{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000400471,0.000138035,0.0002398789,0.0008602631,0.0001539376,0.000009264184,0.0002121758,0.0001042723,0.000004011424],"category_scores_gemma":[0.0001380943,0.0001346943,0.00006303971,0.001749651,0.0001955273,0.00003958097,0.00008314718,0.0003834018,0.000002676967],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007441612,"about_ca_system_score_gemma":0.00003981813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005069254,"about_ca_topic_score_gemma":0.000001503163,"domain_scores_codex":[0.9989724,0.00006211776,0.000368316,0.0001973562,0.0002060954,0.0001936749],"domain_scores_gemma":[0.9987358,0.0002427846,0.00009993667,0.0007456238,0.00007401498,0.0001018803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007754355,0.002320894,0.03424629,0.0005947837,0.0001433722,0.00001684036,0.001242475,0.005464435,0.5274774,0.001442743,0.00003015313,0.426943],"study_design_scores_gemma":[0.0005657385,0.0000744549,0.202243,0.0003737647,0.00005654869,0.0000200321,0.0001377303,0.7913501,0.003697358,0.00002115198,0.001334263,0.0001259261],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8573903,0.001195434,0.1332507,0.002605879,0.00005827154,0.001905603,0.00001322799,0.00287025,0.0007103598],"genre_scores_gemma":[0.9884633,0.0002113764,0.01081722,0.00006642882,0.00003640313,0.0002936114,0.0000801668,0.00002852427,0.000002986008],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7858856,"threshold_uncertainty_score":0.5492675,"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."}}