Aline Pedroso, PhD
Associate Research ScientistAbout
Titles
Associate Research Scientist
Lead, Scientific Operations, Cardiovascular Data Science (CarDS) Lab
Biography
Dr. Aline Pedroso is a pharmacist and epidemiologist and is the Lead for Scientific Operations at the Cardiovascular Data Science (CarDS) Lab at the Yale School of Medicine. Her research focuses on large-scale studies on cardiovascular risk assessment, particularly in low-resource settings. She also works on studying existing and emerging practices and health policies and their effectiveness in improving patient outcomes. In addition, as the operations lead at the CarDS Lab, she is involved in the design and execution of multicenter studies that deploy digital health and artificial intelligence tools for improving the detection and prognostication of cardiovascular diseases. She manages a broad research portfolio at the CarDS Lab working with a large interdisciplinary team of clinician-scientists and data scientists for the design of innovative health technologies, and manages partnerships with academic, pharmaceutical, and industry partners.
Last Updated on June 02, 2025.
Departments & Organizations
- Internal Medicine
Education & Training
- Postdoctoral Research Associate
- Cardiovascular Data Science (CarDS) Lab (2024)
- PhD
- Federal University of Sao Joao del Rei, Health Sciences (2024)
- Postgraduate Associate
- Cardiovascular Data Science (CarDS) Lab (2023)
- MS
- Federal University of Sao Joal del Rei, Health Sciences (2019)
- BSc
- Federal University of Vales do Jequitinhonha e Mucuri, Pharmacy (2014)
Research
Publications
2025
The emerging role of AI in transforming cardiovascular care
Croon P, Pedroso A, Khera R. The emerging role of AI in transforming cardiovascular care. Future Cardiology 2025, ahead-of-print: 1-4. PMID: 40248957, DOI: 10.1080/14796678.2025.2492973.Peer-Reviewed Original ResearchArtificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms
Dhingra L, Aminorroaya A, Pedroso A, Khunte A, Sangha V, McIntyre D, Chow C, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Artificial Intelligence–Enabled Prediction of Heart Failure Risk From Single-Lead Electrocardiograms. JAMA Cardiology 2025, 10 PMID: 40238120, PMCID: PMC12004248, DOI: 10.1001/jamacardio.2025.0492.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFNew-onset HFHarrell's C-statisticProspective population-based cohortUK Biobank (UKBBrazilian Longitudinal StudyELSA-Brasil participantsC-statisticPopulation-based cohortIntegrated discrimination improvementReclassification improvementRisk of deathUKB participantsHealth systemRetrospective cohort studyDiscrimination improvementMain OutcomesLeft ventricular systolic dysfunctionHF riskUKBCohort studySingle-lead ECGIndependent of ageDevelopment 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 ECGAutomated Transformation of Unstructured Cardiovascular Diagnostic Reports into Structured Datasets Using Sequentially Deployed Large Language Model
Vasisht Shankar S, Dhingra LS, Aminorroaya A, Adejumo P, Nadkarni GN, Xu H, Brandt C, Oikonomou EK, Pedroso AF, Khera R. Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models. Eur Heart J Digit Health. 2025;ztaf030.Peer-Reviewed Original ResearchAutomated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models
Vasisht Shankar S, Dhingra L, Aminorroaya A, Adejumo P, Nadkarni G, Xu H, Brandt C, Oikonomou E, Pedroso A, Khera R. Automated transformation of unstructured cardiovascular diagnostic reports into structured datasets using sequentially deployed large language models. European Heart Journal - Digital Health 2025, ztaf030. DOI: 10.1093/ehjdh/ztaf030.Peer-Reviewed Original ResearchEnsemble 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 cohortHarnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care
Aminorroaya A, Biswas D, Pedroso A, Khera R. Harnessing Artificial Intelligence for Innovation in Interventional Cardiovascular Care. Journal Of The Society For Cardiovascular Angiography & Interventions 2025, 4: 102562. PMID: 40230673, PMCID: PMC11993883, DOI: 10.1016/j.jscai.2025.102562.Peer-Reviewed Original ResearchClinical careCommunity-based screening programCare quality outcomesPatient outcomesPatient-focused careHarness artificial intelligenceArtificial intelligencePotential of AIImprove patient outcomesIndividualized clinical careTransform careTransform clinical practiceCardiovascular careScreening programHealth dataQuality outcomesCareClinical workflowClinical tasksAcute coronary syndromeClinical practiceHeart diseaseAI-driven technologiesInterventionAI-enabledHarnessing artificial intelligence for innovation in interventional cardiovascular care
Aminorroaya A, Biswas D, Pedroso AF, Khera R. Harnessing artificial intelligence for innovation in interventional cardiovascular care. J Soc Cardiovasc Angiogr Interv. 2025;4:102562.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsAssessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study
Khera R, Sawano M, Warner F, Coppi A, Pedroso A, Spatz E, Yu H, Gottlieb M, Saydah S, Stephens K, Rising K, Elmore J, Hill M, Idris A, Montoy J, O’Laughlin K, Weinstein R, Venkatesh A, Weinstein R, Gottlieb M, Santangelo M, Koo K, Derden A, Gottlieb M, Gatling K, Ahmed Z, Gomez C, Guzman D, Hassaballa M, Jerger R, Kaadan A, Venkatesh A, Spatz E, Kinsman J, Malicki C, Lin Z, Li S, Yu H, Mannan I, Yang Z, Liu M, Venkatesh A, Spatz E, Ulrich A, Kinsman J, Malicki C, Dorney J, Pierce S, Puente X, Salah W, Nichol G, Stephens K, Anderson J, Schiffgens M, Morse D, Adams K, Stober T, Maat Z, O’Laughlin K, Gentile N, Geyer R, Willis M, Zhang Z, Chang G, Lyon V, Klabbers R, Ruiz L, Malone K, Park J, Rising K, Kean E, Chang A, Renzi N, Watts P, Kelly M, Schaeffer K, Grau D, Cheng D, Shutty C, Charlton A, Shughart L, Shughart H, Amadio G, Miao J, Hannikainen P, Elmore J, Wisk L, L’Hommedieu M, Chandler C, Eguchi M, Roldan K, Moreno R, Rodriguez R, Wang R, Montoy J, Kemball R, Chan V, Chavez C, Wong A, Arreguin M, Hill M, Site R, Kane A, Nikonowicz P, Sapp S, Idris A, McDonald S, Gallegos D, Martin K, Saydah S, Plumb I, Hall A, Briggs-Hagen M. Assessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study. Journal Of The American Medical Informatics Association 2025, 32: 784-794. PMID: 40036551, PMCID: PMC12012333, DOI: 10.1093/jamia/ocaf027.Peer-Reviewed Original ResearchElectronic health recordsSelf-report questionnairesSelf-reportHealth conditionsElectronic health record portalsElectronic health record platformsEHR elementsSelf-reported health conditionsElectronic health record dataSelf-reported conditionsAssessment of health conditionEvaluation of health conditionsPrevalence of conditionsPatient portalsTraditional self-reportPrevalence of comorbiditiesHealth recordsEHR dataEHR phenotypesDiagnosis codesHospitalization riskComputable phenotypeNationwide studyCohen's kappaPatient characteristicsHeart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study
Dhingra L, Aminorroaya A, Sangha V, Pedroso A, Asselbergs F, Brant L, Barreto S, Ribeiro A, Krumholz H, Oikonomou E, Khera R. Heart failure risk stratification using artificial intelligence applied to electrocardiogram images: a multinational study. European Heart Journal 2025, 46: 1044-1053. PMID: 39804243, DOI: 10.1093/eurheartj/ehae914.Peer-Reviewed Original ResearchYale New Haven Health SystemELSA-BrasilPCP-HFUK BiobankHF riskBrazilian Longitudinal Study of Adult HealthLongitudinal Study of Adult HealthBrazilian Longitudinal StudyRisk of new-onset HFPooled Cohort EquationsPrimary HF hospitalizationsHigher HF riskHarrell's C-statisticRisk of deathNew-onset HFCohort EquationsHealth systemComprehensive clinical evaluationAdult healthHeart failureIncident HFHF hospitalizationBaseline HFC-statisticPrevent HF
Academic Achievements & Community Involvement
News
News
- January 23, 2025
New AI Tool Identifies Risk of Future Heart Failure
- November 05, 2024
Yale Researchers at American Heart Association Scientific Session 2024
- August 26, 2024
Which Diabetes Meds Are Best for Reducing Heart Attack and Stroke Risk?
- April 01, 2024
Yale Faculty Present Groundbreaking Clinical Research at the 2024 American College of Cardiology Scientific Sessions
Get In Touch
Contacts
Lab Number
Locations
CarDS Lab
Academic Office
195 Church Street, Fl 6th
New Haven, CT 06510