{"id":"W2612758615","doi":"10.1186/s40942-017-0078-7","title":"Segmentation errors in macular ganglion cell analysis as determined by optical coherence tomography in eyes with macular pathology","year":2017,"lang":"en","type":"article","venue":"International Journal of Retina and Vitreous","topic":"Optical Coherence Tomography Applications","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Optical coherence tomography; Ganglion; Medicine; Ophthalmology; Outer plexiform layer; Inner plexiform layer; Segmentation; Retinal; Nerve fiber layer; Ganglion cell layer; Anatomy; 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":[0.001954174,0.0002538447,0.000232028,0.001911542,0.0002239559,0.0004104399,0.0002459825,0.0004591836,0.0004265324],"category_scores_gemma":[0.01252521,0.0002600126,0.0002263908,0.0009622392,0.0004231752,0.0006841935,0.0004884697,0.0002207778,0.0001113018],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002440601,"about_ca_system_score_gemma":0.0001812934,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000922174,"about_ca_topic_score_gemma":0.001238623,"domain_scores_codex":[0.9982947,0.0004622774,0.0003188652,0.00020256,0.0005926553,0.0001290464],"domain_scores_gemma":[0.9875587,0.004854982,0.00559394,0.0006307311,0.001130202,0.0002313683],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000297119,0.00001574083,0.9949263,0.00001689505,0.00003564692,0.0005869992,0.0001094682,0.00008074372,0.001135248,0.000009117517,0.00002377695,0.002762988],"study_design_scores_gemma":[0.000003599185,0.000158395,0.9939552,0.000008182334,0.00003287941,0.004418646,0.0001009867,0.0003115498,0.0009089878,0.00001807494,0.00007981223,0.000003769791],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9993998,0.0002363846,0.0002200562,0.000007113343,0.000001959854,0.000005365272,0.00002467655,0.000002995845,0.0001017341],"genre_scores_gemma":[0.9996025,0.00005783015,0.0002574351,0.000007591114,0.000004463526,0.000003419616,0.00004090639,0.000002223241,0.00002375052],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001954174,"threshold_uncertainty_score":0.01033473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005456151330351827,"score_gpt":0.2538509994186707,"score_spread":0.2483948480883189,"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."}}