{"id":"W7131103492","doi":"10.1109/idicaihei65991.2025.11379135","title":"Dental Disease Detection Using EfficientNet","year":2025,"lang":"","type":"article","venue":"","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Workload; Identification (biology); Diagnostic accuracy; sort; Disease; Spotting","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":[0.0005388653,0.001517404,0.0008241911,0.001331882,0.0004473719,0.0009914492,0.001452592,0.001321399,0.004587673],"category_scores_gemma":[0.00160257,0.000364359,0.0007307352,0.0006262545,0.000273417,0.001391359,0.0007678706,0.0007975051,0.001814917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009958465,"about_ca_system_score_gemma":0.001311625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01597674,"about_ca_topic_score_gemma":0.02095806,"domain_scores_codex":[0.9997019,0.00003288753,0.00002252023,0.0001109946,0.00007000707,0.00006167706],"domain_scores_gemma":[0.9996459,0.0001058276,0.00003802349,0.00004634371,0.0001315277,0.00003230343],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001066342,0.000912358,0.02485083,0.0007157063,0.0004168909,0.0007025674,0.0001910676,0.263227,0.01410514,0.00419635,0.05795131,0.6316644],"study_design_scores_gemma":[0.00004226122,0.0001444962,0.002601467,0.00004115284,0.00006373403,0.0001802893,0.00006452947,0.9831186,0.005609606,0.002782413,0.005326262,0.00002523769],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5194849,0.007463468,0.3943948,0.003598187,0.00178036,0.0007031673,0.01398945,0.02788242,0.03070333],"genre_scores_gemma":[0.8631193,0.001426887,0.1013419,0.001150895,0.0002171941,0.0002993268,0.01864588,0.0002815994,0.01351698],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01597674,"threshold_uncertainty_score":0.03176749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00989833142895447,"score_gpt":0.282899584371582,"score_spread":0.2730012529426276,"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."}}