{"id":"W3120044285","doi":"10.1016/j.ajo.2020.12.034","title":"Quantification of Key Retinal Features in Early and Late Age-Related Macular Degeneration Using Deep Learning","year":2021,"lang":"en","type":"article","venue":"American Journal of Ophthalmology","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Novartis Pharmaceuticals Canada; Heidelberg Engineering; Radboud Universiteit; Allergan; Deutsche Forschungsgemeinschaft; Bayer; Novartis Pharmaceuticals Corporation; National Institute for Health and Care Research; Novartis; Wellcome Trust; Roche","keywords":"Artificial intelligence; Receiver operating characteristic; Segmentation; Intraclass correlation; Sørensen–Dice coefficient; Deep learning; Macular degeneration; False positive paradox; Computer science; Pattern recognition (psychology); Feature (linguistics); Medicine; Image segmentation; Machine learning; Ophthalmology; Statistics; Reproducibility; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003776578,0.00007886932,0.0004248132,0.0002335845,0.0000330911,0.00001267378,0.0000382325,0.00004121461,0.00003060959],"category_scores_gemma":[0.0002406427,0.00007038897,0.00008825274,0.0004336014,0.0002103347,0.00005554482,0.00001417304,0.0003288616,5.586233e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002980525,"about_ca_system_score_gemma":0.00006515448,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002310193,"about_ca_topic_score_gemma":0.000001353836,"domain_scores_codex":[0.9988674,0.0003177315,0.0004274775,0.0001279442,0.0001415727,0.0001179044],"domain_scores_gemma":[0.9990218,0.00006133304,0.0005191907,0.00008435847,0.0002535788,0.00005971882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000199492,0.00007076615,0.5189531,0.00002331794,0.0001437764,0.005946814,0.000854426,0.003710715,0.4646466,0.00004719867,0.000001176932,0.005402559],"study_design_scores_gemma":[0.0006357977,0.0008378136,0.9349585,0.0001827779,0.0002842895,0.04708077,0.0008310672,0.004382587,0.01061082,0.00009436542,0.00001092361,0.0000903558],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977477,0.001171181,0.0004774197,0.0003902123,0.00003694516,0.00002325793,2.759195e-7,0.000002191844,0.0001508376],"genre_scores_gemma":[0.9924763,0.00009441449,0.007218487,0.00001562378,0.00003006268,2.633523e-7,0.000007405049,0.00000989302,0.0001475596],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4540358,"threshold_uncertainty_score":0.2870379,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0190692269694701,"score_gpt":0.3122160246638718,"score_spread":0.2931467976944017,"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."}}