{"id":"W4313555774","doi":"10.3389/fcvm.2022.1040053","title":"Deep-learning method for fully automatic segmentation of the abdominal aortic aneurysm from computed tomography imaging","year":2023,"lang":"en","type":"article","venue":"Frontiers in Cardiovascular Medicine","topic":"Aortic aneurysm repair treatments","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Mitacs","keywords":"Abdominal aortic aneurysm; Segmentation; Medicine; Radiology; Aneurysm; Lumen (anatomy); Artificial intelligence; Aorta; Asymptomatic; Abdominal aorta; Computed tomography; Computer science; Surgery","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001043554,0.001155131,0.001127238,0.00116167,0.0005442547,0.0009440316,0.001841896,0.00201065,0.001881313],"category_scores_gemma":[0.00165736,0.0007913471,0.001339226,0.0009539942,0.0005252593,0.0007390262,0.00128791,0.001767034,0.001273627],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038078,"about_ca_system_score_gemma":0.002127103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01285877,"about_ca_topic_score_gemma":0.01743922,"domain_scores_codex":[0.9995745,0.00005916755,0.00004205137,0.0001458553,0.0001053192,0.00007312254],"domain_scores_gemma":[0.999517,0.0001746708,0.00005878053,0.00004731895,0.0001685071,0.00003377085],"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":[0.0003544457,0.0001988479,0.001907367,0.0002595356,0.0001574402,0.0002844617,0.0001807172,0.3492302,0.02565276,0.003496581,0.009405034,0.6088726],"study_design_scores_gemma":[0.000008276803,0.00002211296,0.0002456619,0.00001185344,0.0000104909,0.00004454251,0.000006983939,0.9948002,0.002853852,0.001228007,0.0007596479,0.00000846935],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01736738,0.0007081339,0.9766673,0.0002674645,0.00008880925,0.0001094752,0.0003438806,0.003621371,0.0008261202],"genre_scores_gemma":[0.2831142,0.0007836449,0.7069634,0.0005019753,0.0001311846,0.0004721235,0.002549398,0.0004772168,0.00500699],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01285877,"threshold_uncertainty_score":0.02556789,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009499336078887961,"score_gpt":0.2693301625110946,"score_spread":0.2598308264322066,"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."}}