{"id":"W4415114474","doi":"10.1016/j.prosdent.2025.09.040","title":"Dual convolutional neural network framework for segmenting dental caries in panoramic radiographs","year":2025,"lang":"en","type":"article","venue":"Journal of Prosthetic Dentistry","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Korea Evaluation Institute of Industrial Technology; Ministry of Science and ICT, South Korea; Ministry of SMEs and Startups; Ministry of Education, Science and Technology; Ministry of Health and Welfare; National Research Foundation of Korea; Korea Health Industry Development Institute; Ministry of Trade, Industry and Energy; Ministry of Education","keywords":"Convolutional neural network; Radiography; Dual (grammatical number); Segmentation; Market segmentation; Pattern recognition (psychology)","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":[0.0009210457,0.0007164573,0.0005877903,0.001195272,0.0002420175,0.0007325683,0.0010807,0.001014107,0.001292593],"category_scores_gemma":[0.001183699,0.0003628746,0.0006881013,0.0005390279,0.000292764,0.0007004998,0.0006675495,0.0006920841,0.0004133547],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008901742,"about_ca_system_score_gemma":0.001223392,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01180618,"about_ca_topic_score_gemma":0.01446807,"domain_scores_codex":[0.9996395,0.00005048023,0.00002100019,0.0001052498,0.0001213336,0.00006250109],"domain_scores_gemma":[0.9996285,0.00008329601,0.0000594161,0.00003231312,0.0001727386,0.00002355745],"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":[0.0005190396,0.0003040713,0.005972477,0.0002259547,0.0002456686,0.000266766,0.0001250565,0.2971805,0.04855038,0.003464261,0.003720798,0.6394251],"study_design_scores_gemma":[0.000005884024,0.00005317443,0.001173231,0.00001517742,0.00003421589,0.00007219188,0.00001090172,0.9902238,0.006906312,0.000739674,0.0007560286,0.00000933029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1053924,0.001778848,0.8873529,0.0002789421,0.00009418233,0.0001040139,0.0003543854,0.002447746,0.002196626],"genre_scores_gemma":[0.6604934,0.0008980536,0.3321418,0.000254843,0.00008687984,0.0001628982,0.0007951254,0.0001080259,0.005059054],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01180618,"threshold_uncertainty_score":0.02347493,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0115143907078909,"score_gpt":0.289656692649524,"score_spread":0.2781423019416331,"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."}}