{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005473118,0.000544637,0.0004605084,0.0006695269,0.0002195203,0.000620703,0.0006430136,0.0009081647,0.001582638],"category_scores_gemma":[0.001066344,0.000457066,0.0004346156,0.0006910418,0.0005173518,0.0007621079,0.0007178396,0.0004608486,0.0009131843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003768514,"about_ca_system_score_gemma":0.0005327858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00103522,"about_ca_topic_score_gemma":0.001877643,"domain_scores_codex":[0.9994146,0.0001090245,0.00003270926,0.0001670825,0.0002293357,0.00004723606],"domain_scores_gemma":[0.9996585,0.00009225581,0.00007354853,0.00007142559,0.00008620461,0.00001819561],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002567347,0.00008552772,0.002829783,0.0001387171,0.00002982415,0.0001318046,0.00009282814,0.002967686,0.6975288,0.00164653,0.001370759,0.292921],"study_design_scores_gemma":[0.00009428152,0.001961788,0.02238646,0.0000701955,0.0002221807,0.002699265,0.0002380211,0.4569614,0.4927251,0.002275752,0.0201101,0.0002554601],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1184586,0.002072579,0.8732944,0.0006873961,0.0001799825,0.0001573263,0.0001453847,0.001537916,0.003466444],"genre_scores_gemma":[0.427653,0.0009245276,0.567396,0.0002554506,0.0001006589,0.0001279802,0.00014322,0.00006118104,0.003337999],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001582638,"threshold_uncertainty_score":0.005294502,"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."}}