{"id":"W4412491626","doi":"10.1007/s00586-025-09073-8","title":"Lumbar Spinal Stenosis Detection from Sagittal and Axial MR Images using Hybrid of Deep Kronecker Network and SpinalNet","year":2025,"lang":"en","type":"article","venue":"European Spine Journal","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Sagittal plane; Lumbar spinal stenosis; Medicine; Magnetic resonance imaging; Spinal stenosis; Radiology; Lumbar; Segmentation; Artificial intelligence; Computer science","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":[0.0003749868,0.000539601,0.0005494467,0.001113245,0.0002486742,0.0004539845,0.0004390097,0.0005809493,0.0008154191],"category_scores_gemma":[0.0007802106,0.0002286213,0.0003062655,0.000445959,0.0001542404,0.0005169875,0.0005740238,0.0003392939,0.0002541312],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002188572,"about_ca_system_score_gemma":0.0005512593,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007455878,"about_ca_topic_score_gemma":0.01523607,"domain_scores_codex":[0.9998595,0.00002402634,0.00001201147,0.00004141806,0.00003529469,0.00002781125],"domain_scores_gemma":[0.9998091,0.00005621689,0.00002109875,0.00001558427,0.00007777955,0.00002018349],"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":[0.0009217774,0.000524655,0.03850573,0.0002851469,0.0002488747,0.0007681281,0.00018056,0.1191294,0.03128411,0.001904321,0.007737271,0.79851],"study_design_scores_gemma":[0.00001501494,0.0001117765,0.009570989,0.00002376802,0.00004891143,0.0002925526,0.00006284846,0.9824308,0.005189786,0.001449251,0.000788064,0.00001625056],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5292814,0.00231486,0.4607434,0.0006398716,0.0001703047,0.0001388239,0.001306786,0.002100956,0.003303623],"genre_scores_gemma":[0.9272743,0.0006759994,0.06814276,0.0001294774,0.00008402027,0.00005441658,0.001168182,0.00005557035,0.00241522],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007455878,"threshold_uncertainty_score":0.01482499,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007481625594214227,"score_gpt":0.2227017208998412,"score_spread":0.215220095305627,"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."}}