{"id":"W4376639796","doi":"10.1136/jnis-2023-020192","title":"Deep learning-based cerebral aneurysm segmentation and morphological analysis with three-dimensional rotational angiography","year":2023,"lang":"en","type":"article","venue":"Journal of NeuroInterventional Surgery","topic":"Intracranial Aneurysms: Treatment and Complications","field":"Medicine","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital","funders":"","keywords":"Aneurysm; Medicine; Radiology; Angiography; Cohort; Segmentation; Nuclear medicine; Internal medicine; Artificial intelligence; Computer science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003106043,0.0001547036,0.0003745466,0.001072167,0.0001505975,0.00003782711,0.00004891821,0.00004449243,0.0004018139],"category_scores_gemma":[0.00007352485,0.0001139932,0.001029491,0.001232805,0.0001017979,0.0001376261,0.00001795826,0.0002275938,0.00001600973],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002563931,"about_ca_system_score_gemma":0.00006506738,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007839418,"about_ca_topic_score_gemma":0.00001342765,"domain_scores_codex":[0.9983963,0.0001056634,0.0005281987,0.0002134472,0.0005740758,0.0001823163],"domain_scores_gemma":[0.9985456,0.0004855875,0.000391236,0.00008509828,0.0003448197,0.0001475944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001001322,0.0005479173,0.9799556,0.00003488109,0.001085042,0.00229616,0.00001098148,0.01189642,0.001373307,0.0001038315,0.0008208597,0.0008736877],"study_design_scores_gemma":[0.0009041215,0.0007651441,0.9802865,0.00005639173,0.0009991772,0.003044978,0.00001295432,0.0134494,0.0000715596,0.0002571618,0.00005129369,0.0001013245],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9923791,0.0001568902,0.004175357,0.003006058,0.00008656325,0.0001199217,0.00001052661,0.00004406535,0.00002154186],"genre_scores_gemma":[0.9980204,0.000008383282,0.001183049,0.0002765837,0.0001099684,0.00001044682,0.0002616463,0.00001511367,0.0001144538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005641278,"threshold_uncertainty_score":0.4648509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02757817333562258,"score_gpt":0.2698747276952155,"score_spread":0.2422965543595929,"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."}}