{"id":"W4412028744","doi":"10.1016/j.acra.2025.06.024","title":"Disease Classification of Pulmonary Xenon Ventilation MRI Using Artificial Intelligence","year":2025,"lang":"en","type":"article","venue":"Academic Radiology","topic":"Atomic and Subatomic Physics Research","field":"Physics and Astronomy","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute; Natural Sciences and Engineering Research Council of Canada; National Institutes of Health","keywords":"Pulmonary disease; Xenon; Disease; Medicine; Artificial intelligence; Environmental science; Computer science; Internal medicine; Physics; Nuclear physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002202525,0.00009067707,0.0001759433,0.000235017,0.00007457437,0.000004318515,0.0002229419,0.00009503047,0.0000604326],"category_scores_gemma":[0.00001625379,0.00009250848,0.00006979545,0.000461243,0.000166721,0.00009431213,0.0000587118,0.000351057,0.00001564439],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005360721,"about_ca_system_score_gemma":0.0002409988,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004570948,"about_ca_topic_score_gemma":9.791982e-8,"domain_scores_codex":[0.9990332,0.0001060452,0.00034697,0.0002288783,0.00008566228,0.0001992966],"domain_scores_gemma":[0.9994611,0.0001202439,0.0001254213,0.0001895816,0.00004998983,0.00005373609],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001187963,0.00005115302,0.05468106,0.00001817155,0.00004457634,7.026703e-7,0.00008946826,0.0009810955,0.06124911,0.7086669,0.0001681505,0.1739308],"study_design_scores_gemma":[0.00004532588,0.000003614926,0.01461103,0.00003170103,0.0000349753,6.098155e-7,0.0003078567,0.5565636,0.01056796,0.4172804,0.0004532711,0.00009960913],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5976325,0.0001713841,0.3990151,0.000280664,0.0001457381,0.00015557,0.00001419776,0.00001143512,0.002573373],"genre_scores_gemma":[0.9994404,0.00004008604,0.0001266608,0.00002225416,0.0001854804,0.00001375263,0.00005412489,0.00000695719,0.0001102688],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5555825,"threshold_uncertainty_score":0.3772386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05931305001511781,"score_gpt":0.370648383192271,"score_spread":0.3113353331771532,"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."}}