{"id":"W4400604250","doi":"10.1007/s00405-024-08809-4","title":"Artificial intelligence for automatic detection and segmentation of nasal polyposis: a pilot study","year":2024,"lang":"en","type":"article","venue":"European Archives of Oto-Rhino-Laryngology","topic":"Sinusitis and nasal conditions","field":"Medicine","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Surgical Specialties (Canada)","funders":"","keywords":"Artificial intelligence; Computer science; Segmentation; Computer vision; Medicine; 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":[0.001704157,0.0004102785,0.0006197846,0.0008942961,0.000366202,0.0006095768,0.0005254924,0.0007674101,0.002625486],"category_scores_gemma":[0.00440647,0.0002626194,0.0006519918,0.0004269911,0.0004644695,0.000560489,0.0004835153,0.000379605,0.000582491],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002528114,"about_ca_system_score_gemma":0.0003552227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002963339,"about_ca_topic_score_gemma":0.00231624,"domain_scores_codex":[0.9991754,0.0004089982,0.00006559904,0.0001447141,0.0001378563,0.00006750313],"domain_scores_gemma":[0.9970443,0.0020506,0.00009998225,0.0002371432,0.0004396522,0.0001283352],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.01573392,0.009264887,0.1005182,0.0009444946,0.0005164492,0.002461155,0.002267584,0.01279195,0.1902779,0.0004847378,0.001629642,0.6631091],"study_design_scores_gemma":[0.001511801,0.0352167,0.5667825,0.0001146165,0.00132659,0.008475371,0.002630454,0.2990071,0.07650259,0.001346252,0.006910406,0.0001755131],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9870889,0.0004026857,0.01078982,0.00006535199,0.00002540666,0.0002467805,0.0001761523,0.0001070033,0.001097993],"genre_scores_gemma":[0.9754239,0.0003063717,0.02249277,0.00007304506,0.00003144551,0.0001143884,0.0004488877,0.00004188561,0.001067444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002963339,"threshold_uncertainty_score":0.00901252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04141340910961107,"score_gpt":0.3116953408158605,"score_spread":0.2702819317062494,"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."}}