2024
Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan
Faghani S, Nicholas R, Patel S, Baffour F, Moassefi M, Rouzrokh P, Khosravi B, Powell G, Leng S, Glazebrook K, Erickson B, Tiegs-Heiden C. Development of a deep learning model for the automated detection of green pixels indicative of gout on dual energy CT scan. Research In Diagnostic And Interventional Imaging 2024, 9: 100044. PMID: 39076582, PMCID: PMC11265492, DOI: 10.1016/j.redii.2024.100044.Peer-Reviewed Original ResearchDice similarity coefficientDual-energy CTDeep learningDual-energy CT datasetsCurrent image processing methodsDual-energy CT imagingOptimal deep learningAverage Dice similarity coefficientAutomated detectionComputer-aided diagnosis toolDeep learning modelsDual-energy CT scansBackground pixelsSuperior performance metricsHeld-out testMaterial decompositionSegmentation metricsDL modelsPerformance metricsTraining setLearning modelsSegmentation approachPixelPresence of monosodium urate (MSUSuperior performance
2018
Identification of monosodium urate crystal deposits in patients with asymptomatic hyperuricemia using dual-energy CT
Wang P, Smith SE, Garg R, Lu F, Wohlfahrt A, Campos A, Vanni K, Yu Z, Solomon DH, Kim SC. Identification of monosodium urate crystal deposits in patients with asymptomatic hyperuricemia using dual-energy CT. RMD Open 2018, 4: e000593. PMID: 29556417, PMCID: PMC5856918, DOI: 10.1136/rmdopen-2017-000593.Peer-Reviewed Original ResearchMSU crystal depositsAsymptomatic hyperuricemiaMonosodium urate crystal depositsUrate crystal depositsSUA levelsCrystal depositsNon-interventional cross-sectional studyDECT scansOlder ageSerum uric acid levelsUnivariable logistic regression analysisMean SUA levelCent of patientsUric acid levelsCross-sectional studyMean total volumeLogistic regression analysisDual-energy CT scansMetabolic syndromePatient factorsUnivariable analysisMean ageClinical significanceCT scanPatients
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