{"id":"W3159034163","doi":"10.1016/j.cocis.2021.101463","title":"Bursting microbubbles: How nanobubble contrast agents can enable the future of medical ultrasound molecular imaging and image-guided therapy","year":2021,"lang":"en","type":"article","venue":"Current Opinion in Colloid & Interface Science","topic":"Ultrasound and Hyperthermia Applications","field":"Engineering","cited_by":107,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Natural Sciences and Engineering Research Council of Canada; Wallace H. Coulter Foundation; U.S. Department of Defense","keywords":"Microbubbles; Ultrasound; Contrast (vision); Ultrasound imaging; Field (mathematics); Contrast-enhanced ultrasound; Computer science; Medical imaging; Image contrast; Biomedical engineering; Medical physics; Medicine; Radiology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005715601,0.000178773,0.0001989304,0.00009501608,0.0002001341,0.0002595322,0.0006077079,0.000047279,0.00006048074],"category_scores_gemma":[0.0001491356,0.000144166,0.00004669421,0.0008569384,0.000528564,0.0002129035,0.00009868498,0.0002958293,0.000002094474],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009218175,"about_ca_system_score_gemma":0.000271659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003110469,"about_ca_topic_score_gemma":0.00002159412,"domain_scores_codex":[0.9984271,0.00004625313,0.0002770775,0.0003569971,0.0005182136,0.0003743741],"domain_scores_gemma":[0.999173,0.0001385376,0.00006491727,0.0003182869,0.0001805104,0.0001246806],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000002488452,0.00005800847,0.002133102,0.0001043216,0.00001226673,8.893492e-7,0.00150324,0.0005656707,0.9875073,0.0004821645,0.002330201,0.005300297],"study_design_scores_gemma":[0.001790523,0.00003059108,0.002481581,0.001176881,0.00001502639,0.0002472962,0.0101571,0.02112649,0.8432986,0.0005222808,0.1185054,0.0006482661],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9352891,0.04691353,0.008016024,0.004842433,0.003826572,0.0005219664,0.0000499003,0.0000990898,0.0004413984],"genre_scores_gemma":[0.9931507,0.006141836,0.0004434307,0.00005435947,0.0001238484,0.00004071811,0.00000759857,0.00001868958,0.00001886769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1442088,"threshold_uncertainty_score":0.587892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02172452743295613,"score_gpt":0.3039775168009344,"score_spread":0.2822529893679783,"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."}}