{"id":"W4415269518","doi":"10.1016/j.ddj.2025.100042","title":"AI-driven analysis of jaw-bone alterations in CBCT images associated with systemic diseases: A systematic review","year":2025,"lang":"en","type":"article","venue":"Digital Dentistry Journal","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Cone beam computed tomography; Temporomandibular joint; Convolutional neural network; Osteoporosis; Osteoarthritis; Computed tomography; Medical imaging; Artificial neural network","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":[0.008635222,0.00166121,0.007732394,0.009629936,0.0005103892,0.003133814,0.002320525,0.001995478,0.004212806],"category_scores_gemma":[0.0659497,0.0009765988,0.009154627,0.007205065,0.0007488457,0.002246315,0.001245185,0.0009774193,0.0004116307],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002573541,"about_ca_system_score_gemma":0.008117822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00773308,"about_ca_topic_score_gemma":0.02043513,"domain_scores_codex":[0.9914829,0.002706744,0.003413205,0.0008059852,0.001436559,0.0001546467],"domain_scores_gemma":[0.9450865,0.04491101,0.005688298,0.0008483944,0.003265342,0.0002004862],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001607072,0.00001645229,0.001169205,0.9382841,0.01135336,0.00006263887,0.0001080266,0.0001704383,0.0001386714,0.0002141948,0.001324103,0.04699806],"study_design_scores_gemma":[0.0002256739,0.0001928731,0.006843715,0.8727872,0.09851933,0.0004776701,0.000207174,0.0003086044,0.0002498034,0.0005671515,0.01955589,0.00006487567],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0008678891,0.9976344,0.000333564,0.0001816079,0.00008046601,0.000204551,0.0004906504,0.00001024689,0.0001966075],"genre_scores_gemma":[0.01924733,0.9765663,0.00179277,0.0007693699,0.0001650192,0.0006941769,0.0006157061,0.00001486762,0.0001343836],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.009629936,"threshold_uncertainty_score":0.04566795,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00669518659075301,"score_gpt":0.2725054828860897,"score_spread":0.2658102962953367,"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."}}