0003223047,0.0005372305,0.00159498,0.001010671,0.001056533,0.0009616357],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007305636,"about_ca_system_score_gemma":0.001194893,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003274254,"about_ca_topic_score_gemma":0.003522717,"domain_scores_codex":[0.9994088,0.0001707656,0.00002690621,0.0001504614,0.0001547215,0.00008828393],"domain_scores_gemma":[0.9981007,0.0009016016,0.0002372661,0.0002026953,0.0003942906,0.0001634281],"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":[0.002261647,0.001005743,0.02900695,0.0003385923,0.0004265437,0.0009225046,0.0001706487,0.445007,0.06219554,0.05269846,0.01103788,0.3949284],"study_design_scores_gemma":[0.00005070608,0.0002155147,0.001136878,0.00001117806,0.00005196763,0.0002577838,0.00001503826,0.9770629,0.004828051,0.01545359,0.0008993599,0.0000169634],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1806738,0.001348519,0.802134,0.002741734,0.0005000049,0.0001262768,0.0004893459,0.002687542,0.009298636],"genre_scores_gemma":[0.942844,0.0003798622,0.05344134,0.0003013684,0.0001053055,0.00004087222,0.0002547315,0.0001025186,0.002530005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003274254,"threshold_uncertainty_score":0.01085061,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006911980164933713,"score_gpt":0.2443052014486091,"score_spread":0.2373932212836754,"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."}}