{"id":"W4412714137","doi":"10.1016/j.phro.2025.100817","title":"Image-based mandibular and maxillary parcellation and annotation using computed tomography (IMPACT): a deep learning-based clinical tool for orodental dose estimation and osteoradionecrosis assessment","year":2025,"lang":"en","type":"article","venue":"Physics and Imaging in Radiation Oncology","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"National Center for Advancing Translational Sciences; Center for Clinical and Translational Sciences, University of Texas Health Science Center at Houston; KWF Kankerbestrijding; Hitachi; National Cancer Institute; National Institutes of Health; National Aeronautics and Space Administration; National Institute of Dental and Craniofacial Research; University of Texas MD Anderson Cancer Center; Cancer Prevention and Research Institute of Texas; ZonMw; Baylor College of Medicine","keywords":"Computed tomography; Annotation; Osteoradionecrosis; Estimation; Artificial intelligence; Computer science; Medicine; Orthodontics; Dentistry; Radiation therapy; Radiology; Engineering","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":[0.0008062972,0.0008265447,0.0004308435,0.001687299,0.0002030865,0.0007230128,0.0008424136,0.0009738058,0.001961008],"category_scores_gemma":[0.001849613,0.0003518333,0.0005383531,0.0006448427,0.0004072626,0.000436261,0.001068195,0.0005653813,0.0005368215],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007875748,"about_ca_system_score_gemma":0.0007488182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00543633,"about_ca_topic_score_gemma":0.00863592,"domain_scores_codex":[0.9996717,0.00004674672,0.00002301234,0.0000932407,0.0001274463,0.00003791674],"domain_scores_gemma":[0.999519,0.0001661749,0.00009350132,0.00005974971,0.0001253949,0.00003620421],"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":[0.0005691554,0.0001780758,0.01850954,0.0002740987,0.0001307834,0.0004170486,0.0001975444,0.1246294,0.06532082,0.001347298,0.004990919,0.7834353],"study_design_scores_gemma":[0.00002464495,0.0001232784,0.01185841,0.0000435134,0.00006845657,0.0006629122,0.00004206497,0.956153,0.02678439,0.001496304,0.00270885,0.0000340708],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1929556,0.001488219,0.7945324,0.0004341369,0.00005132234,0.0003210778,0.001039819,0.006552607,0.002624938],"genre_scores_gemma":[0.6635404,0.0007113295,0.3304473,0.0002434141,0.00005348104,0.0002434188,0.001512071,0.0003594687,0.002889136],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00543633,"threshold_uncertainty_score":0.01080936,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01429160763060082,"score_gpt":0.3715181879923098,"score_spread":0.357226580361709,"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."}}