{"id":"W4298326948","doi":"10.1111/jgh.16011","title":"Artificial intelligence and colon capsule endoscopy: Automatic detection of ulcers and erosions using a convolutional neural network","year":2022,"lang":"en","type":"article","venue":"Journal of Gastroenterology and Hepatology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Emergent BioSolutions (Canada)","funders":"","keywords":"Capsule endoscopy; Medicine; Convolutional neural network; Artificial intelligence; Colonoscopy; Image processing; Diagnostic accuracy; Pattern recognition (psychology); Gastroenterology; Image (mathematics); Radiology; Internal medicine; Computer science; Colorectal cancer","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":[0.0003108389,0.00007701069,0.0003375932,0.0001548915,0.0002010672,0.000004017857,0.00002694202,0.00005539566,0.00002441752],"category_scores_gemma":[0.00004396461,0.00007341217,0.00004268487,0.00008209443,0.0002490928,0.00004357413,0.00006469256,0.0003348839,5.095316e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005233239,"about_ca_system_score_gemma":0.0000505803,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005719025,"about_ca_topic_score_gemma":0.0001515374,"domain_scores_codex":[0.9990757,0.0001926573,0.0003632594,0.0001129938,0.00009425797,0.0001612069],"domain_scores_gemma":[0.9994508,0.00006813404,0.0002990703,0.00004218684,0.00006130231,0.00007851954],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.03488203,0.0002109863,0.88488,0.0001284717,0.0003096517,0.0001610393,0.000755077,0.003014032,0.06272318,0.0001899919,0.00007533859,0.0126702],"study_design_scores_gemma":[0.002587846,0.06549232,0.3837798,0.0001052289,0.0008457765,0.06850199,0.001257266,0.4714209,0.003288197,0.002455667,0.0001015502,0.000163473],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9931333,0.0006144131,0.005154108,0.0005125125,0.0004865544,0.00008642063,0.000003748683,0.000006530655,0.000002377423],"genre_scores_gemma":[0.9986598,0.00006631813,0.0009593822,0.0002058611,0.00009720204,0.000004127664,8.907413e-7,0.000004961202,0.00000142106],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5011002,"threshold_uncertainty_score":0.2993661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03078672175573141,"score_gpt":0.2776713930459546,"score_spread":0.2468846712902232,"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."}}