{"id":"W3021233705","doi":"10.1016/j.joca.2020.05.002","title":"Automating three-dimensional osteoarthritis histopathological grading of human osteochondral tissue using machine learning on contrast-enhanced micro-computed tomography","year":2020,"lang":"en","type":"article","venue":"Osteoarthritis and Cartilage","topic":"AI in cancer detection","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Mount Sinai Hospital","funders":"Seventh Framework Programme; KAUTE-Säätiö; Sigrid Juséliuksen Säätiö; Academy of Finland; Oulun Yliopisto; H2020 European Research Council; Helsingin Yliopisto","keywords":"Osteoarthritis; Grading (engineering); Computed tomography; Contrast (vision); Medicine; Biomedical engineering; Radiology; Nuclear medicine; Computer science; Artificial intelligence; Pathology; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002552631,0.000346022,0.0005687872,0.0001649481,0.0005431907,0.0001581339,0.0003553435,0.0001622007,0.00003759887],"category_scores_gemma":[0.00005081074,0.0003709775,0.000152971,0.0004696749,0.0002064545,0.0004092529,0.000293638,0.0004646118,0.000007369953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005654532,"about_ca_system_score_gemma":0.00003413619,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003169805,"about_ca_topic_score_gemma":0.0000952624,"domain_scores_codex":[0.9974217,0.0002216707,0.0006032789,0.0008225275,0.0004504102,0.0004804261],"domain_scores_gemma":[0.9988998,0.0001150052,0.0003405217,0.0002994836,0.0001055,0.0002396463],"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":[0.00003654442,0.00002417833,0.00094919,0.00003985035,0.000003226617,0.0001041613,0.000515698,0.0002517232,0.9251073,0.0003139434,0.000009664282,0.07264452],"study_design_scores_gemma":[0.002546093,0.004521649,0.001013738,0.0004096928,0.00002419027,0.0002095873,0.00004477645,0.009876132,0.9803318,0.0002880808,0.000204499,0.0005297183],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805736,0.002121614,0.01572617,0.0002121712,0.0003615666,0.0003822561,0.00002578046,0.0003475661,0.0002492648],"genre_scores_gemma":[0.9884391,0.000008050991,0.01100635,0.0003063009,0.0001615061,0.00001962396,0.0000149814,0.00003252746,0.00001157737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0721148,"threshold_uncertainty_score":0.9998742,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646793862732827,"score_gpt":0.2349466956273016,"score_spread":0.2184787569999734,"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."}}