{"id":"W4406769220","doi":"10.1111/cid.70000","title":"Artificial Intelligence‐Based Detection and Numbering of Dental Implants on Panoramic Radiographs","year":2025,"lang":"en","type":"article","venue":"Clinical Implant Dentistry and Related Research","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Numbering; Radiography; Dentistry; Medicine; Recall; Implant; Segmentation; Dental implant; Artificial intelligence; Precision and recall; Orthodontics; Computer science; Algorithm; Surgery; Psychology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001021959,0.0007799259,0.0005019158,0.002027055,0.0001469655,0.0009676555,0.0007867174,0.0008230406,0.001231519],"category_scores_gemma":[0.0036821,0.0002840634,0.0007141247,0.0009332183,0.0003162071,0.0006949284,0.0005273193,0.0004765456,0.0006882738],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004148633,"about_ca_system_score_gemma":0.0005356052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0030753,"about_ca_topic_score_gemma":0.004731395,"domain_scores_codex":[0.9992079,0.0001350271,0.00008247651,0.0002520162,0.000254336,0.00006833547],"domain_scores_gemma":[0.9986852,0.0004784129,0.0003303535,0.0001019654,0.0003682311,0.00003583713],"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":[0.0005791632,0.0002756641,0.1512887,0.0007512103,0.0002575682,0.0005906363,0.0002472506,0.05898806,0.04789888,0.0006843625,0.004183657,0.7342549],"study_design_scores_gemma":[0.00003034798,0.0004087872,0.1814057,0.0002476929,0.0002331617,0.001894477,0.0003223996,0.763425,0.04239025,0.001965708,0.00760182,0.00007466473],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6789058,0.006240437,0.3029874,0.0007745945,0.0002394477,0.0002691087,0.003024567,0.00203516,0.005523364],"genre_scores_gemma":[0.9033462,0.00162005,0.0897515,0.0001633913,0.0001069342,0.0001079736,0.002472343,0.0000536095,0.00237795],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0030753,"threshold_uncertainty_score":0.006114781,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09629506240003666,"score_gpt":0.4495846022659248,"score_spread":0.3532895398658882,"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."}}