{"id":"W4388590067","doi":"10.1093/neuonc/noad179.0613","title":"INNV-24. MACHINE LEARNING-BASED SPECTROSCOPIC TISSUE DIFFERENTIATION IN FLUORESCENCE-GUIDED NEUROSURGERY","year":2023,"lang":"en","type":"article","venue":"Neuro-Oncology","topic":"Optical Imaging and Spectroscopy Techniques","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Principal component analysis; Computer science; Pattern recognition (psychology); Hyperspectral imaging; Dimensionality reduction; Random forest; Fluorophore; Glioma; Fluorescence; Physics; Biology; Optics; Cancer research","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":[0.001518794,0.0006808618,0.0004468841,0.0007355406,0.0004039775,0.0009541638,0.0009844051,0.0009132304,0.006242445],"category_scores_gemma":[0.001554942,0.0002348558,0.0004162617,0.0005723021,0.0003102503,0.0004106081,0.000629431,0.0004655436,0.003393676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001045171,"about_ca_system_score_gemma":0.001264471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00646791,"about_ca_topic_score_gemma":0.008319692,"domain_scores_codex":[0.9992893,0.0001836131,0.0000293066,0.0001351762,0.00028715,0.00007534157],"domain_scores_gemma":[0.9994455,0.0001088704,0.00004230255,0.00007078914,0.0002660809,0.0000664406],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001122929,0.0002662246,0.008622025,0.0004936276,0.0001400376,0.0002924098,0.00006940911,0.1322371,0.05333928,0.005689761,0.09365909,0.7040681],"study_design_scores_gemma":[0.0001015594,0.000372948,0.01040101,0.00009889313,0.0000344972,0.0002830529,0.00006074643,0.8432152,0.07508959,0.007024903,0.0632402,0.00007740965],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2812644,0.01296153,0.5525056,0.003699313,0.00316906,0.001320607,0.01365318,0.03496998,0.09645639],"genre_scores_gemma":[0.6647169,0.002340757,0.2497026,0.0005216384,0.0004267815,0.0007768337,0.02349695,0.002481089,0.05553648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00646791,"threshold_uncertainty_score":0.02088308,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03009660723060128,"score_gpt":0.3440383126285565,"score_spread":0.3139417053979552,"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."}}