2022
Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports
Torres-Lopez VM, Rovenolt GE, Olcese AJ, Garcia GE, Chacko SM, Robinson A, Gaiser E, Acosta J, Herman AL, Kuohn LR, Leary M, Soto AL, Zhang Q, Fatima S, Falcone GJ, Payabvash MS, Sharma R, Struck AF, Sheth KN, Westover MB, Kim JA. Development and Validation of a Model to Identify Critical Brain Injuries Using Natural Language Processing of Text Computed Tomography Reports. JAMA Network Open 2022, 5: e2227109. PMID: 35972739, PMCID: PMC9382443, DOI: 10.1001/jamanetworkopen.2022.27109.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBrain InjuriesHumansNatural Language ProcessingResearch ReportTomography, X-Ray ComputedConceptsNatural language processingF-scoreTest data setsLanguage processingIndependent test data setsData setsBidirectional Encoder RepresentationsAcute brain injuryLarge data setsHead CTBrain injuryNLP toolsF1 scoreNER modelTransformer architectureClinical textEncoder RepresentationsNLP algorithmNLP modelsCT reportsCustom dictionaryTraining setCross-validation performancePerformance metricsAvailable new toolsThe Need for Medical Artificial Intelligence That Incorporates Prior Images.
Acosta JN, Falcone GJ, Rajpurkar P. The Need for Medical Artificial Intelligence That Incorporates Prior Images. Radiology 2022, 304: 283-288. PMID: 35438563, DOI: 10.1148/radiol.212830.Peer-Reviewed Original ResearchConceptsArtificial intelligenceImage interpretation tasksCurrent AI algorithmsMedical artificial intelligenceBenchmark data setsFuture artificial intelligencePrioritization of tasksData setsAI algorithmsDynamic scenariosPrior imageInterpretation taskRelevant tasksSingle pointTaskIntelligenceSetArchitectureMachineAlgorithmCurationDevicesImagesScenariosInformation
2019
Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage
Dhar R, Falcone GJ, Chen Y, Hamzehloo A, Kirsch EP, Noche RB, Roth K, Acosta J, Ruiz A, Phuah CL, Woo D, Gill TM, Sheth KN, Lee JM. Deep Learning for Automated Measurement of Hemorrhage and Perihematomal Edema in Supratentorial Intracerebral Hemorrhage. Stroke 2019, 51: 648-651. PMID: 31805845, PMCID: PMC6993878, DOI: 10.1161/strokeaha.119.027657.Peer-Reviewed Original ResearchConceptsSupratentorial intracerebral hemorrhagePerihematomal edemaIntracerebral hemorrhagePHE volumeResults Two hundred twentyIntracerebral Hemorrhage (ERICH) studyMechanism of injuryIntracerebral hemorrhage patientsSpontaneous intracerebral hemorrhageLarge cohort studyComputed tomography scanSecondary outcomesCohort studyPrimary outcomeHemorrhage patientsSecondary injuryTomography scanHundred twentyMedian volumeLarge cohortHemorrhagePatientsQuantification of hemorrhageFirst cohortDisease biology