{"id":"W4317933255","doi":"10.3390/app13031516","title":"Detection of Periapical Lesions on Panoramic Radiographs Using Deep Learning","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Artificial intelligence; Convolutional neural network; Classifier (UML); Radiography; Computer science; Detector; Pattern recognition (psychology); Dentistry; Medicine; Radiology","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":[0.0008513193,0.0006616224,0.0004351373,0.001271376,0.0001779096,0.0006501309,0.0007073767,0.0008781257,0.0007906905],"category_scores_gemma":[0.00144924,0.0003049196,0.0004195197,0.0004892685,0.0002472614,0.0006953004,0.0005464079,0.0004018465,0.000402467],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004342433,"about_ca_system_score_gemma":0.0004391069,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002142482,"about_ca_topic_score_gemma":0.005372846,"domain_scores_codex":[0.9995607,0.00005897547,0.00003103784,0.0001309966,0.0001607329,0.00005763393],"domain_scores_gemma":[0.9994192,0.0001777745,0.000139426,0.00006051028,0.000177932,0.00002529558],"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":[0.0003747964,0.0002697427,0.05946344,0.0005381547,0.0002255386,0.0006097952,0.0001792236,0.02439396,0.1690374,0.000542704,0.002408509,0.7419567],"study_design_scores_gemma":[0.00002607024,0.0004666496,0.1056362,0.0001574686,0.0002813919,0.002171323,0.0002699022,0.7779146,0.1060135,0.001628668,0.005380641,0.00005360226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7063089,0.003577966,0.2834735,0.0004532542,0.00010408,0.0001837818,0.0005501276,0.002151018,0.003197408],"genre_scores_gemma":[0.9179504,0.0008520876,0.07867969,0.000180079,0.00003976706,0.0000518763,0.000483998,0.00002997284,0.001732288],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002142482,"threshold_uncertainty_score":0.004502237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02956446719293342,"score_gpt":0.2926277044529598,"score_spread":0.2630632372600264,"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."}}