Featured Publications
Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas
Tillmanns N, Lost J, Tabor J, Vasandani S, Vetsa S, Marianayagam N, Yalcin K, Erson-Omay E, von Reppert M, Jekel L, Merkaj S, Ramakrishnan D, Avesta A, de Oliveira Santo I, Jin L, Huttner A, Bousabarah K, Ikuta I, Lin M, Aneja S, Turowski B, Aboian M, Moliterno J. Application of novel PACS-based informatics platform to identify imaging based predictors of CDKN2A allelic status in glioblastomas. Scientific Reports 2023, 13: 22942. PMID: 38135704, PMCID: PMC10746716, DOI: 10.1038/s41598-023-48918-4.Peer-Reviewed Original ResearchConceptsInformatics platformDeep learning algorithmsImaging featuresCDKN2A alterationsLearning algorithmHeterozygous lossHomozygous deletionLarge datasetsDeep white matter invasionGBM molecular subtypesNew informaticsQualitative imaging biomarkersWhole-exome sequencingQualitative imaging featuresGBM resectionRadiographic evidenceWorse prognosisPACSMolecular subtypesPial invasionImaging biomarkersCDKN2A mutationsAllele statusNoninvasive identificationMagnetic resonance imagesPredicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning
Kucukkaya A, Zeevi T, Chai N, Raju R, Haider S, Elbanan M, Petukhova-Greenstein A, Lin M, Onofrey J, Nowak M, Cooper K, Thomas E, Santana J, Gebauer B, Mulligan D, Staib L, Batra R, Chapiro J. Predicting tumor recurrence on baseline MR imaging in patients with early-stage hepatocellular carcinoma using deep machine learning. Scientific Reports 2023, 13: 7579. PMID: 37165035, PMCID: PMC10172370, DOI: 10.1038/s41598-023-34439-7.Peer-Reviewed Original Research
2025
Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms
Aminorroaya A, Dhingra L, Pedroso A, Shankar S, Coppi A, Khunte A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Development and Multinational Validation of an Ensemble Deep Learning Algorithm for Detecting and Predicting Structural Heart Disease Using Noisy Single-lead Electrocardiograms. European Heart Journal - Digital Health 2025, ztaf034. DOI: 10.1093/ehjdh/ztaf034.Peer-Reviewed Original ResearchDetectable structural heart diseaseStructural heart diseaseCommunity-based screeningLeft-sided valvular diseaseHeart diseaseELSA-BrasilYale-New Haven HospitalAI-ECG algorithmDeep learning algorithmsPopulation-based cohortSevere LVHEchocardiographic dataPredictive biomarkersHospital-based sitesNew Haven HospitalRisk stratificationValvular diseaseEnsemble deep learning algorithmUK BiobankCommunity hospitalLead I ECGEnsemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Shankar S, Coppi A, Foppa M, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Ensemble Deep Learning Algorithm for Structural Heart Disease Screening Using Electrocardiographic Images PRESENT SHD. Journal Of The American College Of Cardiology 2025, 85: 1302-1313. PMID: 40139886, DOI: 10.1016/j.jacc.2025.01.030.Peer-Reviewed Original ResearchConceptsStructural heart diseaseYale-New Haven HospitalTransthoracic echocardiogramRisk stratificationHeart failureLeft-sided valvular diseaseSevere left ventricular hypertrophyLeft ventricular ejection fractionReceiver-operating characteristic curveVentricular ejection fractionLeft ventricular hypertrophyHeart disease screeningELSA-BrasilEnsemble deep learning algorithmRisk of deathConvolutional neural network modelEjection fractionEnsemble deep learning approachVentricular hypertrophyDeep learning algorithmsNew Haven HospitalDeep learning approachValvular diseaseNeural network modelClinical cohortArtificial Intelligence–Guided Lung Ultrasound by Nonexperts
Baloescu C, Bailitz J, Cheema B, Agarwala R, Jankowski M, Eke O, Liu R, Nomura J, Stolz L, Gargani L, Alkan E, Wellman T, Parajuli N, Marra A, Thomas Y, Patel D, Schraft E, O’Brien J, Moore C, Gottlieb M. Artificial Intelligence–Guided Lung Ultrasound by Nonexperts. JAMA Cardiology 2025, 10: 245-253. PMID: 39813064, PMCID: PMC11904735, DOI: 10.1001/jamacardio.2024.4991.Peer-Reviewed Original ResearchThis study shows AI helps non-experts create expert-quality lung ultrasound images, which may improve healthcare diagnostics access in underserved areas.
2024
Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation.
Hoebel K, Bridge C, Ahmed S, Akintola O, Chung C, Huang R, Johnson J, Kim A, Ly K, Chang K, Patel J, Pinho M, Batchelor T, Rosen B, Gerstner E, Kalpathy-Cramer J. Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation. Radiology Artificial Intelligence 2024, 6: e220231. PMID: 38197800, PMCID: PMC10831514, DOI: 10.1148/ryai.220231.Peer-Reviewed Original ResearchConceptsBrain tumor segmentationDeep learning algorithmsSegmentation qualityLearning algorithmsTumor segmentationBrain tumor segmentation algorithmQuantitative quality metricsTumor segmentation algorithmClinical expert evaluationSegmentation performanceAlgorithm evaluationSegmentation algorithmQuality metricsDice scoreHausdorff distanceSegmentation casesAlgorithmExperimental resultsExpert evaluationQuality evaluationMetricsSurvey article
2023
Artificial Intelligence–Enhanced Drug Discovery and the Achievement of Next-Generation Human-Centered Health System
Mbatha S, Mulaudzi T, Mbita Z, Adeola H, Batra J, Blenman K, Dlamini Z. Artificial Intelligence–Enhanced Drug Discovery and the Achievement of Next-Generation Human-Centered Health System. 2023, 155-177. DOI: 10.1007/978-3-031-36461-7_7.Peer-Reviewed Original ResearchArtificial intelligenceMachine learningUses of AIDeep learning algorithmsArtificial intelligence technologyDiscovery processIncorporation of AIInformation-intensive societyIntelligence technologyLearning algorithmHealth care systemHuman-centered societyVast amountComprehensive solutionDevelopment processDesign processChallenges of availabilityDrug discovery processCare systemClinical dataIntelligenceSociety 5.0Health needsPhysical spaceComputational modelArtificial intelligence in medical imaging for cholangiocarcinoma diagnosis: A systematic review with scientometric analysis
Njei B, Kanmounye U, Seto N, McCarty T, Mohan B, Fozo L, Navaneethan U. Artificial intelligence in medical imaging for cholangiocarcinoma diagnosis: A systematic review with scientometric analysis. Journal Of Gastroenterology And Hepatology 2023, 38: 874-882. PMID: 36919223, DOI: 10.1111/jgh.16180.Peer-Reviewed Original ResearchConceptsConvolutional neural networkArtificial intelligenceComputer visionMedical imagingRole of AIDeep learning algorithmsHigh performance metricsMachine learningSocial network analysisLearning algorithmNeural networkPerformance metricsRelational networksIntelligenceNetworkCholangiocarcinoma diagnosisScientometric analysisVisionNetwork analysisClassifierAlgorithmCollaborationLearningImagesMetrics
2022
Automated stain-free histomorphometry of peripheral nerve by contrast-enhancing techniques and artificial intelligence
Coto Hernández I, Mohan S, Jowett N. Automated stain-free histomorphometry of peripheral nerve by contrast-enhancing techniques and artificial intelligence. Journal Of Neuroscience Methods 2022, 375: 109598. PMID: 35436515, DOI: 10.1016/j.jneumeth.2022.109598.Peer-Reviewed Original ResearchConceptsDeep learning algorithmsDeep learningArtificial intelligenceMachine learningLearning algorithmImage processingImage reconstructionExisting methodsSegmentationPhase contrast imagesLearningContrast imagesImagesIntelligenceAlgorithmEmbeddingBrightfield microscopyTechniqueTransmission modalityProcessingApplications
2021
Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus
Harper JR, Cherukuri V, O’Reilly T, Yu M, Mbabazi-Kabachelor E, Mulando R, Sheth KN, Webb AG, Warf BC, Kulkarni AV, Monga V, Schiff SJ. Assessing the utility of low resolution brain imaging: treatment of infant hydrocephalus. NeuroImage Clinical 2021, 32: 102896. PMID: 34911199, PMCID: PMC8646178, DOI: 10.1016/j.nicl.2021.102896.Peer-Reviewed Original ResearchConceptsDeep learning enhancementLow-quality imagesDeep learningQuality imagesDeep learning algorithmsEnhanced imageRole of machineLearning enhancementImage qualityLearning algorithmAcceptable image qualityReconstruction errorImage resolutionImagesAlgorithmCT imagesLearningCT counterpartsNoise ratioTreatment planningPlanningNew standardStructural errorsExperienced pediatric neurosurgeonsMachineNIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS
Petersen G, Shatalov J, Brim W, Subramanian H, cui J, Johnson M, Malhotra A, Aboian M, Brackett A. NIMG-67. A SYSTEMATIC REVIEW ON THE DEVELOPMENT OF MACHINE LEARNING MODELS FOR DIFFERENTIATING PCNSL FROM GLIOMAS. Neuro-Oncology 2021, 23: vi144-vi145. PMCID: PMC8598874, DOI: 10.1093/neuonc/noab196.565.Peer-Reviewed Original ResearchMachine learningDL algorithmsApplication of MLDeep learning algorithmsConvolutional neural networkMachine learning modelsSupport vector machineRisk of overfittingArtificial intelligenceLearning algorithmML algorithmsNeural networkVector machineLearning modelLarge datasetsNovel DLInternal datasetML methodsAlgorithmAverage AUCSearch strategyDatasetPromising resultsLearningRelated termsExtracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning
Du J, Xiang Y, Sankaranarayanapillai M, Zhang M, Wang J, Si Y, Pham H, Xu H, Chen Y, Tao C. Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning. Journal Of The American Medical Informatics Association 2021, 28: 1393-1400. PMID: 33647938, PMCID: PMC8279785, DOI: 10.1093/jamia/ocab014.Peer-Reviewed Original ResearchConceptsDeep learning algorithmsLearning-based methodsVaccine Adverse Event Reporting SystemLearning algorithmArt deep learning algorithmsDeep learning-based methodsConventional machine learning-based methodsMachine learning-based methodsConventional machine learningAdverse Event Reporting SystemGuillain-Barré syndromeLarge modelsAdverse eventsEvent Reporting SystemVAERS reportsDeep learningMachine learningEntity recognitionPeer modelInfluenza vaccine safetyNervous system disordersExact matchVaccine adverse eventsSafety reportsReporting system
2020
Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning
Bousabarah K, Letzen B, Tefera J, Savic L, Schobert I, Schlachter T, Staib LH, Kocher M, Chapiro J, Lin M. Automated detection and delineation of hepatocellular carcinoma on multiphasic contrast-enhanced MRI using deep learning. Abdominal Radiology 2020, 46: 216-225. PMID: 32500237, PMCID: PMC7714704, DOI: 10.1007/s00261-020-02604-5.Peer-Reviewed Original ResearchConceptsDeep convolutional neural networkAverage false positive rateDice similarity coefficientU-NetDeep learning algorithmsConvolutional neural networkTest setMean Dice similarity coefficientRandom forest classifierDCNN methodDCNN approachDeep learningNet architectureLearning algorithmNeural networkLiver segmentationManual 3D segmentationForest classifierGround truthManual segmentationFalse positive rateCorresponding segmentationSegmentationMultiphasic contrast-enhanced MRIThresholdingHow Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening
Shung DL, Byrne MF. How Artificial Intelligence Will Impact Colonoscopy and Colorectal Screening. Gastrointestinal Endoscopy Clinics Of North America 2020, 30: 585-595. PMID: 32439090, DOI: 10.1016/j.giec.2020.02.010.Peer-Reviewed Original ResearchConceptsArtificial intelligenceArtificial intelligence-based technologiesDeep learning algorithmsComputer-assisted diagnosisComputer-assisted detectionLearning algorithmCenter efficiencyIntelligenceUnnecessary costsKey challengesColorectal screeningWorkflowDetection rateLow-risk polypsAdenoma detection rateTechnologyQuality of screeningTreatment of cancerInterpretabilityGastrointestinal tractAlgorithmClinical integrationCostPolypsDiagnosis
2019
Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization
Onofrey JA, Casetti-Dinescu DI, Lauritzen AD, Sarkar S, Venkataraman R, Fan RE, Sonn GA, Sprenkle PC, Staib LH, Papademetris X. Generalizable Multi-Site Training and Testing Of Deep Neural Networks Using Image Normalization. 2011 IEEE International Symposium On Biomedical Imaging: From Nano To Macro 2019, 00: 348-351. PMID: 32874427, PMCID: PMC7457546, DOI: 10.1109/isbi.2019.8759295.Peer-Reviewed Original ResearchDeep neural networksNeural networkDeep learning algorithmsProstate gland segmentationImage normalization methodGland segmentationLearning algorithmImage normalizationMulti-site dataIntensity normalization methodNormalization methodSingle-site dataAlgorithmNetworkPotential solutionsEquipment sourcesClinical adoptionSegmentationTrainingIntensity characteristicsRobustnessDataSite trainingMethodAdoption
2018
Deep networks in identifying CT brain hemorrhage
Helwan A, El-Fakhri G, Sasani H, Uzun Ozsahin D. Deep networks in identifying CT brain hemorrhage. Journal Of Intelligent & Fuzzy Systems 2018, Preprint: 1-1. DOI: 10.3233/jifs-172261.Peer-Reviewed Original ResearchConvolutional neural networkStacked autoencoderDeep networksMedical image classificationDeep learning algorithmsMedical expert's experienceImage classificationTraining timeLearning algorithmsNeural networkAutoencoderExpert experienceBrain CT imagesCT imagesNetworkHigher accuracyLess errorAlgorithmImagesAccuracyErrorClassificationArtificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability
Tajmir S, Lee H, Shailam R, Gale H, Nguyen J, Westra S, Lim R, Yune S, Gee M, Do S. Artificial intelligence-assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiology 2018, 48: 275-283. PMID: 30069585, DOI: 10.1007/s00256-018-3033-2.Peer-Reviewed Original ResearchConceptsBone age assessmentAutomated artificial intelligenceAI assistanceBone age radiographsConvolutional neural networkDeep learning algorithmsRoot mean square errorMean square errorPediatric radiologistsUtilization of AILearning algorithmsNeural networkArtificial intelligenceIntraclass correlation coefficientImproved performancePooled cohortRadiologist interpretationImaging studiesInter-rater variationAccuracyMetabolic disordersIncreased accuracyRadiologistsAge accuracyMeasures of accuracy
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