{"id":"W4224213480","doi":"10.1038/s43856-022-00107-6","title":"Automated bone marrow cytology using deep learning to generate a histogram of cell types","year":2022,"lang":"en","type":"article","venue":"Communications Medicine","topic":"Digital Imaging for Blood Diseases","field":"Computer Science","cited_by":112,"is_retracted":false,"has_abstract":true,"ca_institutions":"Juravinski Hospital; McMaster University; University of Waterloo","funders":"","keywords":"Bone marrow; Cytology; Histogram; Hematology; Pathology; Medicine; Artificial intelligence; Computer science; Internal medicine; Image (mathematics)","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":[],"consensus_categories":[],"category_scores_codex":[0.0002832294,0.0000941582,0.0002074784,0.0002782146,0.0003346681,0.00002030448,0.001795428,0.00001421355,0.00003653826],"category_scores_gemma":[0.0001826112,0.00009744634,0.00003534925,0.0009474506,0.0001906971,0.0001376209,0.001800859,0.0001677083,0.00001180584],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008290791,"about_ca_system_score_gemma":0.00008193854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001423967,"about_ca_topic_score_gemma":0.000009308421,"domain_scores_codex":[0.9988945,0.0002389465,0.0002915252,0.0001875674,0.000211063,0.0001764168],"domain_scores_gemma":[0.9979175,0.0001453217,0.0001640992,0.001542336,0.000126019,0.0001047647],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001113995,0.00508378,0.01251544,0.0003333355,0.0003432097,0.0001593689,0.03326805,0.1592348,0.4583209,0.07190782,0.03373975,0.2249822],"study_design_scores_gemma":[0.0006421884,0.0003759864,0.0007399472,0.00004228061,0.00006641805,0.00008375144,0.0005648408,0.9436688,0.001033806,0.0006131485,0.05194565,0.0002232077],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6912731,0.04507107,0.1730385,0.03523039,0.001361065,0.00139493,0.00003624618,0.004089043,0.04850568],"genre_scores_gemma":[0.9123647,0.000026911,0.0867845,0.0004734553,0.00001090557,0.00003499243,0.00003534048,0.00001195706,0.0002572052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.784434,"threshold_uncertainty_score":0.3973746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03541178120886098,"score_gpt":0.3038375492206277,"score_spread":0.2684257680117668,"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."}}