{"id":"W4413396650","doi":"10.1155/ijod/6644310","title":"Automated Classification of Dental Caries in Bitewing Radiographs Using Machine Learning and the ICCMS Framework","year":2025,"lang":"en","type":"article","venue":"International Journal of Dentistry","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of Alberta","funders":"University of Alberta","keywords":"Artificial intelligence; Radiography; Medicine; Inter-rater reliability; Segmentation; Dentistry; Recall; Reliability (semiconductor); Orthodontics; Computer science; Radiology; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.000528954,0.0001108025,0.0002437196,0.0007198949,0.00008202756,0.0001877097,0.0003672818,0.00007642857,0.00002703497],"category_scores_gemma":[0.0003440883,0.00009084452,0.0002022444,0.0005048641,0.0002802829,0.0003302245,0.00009359187,0.0004735897,9.245846e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007814833,"about_ca_system_score_gemma":0.00004186565,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002191449,"about_ca_topic_score_gemma":0.00005289242,"domain_scores_codex":[0.9984931,0.0001489186,0.0006759299,0.0001209978,0.0004425764,0.0001184728],"domain_scores_gemma":[0.9988301,0.0003202107,0.0005479798,0.00008111951,0.0001900272,0.00003060998],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002884824,0.00006622311,0.9818593,0.00002847212,0.0004145382,0.000254501,0.0002445935,0.000184633,0.01134216,0.003615388,0.00004729386,0.001654443],"study_design_scores_gemma":[0.004010486,0.00002252849,0.9318413,0.001359092,0.0001851078,0.003374229,0.002584379,0.04829256,0.00425074,0.003601283,0.0003146654,0.0001636517],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9875306,0.003903931,0.005617463,0.0001294157,0.002439386,0.000061568,0.00001189631,0.00002004775,0.0002856237],"genre_scores_gemma":[0.998037,0.0002078582,0.001560691,0.00004072822,0.00007867865,0.000001184731,0.000006267773,0.000008643744,0.00005894667],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05001799,"threshold_uncertainty_score":0.3704532,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01285953010630101,"score_gpt":0.3237286669297024,"score_spread":0.3108691368234013,"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."}}