{"id":"W4212905562","doi":"10.1038/s41598-022-06726-2","title":"Artificial intelligence enabled automated diagnosis and grading of ulcerative colitis endoscopy images","year":2022,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":99,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Canadian Institutes of Health Research; Alberta Innovates; Alberta Machine Intelligence Institute; University of Alberta; Natural Sciences and Engineering Research Council of Canada; Alberta Health Services; Government of Alberta; Canadian Institute for Advanced Research","keywords":"Artificial intelligence; Hyperparameter; Convolutional neural network; Grading (engineering); Computer science; Colonoscopy; Ulcerative colitis; Artificial neural network; Class (philosophy); Pattern recognition (psychology); Machine learning; Colorectal cancer; Medicine; Pathology; Cancer; Disease; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009981737,0.00009061124,0.0002176875,0.0002344908,0.000469554,0.0000680665,0.00003758941,0.00002397412,0.0003204626],"category_scores_gemma":[0.0002457501,0.0000895249,0.00005871227,0.0006162168,0.0002035441,0.00008228728,0.00009372405,0.0001343567,0.000001230626],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001060548,"about_ca_system_score_gemma":0.0001441361,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002287262,"about_ca_topic_score_gemma":0.00005273485,"domain_scores_codex":[0.9984483,0.00007605424,0.0004039371,0.0004578707,0.0004315715,0.0001823339],"domain_scores_gemma":[0.9992188,0.00007555765,0.0002279405,0.0002649994,0.0001229756,0.00008976595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.009303443,0.001606957,0.09238623,0.0008319087,0.0004948751,0.006210294,0.01846718,0.002128466,0.4336826,0.0009698262,0.06822306,0.3656952],"study_design_scores_gemma":[0.00008790606,0.00107561,0.009365872,0.00006716501,0.0000665157,0.0009646999,0.00135645,0.004540817,0.9753827,0.004398486,0.002556275,0.000137482],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955312,0.0001983786,0.0003239564,0.0001783,0.002436748,0.0003951935,0.00000919021,0.0001881535,0.0007388548],"genre_scores_gemma":[0.9986834,0.000009321246,0.0006154859,0.00001681022,0.00003830896,0.0001639173,0.00002159347,0.000009340105,0.0004418569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5417001,"threshold_uncertainty_score":0.365072,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02803851616073795,"score_gpt":0.3013276196534549,"score_spread":0.273289103492717,"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."}}