2022
Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology
Joel MZ, Umrao S, Chang E, Choi R, Yang DX, Duncan JS, Omuro A, Herbst R, Krumholz HM, Aneja S. Using Adversarial Images to Assess the Robustness of Deep Learning Models Trained on Diagnostic Images in Oncology. JCO Clinical Cancer Informatics 2022, 6: e2100170. PMID: 35271304, PMCID: PMC8932490, DOI: 10.1200/cci.21.00170.Peer-Reviewed Original Research
2013
Comparison of Clinical Interpretation With Visual Assessment and Quantitative Coronary Angiography in Patients Undergoing Percutaneous Coronary Intervention in Contemporary Practice
Nallamothu BK, Spertus JA, Lansky AJ, Cohen DJ, Jones PG, Kureshi F, Dehmer GJ, Drozda JP, Walsh MN, Brush JE, Koenig GC, Waites TF, Gantt DS, Kichura G, Chazal RA, O’Brien P, Valentine CM, Rumsfeld JS, Reiber JH, Elmore JG, Krumholz RA, Weaver WD, Krumholz HM. Comparison of Clinical Interpretation With Visual Assessment and Quantitative Coronary Angiography in Patients Undergoing Percutaneous Coronary Intervention in Contemporary Practice. Circulation 2013, 127: 1793-1800. PMID: 23470859, PMCID: PMC3908681, DOI: 10.1161/circulationaha.113.001952.Peer-Reviewed Original ResearchConceptsQuantitative coronary angiographyPercent diameter stenosisPercutaneous coronary interventionDiameter stenosisCoronary interventionCoronary angiographyCoronary lesionsClinical interpretationAngiographic interpretationStenosis severityHigher percent diameter stenosisMedian percent diameter stenosisElective percutaneous coronary interventionMean differenceCoronary stenosis severityIntermediate lesionsUS hospitalsStenosisLesionsAngiographyPatientsInterventionSeverityVisual assessmentSuch findings