{"id":"W4232421506","doi":"10.1093/ibd/izaa347.023","title":"MACHINE LEARNING FOR CROHN’S DISEASE PHENOTYPE MODELING USING BIOPSY IMAGES","year":2021,"lang":"en","type":"article","venue":"Inflammatory Bowel Diseases","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Phenotype; Biopsy; Artificial intelligence; Medicine; H&E stain; Pathology; Radiology; Computer science; Machine learning; Immunohistochemistry; Gene; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0005941698,0.0006046915,0.0003558265,0.0006216846,0.0001430934,0.0004536972,0.0004484945,0.0006247242,0.0009477721],"category_scores_gemma":[0.001338215,0.0002299943,0.0005684112,0.0002724256,0.0001814586,0.0003048881,0.0002358199,0.0004971744,0.000257189],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006169098,"about_ca_system_score_gemma":0.0003909749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00859483,"about_ca_topic_score_gemma":0.007335216,"domain_scores_codex":[0.9998577,0.00003655428,0.000009426231,0.00005154478,0.0000217402,0.00002312099],"domain_scores_gemma":[0.9995503,0.0002375982,0.00005417352,0.00003540002,0.00009972269,0.00002275536],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003845472,0.0002867904,0.04424079,0.00007473588,0.0001201334,0.000410409,0.00005581563,0.7467961,0.01399813,0.0007617096,0.00207654,0.1907943],"study_design_scores_gemma":[0.000002627707,0.00002388263,0.001634379,0.000003574253,0.000004244263,0.00002603922,0.000004851212,0.996806,0.001148431,0.0002367169,0.0001070532,0.000002221977],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7422895,0.001102338,0.2508265,0.0007136727,0.00009042396,0.0001725233,0.0010535,0.001834641,0.001916924],"genre_scores_gemma":[0.9615098,0.0001576646,0.03611197,0.00008576967,0.00002247551,0.00006123594,0.0007123941,0.00002299249,0.00131557],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00859483,"threshold_uncertainty_score":0.01708961,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02405293412529603,"score_gpt":0.2797838170657637,"score_spread":0.2557308829404677,"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."}}