{"id":"W4409578837","doi":"10.3171/2024.12.jns242167","title":"Utility of artificial intelligence in radiosurgery for pituitary adenoma: a deep learning–based automated segmentation model and evaluation of its clinical applicability","year":2025,"lang":"en","type":"article","venue":"Journal of neurosurgery","topic":"Pituitary Gland Disorders and Treatments","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University Health Centre","funders":"Ministry of Defense; Ministerstvo Zdravotnictví Ceské Republiky; Univerzita Karlova v Praze","keywords":"Radiosurgery; Medicine; Pituitary adenoma; Radiomics; Optic chiasm; Artificial intelligence; Nuclear medicine; Segmentation; Adenoma; Radiology; Medical physics; Machine learning; Radiation therapy; Computer science; Pathology; Optic nerve","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":[0.001578495,0.0006389539,0.0004987857,0.0005608663,0.000190688,0.0006107332,0.0006085284,0.0007155836,0.0006154308],"category_scores_gemma":[0.004178599,0.0002344356,0.0004808064,0.0002956717,0.0003751079,0.0003854206,0.0004584488,0.0005042715,0.0001523793],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001183938,"about_ca_system_score_gemma":0.0008814818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01272999,"about_ca_topic_score_gemma":0.008281069,"domain_scores_codex":[0.9995345,0.0001738617,0.00002971539,0.0001127988,0.0001070342,0.00004219635],"domain_scores_gemma":[0.9986629,0.0008043282,0.0001571102,0.00008251553,0.0002475989,0.00004542557],"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":[0.0004361703,0.0002135329,0.01331045,0.00006639463,0.0001298723,0.00009116629,0.00005480659,0.8721695,0.003412284,0.000368646,0.0006580143,0.1090892],"study_design_scores_gemma":[0.000006284299,0.00007167077,0.001126019,0.000004788755,0.0000095276,0.00001802691,0.000004345524,0.9979373,0.0006073267,0.0001366805,0.00007449333,0.00000356105],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8162075,0.001335079,0.1778786,0.0007367373,0.00005979957,0.0001768902,0.0002934994,0.0008014123,0.002510527],"genre_scores_gemma":[0.9757986,0.0001767071,0.02307734,0.00008455404,0.00001494591,0.00005489321,0.0002067427,0.00001665756,0.0005695836],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01272999,"threshold_uncertainty_score":0.02531177,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09198076509440456,"score_gpt":0.4014115738379829,"score_spread":0.3094308087435783,"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."}}