{"id":"W3195607837","doi":"10.1016/j.media.2021.102218","title":"Automatic annotation of cervical vertebrae in videofluoroscopy images via deep learning","year":2021,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Dysphagia Assessment and Management","field":"Health Professions","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto General Hospital; North York General Hospital","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institutes of Health","keywords":"Artificial intelligence; Computer science; Swallowing; Pixel; Gold standard (test); Convolutional neural network; Kinematics; Deep learning; Landmark; Computer vision; Pattern recognition (psychology); Medicine; Radiology","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":[0.0003838825,0.0007577856,0.0005936117,0.002156619,0.0003917507,0.001065116,0.0007610031,0.001489927,0.001878386],"category_scores_gemma":[0.001475772,0.000327017,0.000796821,0.0008942565,0.0002573146,0.0004883461,0.0007359234,0.0007551314,0.001543189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005232039,"about_ca_system_score_gemma":0.001136108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01970888,"about_ca_topic_score_gemma":0.02985415,"domain_scores_codex":[0.9996879,0.00003222466,0.00002118345,0.0001046226,0.00008035267,0.00007369745],"domain_scores_gemma":[0.9996443,0.0001025817,0.00003900297,0.0000427292,0.0001464843,0.00002492614],"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":[0.0005214188,0.0001839769,0.01479542,0.0005444533,0.0001565735,0.0007498041,0.0001797005,0.0183226,0.1166819,0.001067405,0.0126904,0.8341063],"study_design_scores_gemma":[0.00004553887,0.0002330358,0.04125045,0.0003503656,0.000291189,0.001949544,0.0003211711,0.8387361,0.09600099,0.003808233,0.01691865,0.00009473672],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2241126,0.006474679,0.7463129,0.0008986788,0.0003387652,0.0004292964,0.005447608,0.008287064,0.007698517],"genre_scores_gemma":[0.6901123,0.002345178,0.293117,0.0004427444,0.0001774341,0.0002338229,0.005127329,0.000441782,0.008002342],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01970888,"threshold_uncertainty_score":0.03918833,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01309589200679523,"score_gpt":0.393504932193157,"score_spread":0.3804090401863618,"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."}}