{"id":"W4396781314","doi":"10.2967/jnumed.124.267542","title":"Deep Learning–Enabled Quantification of<sup>99m</sup>Tc-Pyrophosphate SPECT/CT for Cardiac Amyloidosis","year":2024,"lang":"en","type":"article","venue":"Journal of Nuclear Medicine","topic":"Amyloidosis: Diagnosis, Treatment, Outcomes","field":"Biochemistry, Genetics and Molecular Biology","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University of Calgary","funders":"National Heart, Lung, and Blood Institute","keywords":"Cardiac amyloidosis; Pyrophosphate; Nuclear medicine; Radiochemistry; Medicine; Amyloidosis; Chemistry; Physics; Internal medicine; Nuclear magnetic resonance","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":[0.0006848608,0.0002418256,0.0005879751,0.0001923968,0.00008991362,0.0000299979,0.0002514141,0.00006992828,0.0001663807],"category_scores_gemma":[0.0005881879,0.0001813534,0.0004241957,0.0002213667,0.0001507634,0.00001745458,0.00005038342,0.000230323,0.00002643541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007428247,"about_ca_system_score_gemma":0.00007706386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002716306,"about_ca_topic_score_gemma":0.00000171676,"domain_scores_codex":[0.9982563,0.0001051119,0.0007168094,0.000307266,0.0003420109,0.000272504],"domain_scores_gemma":[0.9987239,0.0001466682,0.0004299201,0.0002948218,0.0002474539,0.0001572173],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001362367,0.0005655368,0.01799796,0.0008522142,0.004382121,0.0002909219,0.00160507,0.004729717,0.7860379,0.001273219,0.136,0.04490301],"study_design_scores_gemma":[0.003885109,0.005996844,0.009871558,0.0008654116,0.001859426,0.0003341456,0.003015299,0.003720103,0.1460725,0.000218253,0.82361,0.0005513499],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9635499,0.02822638,0.001021845,0.005273978,0.0009482754,0.0004083261,0.00001775701,0.00003186252,0.000521744],"genre_scores_gemma":[0.9886935,0.008782139,0.0007207366,0.0002577051,0.0008997666,0.00001074045,0.00003975413,0.00007995169,0.0005157485],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.68761,"threshold_uncertainty_score":0.7395378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01259238892374036,"score_gpt":0.276535249460697,"score_spread":0.2639428605369566,"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."}}