{"id":"W4401560007","doi":"10.1021/acssensors.4c00124","title":"Intraoral Ultrasound Imaging Using a Rotational Transducer with Periodontal Feature Identification by Machine Learning","year":2024,"lang":"en","type":"article","venue":"ACS Sensors","topic":"Dental Radiography and Imaging","field":"Dentistry","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"National Research Council Canada; Alberta Innovates; Mitacs","keywords":"Transducer; Feature (linguistics); Ultrasound; Biomedical engineering; Identification (biology); Computer science; Dentistry; Materials science; Artificial intelligence; Orthodontics; Computer vision; Acoustics; Medicine; Radiology; Biology; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001279909,0.000215814,0.0001502599,0.000192377,0.0002824911,0.000670046,0.0001178551,0.00005394052,0.0001938098],"category_scores_gemma":[0.00002507184,0.0001981795,0.0001155714,0.0005092824,0.0001234543,0.0007506386,0.00001154574,0.0004911761,0.00007607195],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008436968,"about_ca_system_score_gemma":0.00002947259,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001727906,"about_ca_topic_score_gemma":0.00003937379,"domain_scores_codex":[0.9986122,0.00007734714,0.0002069857,0.0004275657,0.0003778742,0.000297986],"domain_scores_gemma":[0.999593,0.00008246533,0.0000601398,0.0001364672,0.00004930751,0.00007862406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0001229813,0.00008322159,0.1835714,0.000157991,0.0003047706,0.0006798008,0.003694561,0.001431313,0.7986347,0.0003089102,0.003103368,0.007907],"study_design_scores_gemma":[0.004595431,0.0002294354,0.386583,0.001614865,0.001667216,0.03381294,0.01640154,0.2859919,0.2057129,0.0006281679,0.05852389,0.004238702],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927885,0.002544958,0.00298735,0.0002135634,0.0004985843,0.0001488561,0.000101165,0.000241059,0.0004759629],"genre_scores_gemma":[0.9966968,0.00002802894,0.000679189,0.00005987555,0.0001480451,0.000006682057,0.0003322792,0.00005941575,0.001989635],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5929218,"threshold_uncertainty_score":0.8081527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007085265633896546,"score_gpt":0.2483644562265152,"score_spread":0.2412791905926187,"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."}}