{"id":"W3208374152","doi":"10.1016/j.ijrobp.2021.07.485","title":"Evaluating Clinical Acceptability of Organs-at-Risk Segmentation in Head & Neck Cancer (HNC) by Open-Source 3D Convolutional Neural Networks (CNNs)","year":2021,"lang":"en","type":"article","venue":"International Journal of Radiation Oncology*Biology*Physics","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"","keywords":"Medicine; Segmentation; Artificial intelligence; Convolutional neural network; Wilcoxon signed-rank test; Deep learning; Hausdorff distance; Pyramid (geometry); Pattern recognition (psychology); Radiation treatment planning; Dice; Radiology; Computer science; Radiation therapy; Statistics","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":[0.002508411,0.0004992385,0.0004226039,0.0007158959,0.0002551314,0.001230197,0.0005450163,0.001138475,0.001348077],"category_scores_gemma":[0.01426791,0.0002194937,0.0005700296,0.000298758,0.0003438068,0.0004951422,0.0008934329,0.0004242314,0.0005732425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006397317,"about_ca_system_score_gemma":0.0005224075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008056655,"about_ca_topic_score_gemma":0.01196786,"domain_scores_codex":[0.9987747,0.0003998717,0.000110319,0.0002956402,0.0003106961,0.0001086779],"domain_scores_gemma":[0.9962332,0.0024513,0.0003163779,0.0002160267,0.0005624844,0.0002206566],"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":[0.005302994,0.0002841333,0.6002233,0.0005146906,0.0006964979,0.000786876,0.0007344392,0.0658125,0.02034337,0.0007210624,0.008528077,0.2960521],"study_design_scores_gemma":[0.0001208973,0.001528745,0.4264058,0.0001960786,0.0006840996,0.00234668,0.001252101,0.5273345,0.02999202,0.001915835,0.008088358,0.000134937],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9830285,0.001737098,0.009228015,0.0005496027,0.0001118219,0.00008286735,0.001758226,0.0004851813,0.003018697],"genre_scores_gemma":[0.9936485,0.0002153317,0.003272791,0.0001010221,0.00003082841,0.00001989764,0.001943941,0.00009359313,0.0006741515],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008056655,"threshold_uncertainty_score":0.01601946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05045488354550652,"score_gpt":0.4189375594159048,"score_spread":0.3684826758703983,"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."}}