{"id":"W4318830044","doi":"10.4015/s1016237222500533","title":"AUTOMATED DETECTION OF CHILDHOOD OBESITY IN ABDOMINOPELVIC REGION USING THERMAL IMAGING BASED ON DEEP LEARNING TECHNIQUES","year":2023,"lang":"en","type":"article","venue":"Biomedical Engineering Applications Basis and Communications","topic":"Infrared Thermography in Medicine","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Tellabs (Canada)","funders":"","keywords":"Obesity; Medicine; Childhood obesity; Body surface; Body shape; Overweight; Internal medicine; Pathology; Mathematics","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":[0.0002047898,0.0005275544,0.0003237361,0.0008251353,0.00014578,0.0003640122,0.0004012758,0.0004195053,0.001178168],"category_scores_gemma":[0.0004763095,0.0001733239,0.000455648,0.0003813312,0.0001363241,0.0003193917,0.0003448422,0.000439832,0.000353797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003150729,"about_ca_system_score_gemma":0.0003493949,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004921649,"about_ca_topic_score_gemma":0.008003672,"domain_scores_codex":[0.9998807,0.00001934217,0.000007333341,0.00003404674,0.00002443016,0.00003410394],"domain_scores_gemma":[0.9998822,0.00003280518,0.00002325894,0.000008237887,0.00004029845,0.00001317693],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008116412,0.0004854885,0.08272099,0.0003262973,0.000235286,0.001259723,0.0001864234,0.1340312,0.07084893,0.00166021,0.008922363,0.6985114],"study_design_scores_gemma":[0.00001981468,0.0001833606,0.03349393,0.00007106209,0.00007192567,0.0006018628,0.00009973603,0.9435222,0.01873325,0.001250042,0.001926216,0.00002655097],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.5675995,0.002550702,0.4177953,0.000566107,0.0002181442,0.0001488923,0.001462561,0.002566156,0.007092753],"genre_scores_gemma":[0.9210243,0.0008566826,0.07282986,0.0001953452,0.0000657304,0.00008571438,0.001153607,0.00006871855,0.003720092],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.004921649,"threshold_uncertainty_score":0.00978601,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009711889395853624,"score_gpt":0.2572341152532024,"score_spread":0.2475222258573488,"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."}}