{"id":"W4401219250","doi":"10.1155/2024/5551209","title":"Using Advanced Convolutional Neural Network Approaches to Reveal Patient Age, Gender, and Weight Based on Tongue Images","year":2024,"lang":"en","type":"article","venue":"BioMed Research International","topic":"Traditional Chinese Medicine Studies","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Fields Institute for Research in Mathematical Sciences; York University; University of Toronto","funders":"Key Research and Development Program of Zhejiang Province","keywords":"Convolutional neural network; Tongue; Artificial intelligence; Deep learning; Pearson product-moment correlation coefficient; Pattern recognition (psychology); Receiver operating characteristic; Computer science; Correlation coefficient; Correlation; Mean squared error; Artificial neural network; Medicine; Machine learning; Statistics; Mathematics; Pathology","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":[0.0003382771,0.0007161256,0.0003157637,0.0008103519,0.0001289481,0.000407624,0.0002525592,0.0003552696,0.001232745],"category_scores_gemma":[0.001065918,0.0001585243,0.0003780718,0.0004777011,0.0001382872,0.0003776316,0.0004035392,0.0004097678,0.0003680349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002988241,"about_ca_system_score_gemma":0.0003105584,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004770683,"about_ca_topic_score_gemma":0.01077279,"domain_scores_codex":[0.9998982,0.00002031943,0.000009706277,0.00003385521,0.00002227668,0.00001562577],"domain_scores_gemma":[0.9998063,0.00006000379,0.00004401197,0.00002098652,0.00005324331,0.00001539632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009742921,0.000257999,0.249303,0.0003589556,0.0003830538,0.001171195,0.0002507846,0.04155859,0.07265957,0.001846399,0.00539222,0.6258439],"study_design_scores_gemma":[0.00003374335,0.0002997409,0.1396668,0.0001102224,0.0002585223,0.001132124,0.0002348825,0.8173571,0.03203934,0.004025124,0.004779305,0.00006308285],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6630983,0.00244606,0.3233144,0.0006378194,0.0002309739,0.0001757207,0.002958539,0.0009176583,0.006220542],"genre_scores_gemma":[0.9451656,0.0009674009,0.05027992,0.0001501884,0.0000528224,0.00006446169,0.001220024,0.00003077024,0.002068786],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004770683,"threshold_uncertainty_score":0.009485841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3685846826560442,"score_gpt":0.4233343598998434,"score_spread":0.0547496772437992,"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."}}