{"id":"W4221085494","doi":"10.2196/36977","title":"Fully Automated Wound Tissue Segmentation Using Deep Learning on Mobile Devices: Cohort Study","year":2022,"lang":"en","type":"article","venue":"JMIR mhealth and uhealth","topic":"Pressure Ulcer Prevention and Management","field":"Health Professions","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Segmentation; Wound care; Artificial intelligence; Convolutional neural network; Granulation tissue; Wound healing; Surgery; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001486268,0.0005296037,0.0004509666,0.0008831544,0.0004925104,0.0007505113,0.0006507737,0.000669116,0.001464018],"category_scores_gemma":[0.004715951,0.0004439701,0.0006084974,0.0005977198,0.0004998039,0.0006833271,0.0008545109,0.000876544,0.0008672407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005287973,"about_ca_system_score_gemma":0.000515289,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006187892,"about_ca_topic_score_gemma":0.00883662,"domain_scores_codex":[0.9993361,0.0001532102,0.00005248712,0.0002555157,0.000114332,0.00008825856],"domain_scores_gemma":[0.9970066,0.0007747359,0.0004201062,0.0009205887,0.0006162412,0.000261735],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002378727,0.001399692,0.9430079,0.0001551087,0.0004690913,0.001161591,0.0008327535,0.001435373,0.002783759,0.0002771204,0.007128689,0.03897021],"study_design_scores_gemma":[0.0002260423,0.002443991,0.9546575,0.0001226521,0.0004681374,0.003425706,0.001898969,0.025491,0.003335434,0.0008717803,0.006913722,0.0001450999],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959643,0.0001417587,0.001850854,0.00004896156,0.00001539789,0.00006794619,0.001614245,0.0000429317,0.0002535872],"genre_scores_gemma":[0.9922664,0.0001848587,0.002303374,0.00006737851,0.00003035525,0.0001566611,0.004291867,0.00003661053,0.0006624881],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006187892,"threshold_uncertainty_score":0.01230371,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07538774162963366,"score_gpt":0.4781474153773049,"score_spread":0.4027596737476712,"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."}}