{"id":"W4407797489","doi":"10.1007/s00259-025-07145-x","title":"Artificial intelligence-powered coronary artery disease diagnosis from SPECT myocardial perfusion imaging: a comprehensive deep learning study","year":2025,"lang":"en","type":"article","venue":"European Journal of Nuclear Medicine and Molecular Imaging","topic":"Cardiac Imaging and Diagnostics","field":"Medicine","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Université de Genève; Shaheed Rajaei Cardiovascular Medical and Research Center; Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung; National Science Foundation","keywords":"Coronary artery disease; CAD; Myocardial perfusion imaging; Artificial intelligence; Single-photon emission computed tomography; Receiver operating characteristic; Medicine; Deep learning; Computer science; Radiology; Nuclear medicine; Machine learning; Internal medicine","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":[0.002595873,0.0009902116,0.0006681656,0.001121605,0.0001993021,0.0008123782,0.0007279692,0.0007550605,0.0005266533],"category_scores_gemma":[0.004877531,0.0002795487,0.0009568002,0.0006521039,0.0004427162,0.0008988283,0.000794702,0.000940196,0.0001365938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008294786,"about_ca_system_score_gemma":0.0008865446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005343887,"about_ca_topic_score_gemma":0.003725705,"domain_scores_codex":[0.9995334,0.0001869444,0.0000305448,0.00009991854,0.0000958129,0.00005349003],"domain_scores_gemma":[0.9980155,0.001319936,0.0001233746,0.0001438334,0.0002980103,0.00009927289],"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":[0.0007815394,0.001163945,0.1074662,0.0005356437,0.001708029,0.0003185607,0.0001832838,0.4078064,0.003071338,0.003613057,0.004708225,0.4686438],"study_design_scores_gemma":[0.00002642741,0.0003215973,0.02192221,0.00007796063,0.0002268568,0.00008877109,0.00004105041,0.9727486,0.0009674357,0.002650243,0.0009072357,0.00002169295],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8632456,0.02358878,0.1059397,0.001891429,0.0001053108,0.0001354671,0.0004835976,0.0004446865,0.004165423],"genre_scores_gemma":[0.9853902,0.002823017,0.01026701,0.0001694268,0.00006365263,0.00004365616,0.0006227324,0.00002499898,0.0005953565],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005343887,"threshold_uncertainty_score":0.01372844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0172087582551324,"score_gpt":0.2751024746954446,"score_spread":0.2578937164403122,"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."}}