{"id":"W4412815123","doi":"10.35882/jeeemi.v7i3.868","title":"Improving Kidney Stone Detection with YOLOV10 and Channel Attention Mechanisms in Medical Imaging","year":2025,"lang":"en","type":"article","venue":"Journal of Electronics Electromedical Engineering and Medical Informatics","topic":"Advanced X-ray and CT Imaging","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Kidney stones; Channel (broadcasting); Medicine; Computer science; Internal medicine; Telecommunications","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":[0.001092051,0.0002339524,0.0004158917,0.0004921769,0.0000592689,0.00004200777,0.0001734138,0.0002137546,0.000009298416],"category_scores_gemma":[0.0006901171,0.000195201,0.00004565477,0.0004479967,0.0000840612,0.0003906175,0.00004698014,0.001566218,3.04217e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002018283,"about_ca_system_score_gemma":0.0003558856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003363912,"about_ca_topic_score_gemma":0.000009936601,"domain_scores_codex":[0.9977175,0.00001824682,0.0008105229,0.00009980166,0.000763101,0.0005908411],"domain_scores_gemma":[0.9990025,0.0001360317,0.0001210175,0.0000901431,0.00007059336,0.0005796901],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002763987,0.0002250047,0.0001841149,0.003412439,0.0005907569,0.0004379397,0.001201938,0.013513,0.03308555,0.009495538,0.0003474061,0.9372299],"study_design_scores_gemma":[0.001892315,0.0002291436,0.00008707937,0.001019429,0.00005395923,0.001363041,0.0001946969,0.9913016,0.001927197,0.0007634479,0.0009492806,0.0002188226],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1151372,0.002954248,0.8808219,0.0005399392,0.0002765814,0.00009395234,6.535375e-7,0.00008831572,0.0000872767],"genre_scores_gemma":[0.9928158,0.003204176,0.003316806,0.0005059862,0.0001059355,0.000009036097,0.000003243576,0.00002925133,0.000009748005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9777886,"threshold_uncertainty_score":0.7960067,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.001326964061861807,"score_gpt":0.1910912440452651,"score_spread":0.1897642799834033,"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."}}