{"id":"W4323352910","doi":"10.1093/jcag/gwac036.108","title":"A108 AUTOMATED DETECTION OF ILEOCECAL VALVE, APPENDICEAL ORIFICE, AND POLYP DURING COLONOSCOPY USING A DEEP LEARNING MODEL","year":2023,"lang":"en","type":"article","venue":"Journal of the Canadian Association of Gastroenterology","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Colonoscopy; Artificial intelligence; Ileocecal valve; Medicine; Deep learning; Convolutional neural network; Computer science; Radiology; Internal medicine; Colorectal cancer; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005200501,0.0006004975,0.0004058091,0.0007721135,0.0002192079,0.0004757169,0.0005891998,0.0006780114,0.0008812664],"category_scores_gemma":[0.001248365,0.0002748778,0.0004710664,0.0003070268,0.0001629421,0.0003684023,0.0004504798,0.0005155174,0.000368218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009448337,"about_ca_system_score_gemma":0.0008873127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02031303,"about_ca_topic_score_gemma":0.02168225,"domain_scores_codex":[0.9997633,0.00003062575,0.00001503463,0.0000954574,0.00004389029,0.00005173729],"domain_scores_gemma":[0.9995852,0.0001307454,0.00004527942,0.00003811411,0.0001639311,0.00003669945],"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":[0.001073133,0.0008121812,0.05050491,0.0002126552,0.000219948,0.0006389128,0.0001435949,0.2926663,0.05942536,0.0006828129,0.007365661,0.5862544],"study_design_scores_gemma":[0.000007047287,0.00007913439,0.00507135,0.00001346431,0.0000187335,0.00005104201,0.00001188264,0.9892169,0.005125256,0.0001092987,0.0002878193,0.000008038145],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7736031,0.001105077,0.2175935,0.0004217677,0.0001487866,0.0001832143,0.001689707,0.002384769,0.002870105],"genre_scores_gemma":[0.942582,0.0002146099,0.052465,0.0001154566,0.00002493542,0.00008932367,0.001680656,0.00002927335,0.00279883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02031303,"threshold_uncertainty_score":0.0403896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01055242978790686,"score_gpt":0.2501414566370531,"score_spread":0.2395890268491463,"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."}}