{"id":"W4366089059","doi":"10.3390/app13085012","title":"Comparative Analysis of Supervised Machine and Deep Learning Algorithms for Kyphosis Disease Detection","year":2023,"lang":"en","type":"article","venue":"Applied Sciences","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Princess Nourah Bint Abdulrahman University","keywords":"Hyperparameter; Kyphosis; Support vector machine; Artificial intelligence; Naive Bayes classifier; Machine learning; Computer science; Random forest; Cross-validation; Logistic regression; Artificial neural network; Statistics; Medicine; Mathematics; Surgery","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":[0.0002991107,0.00006454615,0.0001988566,0.0003238412,0.0001462487,0.0000267482,0.00007540377,0.00001417748,0.00001611845],"category_scores_gemma":[0.00002653404,0.00005316797,0.00005912627,0.001930554,0.0001365672,0.00004073936,0.00001505986,0.00004623512,0.00000281784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000005944216,"about_ca_system_score_gemma":0.000004370083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002326651,"about_ca_topic_score_gemma":0.00002268454,"domain_scores_codex":[0.9994217,0.00001003348,0.0001189637,0.0001550097,0.0001608551,0.0001334628],"domain_scores_gemma":[0.9996737,0.0001566887,0.00002122964,0.0000519934,0.00001545085,0.00008091395],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001149658,0.00002693153,0.01914625,0.0001290623,0.001022695,0.000001067796,0.002934006,0.7021015,0.04866161,0.0003361148,0.00005399548,0.2255753],"study_design_scores_gemma":[0.00008416054,0.000007952786,0.01086997,0.000002991357,0.0003243732,3.19929e-8,0.0005519594,0.9848103,0.003087326,0.0001039129,0.00009247637,0.00006454151],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9215898,0.0003258538,0.07726488,0.00005330491,0.00003949925,0.00008770176,0.00001081646,0.000176738,0.0004514146],"genre_scores_gemma":[0.9989783,0.00006133994,0.0008707822,0.000009577207,0.00001267218,0.00002728072,0.00001748914,0.000002974844,0.00001960737],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2827089,"threshold_uncertainty_score":0.2168127,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02677913621300018,"score_gpt":0.2826946303811358,"score_spread":0.2559154941681356,"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."}}