{"id":"W4401369518","doi":"10.2196/58776","title":"A Deep Learning Model to Predict Breast Implant Texture Types Using Ultrasonography Images: Feasibility Development Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Breast Implant and Reconstruction","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pixel; Artificial intelligence; Breast augmentation; Medicine; Receiver operating characteristic; Computer science; Breast implant; Texture (cosmology); Precision and recall; Implant; Pattern recognition (psychology); Computer vision; Surgery; Image (mathematics); Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001394461,0.001418247,0.0008042741,0.0009288113,0.0002160304,0.0006553518,0.001191171,0.001101371,0.001618686],"category_scores_gemma":[0.002415855,0.0003340584,0.0008529002,0.0005647369,0.0002138345,0.0006772391,0.0007374338,0.0009000872,0.0006561949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000810529,"about_ca_system_score_gemma":0.001347905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01209213,"about_ca_topic_score_gemma":0.008650166,"domain_scores_codex":[0.9996185,0.00008049014,0.00002278158,0.0001069258,0.00008889144,0.0000823268],"domain_scores_gemma":[0.9991725,0.0003147727,0.00005300045,0.00006780872,0.0003294008,0.00006238324],"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":[0.001543094,0.001972286,0.06139551,0.0003888963,0.0005303419,0.0005974437,0.0001096875,0.3997619,0.01442494,0.0006960172,0.01202358,0.5065563],"study_design_scores_gemma":[0.00002468963,0.0001838637,0.002461926,0.00001672797,0.00004956532,0.00005192365,0.00002412435,0.9946442,0.001960814,0.0001882327,0.0003840152,0.000009914188],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.854704,0.003077154,0.1316032,0.001028829,0.000321953,0.0004179176,0.002112509,0.00225587,0.004478614],"genre_scores_gemma":[0.9571875,0.0005792122,0.03656285,0.0003095696,0.00006050934,0.0002231754,0.002901432,0.00004497333,0.002130937],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.01209213,"threshold_uncertainty_score":0.0240435,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0676685830701474,"score_gpt":0.401447543531688,"score_spread":0.3337789604615406,"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."}}