Featured Publications
Whole-cell organelle segmentation in volume electron microscopy
Heinrich L, Bennett D, Ackerman D, Park W, Bogovic J, Eckstein N, Petruncio A, Clements J, Pang S, Xu CS, Funke J, Korff W, Hess HF, Lippincott-Schwartz J, Saalfeld S, Weigel AV. Whole-cell organelle segmentation in volume electron microscopy. Nature 2021, 599: 141-146. PMID: 34616042, DOI: 10.1038/s41586-021-03977-3.Peer-Reviewed Original ResearchConceptsAutomatic reconstructionDeep learning architectureLearning architectureWeb repositoriesOpen dataAutomatic methodThree-dimensional reconstructionSuch methodsVolume electron microscopyQueriesSegmentationRepositoryArchitectureComputer codeSpatial interactionsDatasetReconstructionImagesMetricsCodeSuch reconstructionsMachine-guided design of cell-type-targeting cis-regulatory elements
Gosai S, Castro R, Fuentes N, Butts J, Mouri K, Alasoadura M, Kales S, Nguyen T, Noche R, Rao A, Joy M, Sabeti P, Reilly S, Tewhey R. Machine-guided design of cell-type-targeting cis-regulatory elements. Nature 2024, 634: 1211-1220. PMID: 39443793, PMCID: PMC11525185, DOI: 10.1038/s41586-024-08070-z.Peer-Reviewed Original ResearchConceptsCis-regulatory elementsCell typesActivation of off-target cellsGene expressionCell type-specific expressionSynthetic cis-regulatory elementsCell-type specificityHuman genomeUnique cell typeTissue identityBiotechnological applicationsTissue specificityIn vitro validationCell linesCre activitySequenceGenesNatural sequenceDevelopmental timeExpressionCellsGenomeTested in vivoMotifOff-target cellsscNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles
Zhu B, Wang Y, Ku L, van Dijk D, Zhang L, Hafler D, Zhao H. scNAT: a deep learning method for integrating paired single-cell RNA and T cell receptor sequencing profiles. Genome Biology 2023, 24: 292. PMID: 38111007, PMCID: PMC10726524, DOI: 10.1186/s13059-023-03129-y.Peer-Reviewed Original ResearchSevere aortic stenosis detection by deep learning applied to echocardiography
Holste G, Oikonomou E, Mortazavi B, Coppi A, Faridi K, Miller E, Forrest J, McNamara R, Ohno-Machado L, Yuan N, Gupta A, Ouyang D, Krumholz H, Wang Z, Khera R. Severe aortic stenosis detection by deep learning applied to echocardiography. European Heart Journal 2023, 44: 4592-4604. PMID: 37611002, PMCID: PMC11004929, DOI: 10.1093/eurheartj/ehad456.Peer-Reviewed Original ResearchConceptsSevere aortic stenosisUsing 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 ResearchMeSH KeywordsBreastDeep LearningHumansMagnetic Resonance ImagingMammographyTomography, X-Ray ComputedHistopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies.
Irshaid L, Bleiberg J, Weinberger E, Garritano J, Shallis RM, Patsenker J, Lindenbaum O, Kluger Y, Katz SG, Xu ML. Histopathologic and Machine Deep Learning Criteria to Predict Lymphoma Transformation in Bone Marrow Biopsies. Archives Of Pathology & Laboratory Medicine 2021, 146: 182-193. PMID: 34086849, DOI: 10.5858/arpa.2020-0510-oa.Peer-Reviewed Original ResearchConceptsLarge cell transformationChronic lymphocytic leukemiaBone marrow biopsyMarrow biopsyFollicular lymphomaIndolent B-cell lymphomaLarge lymphoma cellsClinical disease progressionDiagnosis of FLFinal outcome dataB-cell lymphomaLarge tumor cellsEase of procedureAggressive chemotherapyLow morbidityLymph nodesWorse prognosisWhole slide scansHistologic findingsPatient's probabilityDisease progressionLymphocytic leukemiaLymphoma transformationClinical questionsOutcome dataDeep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver
Oestmann PM, Wang CJ, Savic LJ, Hamm CA, Stark S, Schobert I, Gebauer B, Schlachter T, Lin M, Weinreb JC, Batra R, Mulligan D, Zhang X, Duncan JS, Chapiro J. Deep learning–assisted differentiation of pathologically proven atypical and typical hepatocellular carcinoma (HCC) versus non-HCC on contrast-enhanced MRI of the liver. European Radiology 2021, 31: 4981-4990. PMID: 33409782, PMCID: PMC8222094, DOI: 10.1007/s00330-020-07559-1.Peer-Reviewed Original ResearchConceptsNon-HCC lesionsHepatocellular carcinomaHCC lesionsAtypical imagingGrading systemLI-RADS criteriaAtypical imaging featuresPrimary liver cancerTypical hepatocellular carcinomaAtypical hepatocellular carcinomaContrast-enhanced MRISensitivity/specificityLiver transplantMethodsThis IRBRetrospective studyLiver malignanciesImaging featuresLiver cancerAtypical featuresConclusionThis studyLesionsMRIClinical applicationCarcinomaImage-based diagnosis
2025
Artificial 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.Deep learning to quantify the pace of brain aging in relation to neurocognitive changes
Yin C, Imms P, Chowdhury N, Chaudhari N, Ping H, Wang H, Bogdan P, Irimia A, Weiner M, Aisen P, Petersen R, Jack C, Jagust W, Landau S, Rivera-Mindt M, Okonkwo O, Shaw L, Lee E, Toga A, Beckett L, Harvey D, Green R, Saykin A, Nho K, Perrin R, Tosun D, Sachdev P, Green R, Drake E, Montine T, Conti C, Weiner M, Nosheny R, Truran Sacrey D, Fockler J, Miller M, Conti C, Kwang W, Jin C, Diaz A, Ashford M, Flenniken D, Kormos A, Petersen R, Aisen P, Rafii M, Raman R, Jimenez G, Donohue M, Salazar J, Fidell A, Boatwright V, Robison J, Zimmerman C, Cabrera Y, Walter S, Clanton T, Shaffer E, Webb C, Hergesheimer L, Smith S, Ogwang S, Adegoke O, Mahboubi P, Pizzola J, Jenkins C, Beckett L, Harvey D, Donohue M, Saito N, Diaz A, Hussen K, Okonkwo O, Rivera-Mindt M, Amaza H, Thao M, Parkins S, Ayo O, Glittenberg M, Hoang I, Germano K, Strong J, Weisensel T, Magana F, Thomas L, Guzman V, Ajayi A, Di Benedetto J, Talavera S, Jack C, Felmlee J, Fox N, Thompson P, DeCarli C, Forghanian-Arani A, Borowski B, Reyes C, Hedberg C, Ward C, Schwarz C, Reyes D, Gunter J, Moore-Weiss J, Kantarci K, Matoush L, Senjem M, Vemuri P, Reid R, Malone I, Thomopoulos S, Nir T, Jahanshad N, Knaack A, Fletcher E, Harvey D, Tosun-Turgut D, Rossi Chen S, Choe M, Crawford K, Yushkevich P, Das S, Jagust W, Landau S, Koeppe R, Rabinovici G, Villemagne V, LoPresti B, Perrin R, Morris J, Franklin E, Bernhardt H, Cairns N, Taylor-Reinwald L, Shaw L, Lee E, Lee V, Korecka M, Brylska M, Wan Y, Trojanowki J, Toga A, Crawford K, Neu S, Saykin A, Nho K, Foroud T, Jo T, Risacher S, Craft H, Apostolova L, Nudelman K, Faber K, Potter Z, Lacy K, Kaddurah-Daouk R, Shen L, Karlawish J, Erickson C, Grill J, Largent E, Harkins K, Weiner M, Thal L, Kachaturian Z, Frank R, Snyder P, Buckholtz N, Hsiao J, Ryan L, Molchan S, Khachaturian Z, Carrillo M, Potter W, Barnes L, Bernard M, González H, Ho C, Hsiao J, Jackson J, Masliah E, Masterman D, Okonkwo O, Perrin R, Ryan L, Silverberg N, Silbert L, Kaye J, White S, Pierce A, Thomas A, Clay T, Schwartz D, Devereux G, Taylor J, Ryan J, Nguyen M, DeCapo M, Shang Y, Schneider L, Munoz C, Ferman D, Conant C, Martin K, Oleary K, Pawluczyk S, Trejo E, Dagerman K, Teodoro L, Becerra M, Fairooz M, Garrison S, Boudreau J, Avila Y, Brewer J, Jacobson A, Gama A, Kim C, Little E, Frascino J, Ferng N, Trujillo S, Heidebrink J, Koeppe R, MacDonald S, Malyarenko D, Ziolkowski J, O’Connor J, Robert N, Lowe S, Rogers V, Petersen R, Hackenmiller B, Boeve B, Albers C, Kreuger C, Jones D, Knopman D, Botha H, Magnuson J, Graff-Radford J, Crawley K, Schumacher M, McKinzie S, Smith S, Helland T, Lowe V, Ramanan V, Pavlik V, Faircloth J, Bishop J, Nath J, Chaudhary M, Kataki M, Yu M, Pacini N, Barker R, Brooks R, Aggarwal R, Honig L, Stern Y, Mintz A, Cordona J, Hernandez M, Long J, Arnold A, Groves A, Middleton A, Vogler B, McCurry C, Mayo C, Raji C, Amtashar F, Klemp H, Elmore H, Ruszkiewicz J, Kusuran J, Stewart J, Horenkamp J, Greeson J, Wever K, Vo K, Larkin K, Rao L, Schoolcraft L, Gallagher L, Paczynski M, McMillan M, Holt M, Gagliano N, Henson R, LaBarge R, Swarm R, Munie S, Cepeda S, Winterton S, Hegedus S, Wilson T, Harte T, Bonacorsi Z, Geldmacher D, Watkins A, Barger B, Smelser B, Bates C, Stover C, McKinley E, Ikner G, Hendrix H, Cooper H, Mahaffey J, Booth Robbins L, Brown Ashley L, Natelson-Love M, Carter P, Solomon V, Grossman H, Groome A, Ardolino A, Kaplan A, Sheppard F, Burgos-Rivera G, Garcia-Camilo G, Lim J, Neugroschl J, Jackson K, Evans K, Soleimani L, Sano M, Ghesani N, Binder S, Apuango X, Sood A, Troutman A, Blanchard K, Richards A, Nelson G, Hendrickson K, Yurko E, Plenge J, Rufo V, Shah R, Duara R, Lynch B, Chirinos C, Dittrich C, Campbell D, Mejia D, Perez G, Colvee H, Gonzalez J, Gondrez J, Knaack J, Acevedo M, Cereijo M, Greig-Custo M, Villar M, Wishnia M, Detling S, Barker W, Albert M, Moghekar A, Rodzon B, Demsky C, Pontone G, Pekar J, Farrington L, Pomper M, Johnson N, Alo T, Sadowski M, Ulysse A, Masurkar A, Marti B, Mossa D, Geesey E, Petrocca E, Schulze E, Wong J, Boonsiri J, Kenowsky S, Martinez T, Briglall V, Doraiswamy P, Nwosu A, Adhikari A, Hellegers C, Petrella J, James O, Wong T, Hawk T, Vaishnavi S, McCoubrey H, Nasrallah I, Rovere R, Maneval J, Robinson E, Rivera F, Uffelman J, Combs M, O’Donnell P, Manning S, King R, Nieto A, Glueck A, Mandal A, Swain A, Gamble B, Meacham B, Forenback D, Ross D, Cheatham E, Hartman E, Cornell G, Harp J, Ashe L, Goins L, Watts L, Yazell M, Mandal P, Buckler R, Vincent S, Rudd T, Lopez O, Malia A, Chiado C, Zik C, Ruszkiewicz J, Savage K, Fenice L, Oakley M, Tacey P, Berman S, Bowser S, Hegedus S, Saganis X, Porsteinsson A, Mathewson A, Widman A, Holvey B, Clark E, Morales E, Young I, Ruszkiewicz J, Hopkins K, Martin K, Kowalski N, Hunt R, Calzavara R, Kurvach R, D’Ambrosio S, Thai G, Vides B, Lieb B, McAdams-Ortiz C, Toso C, Mares I, Moorlach K, Liu L, Corona M, Nguyen M, Tallakson M, McDonnell M, Rangel M, Basheer N, Place P, Romero R, Tam S, Nguyen T, Thomas A, Frolov A, Khera A, Browning A, Kelley B, Dawson C, Mathews D, Most E, Phillips E, Nguyen L, Nunez M, Miller M, Jones M, Martinez N, Logan R, McColl R, Pham S, Fox T, Moore T, Levey A, Brown A, Kippels A, Ellison A, Lyons C, Hales C, Parry C, Williams C, McCorkle E, Harris G, Rose H, Jooma I, Al-Amin J, Lah J, Webster J, Swiniarski J, Chapman L, Donnelly L, Mariotti L, Locke M, Vaughn P, Penn R, Carpentier S, Yeboah S, Basadre S, Malakauskas S, Lyron S, Villinger T, Burney T, Burns J, Abusalim A, Dahlgren A, Montero A, Arthur A, Dooly H, Kreszyn K, Berner K, Gillen L, Scanlan M, Madison M, Mathis N, Switzer P, Townley R, Fikru S, Sullivan S, Wright E, Beigi M, Daley A, Ko A, Luong B, Nyborg G, Morales J, Durbin K, Garcia L, Parand L, Macias L, Monserratt L, Farchi M, Wu P, Hernandez R, Rodriguez T, Graff-Radford N, Marolt A, Thomas A, Aloszka D, Moncayo E, Westerhold E, Day G, Chrestensen K, Imhansiemhonehi M, McKinzie S, Stephens S, Grant S, Brosch J, Perkins A, Saunders A, Kovac D, Polson H, Mwaura I, Mejia K, Britt K, King K, Nichols K, Lawrence K, Rankin L, Farlow M, Wiesenauer P, Bryant R, Herring S, Lynch S, Wilson S, Day T, Korst W, van Dyck C, Mecca A, Miller A, Brennan A, Khan A, Ruan A, Gunnoud C, Mendonca C, Raynes-Goldfinger D, Salardini E, Hidalgo E, Cooper E, Singh E, Murphy E, May J, Stanhope J, Lam J, Waszak J, Nelsen K, Sacaza K, Hasbani M, Donahue M, Chen M, Barcelos N, Eigenberger P, Bonomi R, O’Dell R, Jefferson S, Khasnavis S, Smilowitz S, DeStefano S, Good S, Camarro T, Clayton V, Cavrel Y, Lu Y, Chertkow H, Bergman H, Hosein C, Black S, Kapadia A, Bhan A, Lam B, Scott C, Gabriel G, Bray J, Zotovic L, Gutierrez M, Masellis M, Farshadi M, Gui M, Mitchell M, Taylor R, Endre R, Taghi-Zada Z, Hsiung R, English C, Kim E, Yau E, Tong H, Barlow L, Jennings L, Assaly M, Nunes P, Marian T, Kertesz A, Rogers J, Trost D, Wint D, Bernick C, Munic D, Grant I, Korkoyah A, Raja A, Lapins A, Ryan C, Pejic J, Basham K, Lukose L, Haddad L, Quinlan L, Houghtaling N, Sadowsky C, Martinez W, Villena T, Reynolds B, Forero A, Ward C, Brennan E, Figueroa E, Esposito G, Mallory J, Johnson K, Turner K, Seidenberg K, McCann K, Bassett M, Chadwick M, Turner R, Bean R, Sharma S, Marshall G, Haviari A, Pietras A, Wallace B, Munro C, Rivera-Delpin G, Hustead H, Levesque I, Ramirez J, Nolan K, Glennon K, Palou M, Erkkinen M, DaSilva N, Friedman P, Silver R, Salazar R, Polleys R, McGinnis S, Gale S, Hall T, Luu T, Chao S, Lin E, Coleman J, Epperson K, Vasanawala M, Atri A, Rangel A, Evans B, Monarrez C, Cline C, Liebsack C, Bandy D, Goldfarb D, Intorcia D, Olgin J, Clark K, King K, York K, Reade M, Callan M, Glass M, Johnson M, Gutierrez M, Goddard M, Trncic N, Choudhury P, Reyes P, Lowery S, Hall S, Olgin S, de Santiago S, Alosco M, Ton A, Jimenez A, Ellison A, Tran A, Anderson B, Carter D, Veronelli D, Lenio S, Steinberg E, Mez J, Weller J, Johns J, Mez J, Harkins J, Puleio A, Hoti I, Mwicigi J, Puleio A, Alosco M, Schultz O, Lauture M, Steinberg E, Denis R, Killiany R, Singh S, Lenio S, Qiu W, Devis Y, Obisesan T, Stone A, Ordor D, Udodong I, Okonkwo I, Khan J, Turner J, Hughes K, Kadiri O, Duffy C, Moss A, Stapleton K, Toth M, Sanders M, Ayres M, Hamski M, Fatica P, Ogrocki P, Ash S, Pot S, Chen D, Soto A, Tanase C, Bissig D, Vanya H, Russell H, Patel H, Zhang H, Wallace K, Ayers K, Gallegos M, Forloines M, Sinn M, Kahulugan Q, Isip R, Calderon S, Hamm T, Borrie M, Lee T, Bartha R, Johnson S, Asthana S, Carlsson C, Perrin A, Tariot P, Fleisher A, Reeder S, Capote H, Emborsky A, Mattle A, Ajtai B, Wagner B, Myers B, Slazyk D, Fragale D, Fransen E, Macnamara H, Falletta J, Hirtreiter J, Mechtler L, King M, Asbach M, Rainka M, Zawislak R, Wisniewski S, O’Malley S, Jimenez-Knight T, Peehler T, Aladeen T, Bates V, Wenner V, Elmalik W, Scharre D, Ramamurthy A, Bouchachi S, Kataki M, Tarawneh R, Kelley B, Celmins D, Leader A, Figueroa C, Bauerle H, Patterson K, Reposa M, Presto S, Ahmed T, Stewart W, Pearlson G, Blank K, Anderson K, Santulli R, Schwartz E, Williamson J, Jessup A, Williams A, Duncan C, O’Connell A, Gagnon K, Zamora E, Bateman J, Crawford F, Thompson D, Walker E, Rowell J, White M, Ledford P, Bohlman S, Henkle S, Bottoms J, Moretz L, Hoover B, Shannon M, Rogers S, Baker W, Harrison W, Wu C, DeMarco A, Stipanovich A, Arcuri D, Clark J, Davis J, Doyon K, Amoyaw M, Acosta M, Bailey R, Warren S, Fogerty T, Sanborn V, Riddle M, Salloway S, Malloy P, Correia S, Windon C, Blackburn M, Rosen H, Miller B, Smith A, Mba I, Echevarria J, Janavs J, Roglaski E, Yong M, Devine R, Okhravi H, Rivera E, Kalowsky T, Smith C, Rosario C, Masdeu J, Le R, Gurung M, Sabbagh M, Garcia A, Ellis Slaughter M, Elayan N, Acothley S, Pomara N, Hernando R, Pomara V, Reichert C, Brawman-Mintzer O, Acree A, Williams A, Long C, Long R, Newhouse P, Hill S, Boegel A, Seshadri S, Saklad A, Jones F, Hu W, Sotelo V, Gonazalez Rojas Y, Mintzer J, Flynn Longmire C, Spicer K. Deep learning to quantify the pace of brain aging in relation to neurocognitive changes. Proceedings Of The National Academy Of Sciences Of The United States Of America 2025, 122: e2413442122. PMID: 39993207, PMCID: PMC11912385, DOI: 10.1073/pnas.2413442122.Peer-Reviewed Original ResearchConceptsCN adultsBrain agingNeuroanatomical agingLongitudinal modelAdverse cognitive changesChronological ageAlzheimer's diseaseNeurocognitive agingCognitive changesNeurocognitive changesCognitive functionNeurocognitive statusAssociated with changesAge trendsLongitudinal MRIAdultsBrainQuantify DNA methylationAD risk assessmentBlood-brain barrierT‑ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment
Kyro G, Smaldone A, Shee Y, Xu C, Batista V. T‑ALPHA: A Hierarchical Transformer-Based Deep Neural Network for Protein–Ligand Binding Affinity Prediction with Uncertainty-Aware Self-Learning for Protein-Specific Alignment. Journal Of Chemical Information And Modeling 2025, 65: 2395-2415. PMID: 39965912, DOI: 10.1021/acs.jcim.4c02332.Peer-Reviewed Original ResearchConceptsProtein-ligand binding affinity predictionBinding affinity predictionState-of-the-art performanceTransformer-based deep neural networksMultimodal feature representationAffinity predictionBinding affinity of small moleculesState-of-the-artDeep neural networksDeep learning modelsAffinity of small moleculesSelf-learning methodSARS-CoV-2 main proteasePredicted binding affinitiesFeature representationBinding affinityOn-target potencyNeural networkDrug discovery applicationsTransformation frameworkLearning modelsScoring functionCrystal structureSelf-learningMain proteaseImproving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media
Li Y, Viswaroopan D, He W, Li J, Zuo X, Xu H, Tao C. Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media. Journal Of Biomedical Informatics 2025, 163: 104789. PMID: 39923968, DOI: 10.1016/j.jbi.2025.104789.Peer-Reviewed Original ResearchConceptsTraditional deep learning modelsDeep learning modelsRecurrent neural networkLearning modelsEntity recognitionLanguage modelF1 scoreEnsemble of deep learningAdvances of natural language processingEffectiveness of ensemble methodsMicro-averaged F1Bidirectional Encoder RepresentationsExtensive labeled dataNatural language processingFine-tuned modelsBiomedical text miningFeature representationEncoder RepresentationsEvent extractionEntity typesText dataDeep learningSequential dataGPT-2Neural networkA deep learning analysis for dual healthcare system users and risk of opioid use disorder
Yin Y, Workman E, Ma P, Cheng Y, Shao Y, Goulet J, Sandbrink F, Brandt C, Spevak C, Kean J, Becker W, Libin A, Shara N, Sheriff H, Butler J, Agrawal R, Kupersmith J, Zeng-Trietler Q. A deep learning analysis for dual healthcare system users and risk of opioid use disorder. Scientific Reports 2025, 15: 3648. PMID: 39881142, PMCID: PMC11779826, DOI: 10.1038/s41598-024-77602-4.Peer-Reviewed Original ResearchConceptsRisk of opioid use disorderOpioid use disorderDeep neural networksUse disorderClinical factorsIncreased risk of opioid use disorderOpioid use disorder riskOpioid prescribing guidelinesNatural language processing of clinical notesDeep neural network modelU.S. veteransSubstance useOpioid useNatural language processingRetrospective studyBaltimore VA Medical CenterVA Medical CenterIncreased riskPrescribing guidelinesDrug useRisk factorsOpioidMedical CenterVeteransVeterans Health AdministrationDeep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy
Guo M, Wu Y, Hobson C, Su Y, Qian S, Krueger E, Christensen R, Kroeschell G, Bui J, Chaw M, Zhang L, Liu J, Hou X, Han X, Lu Z, Ma X, Zhovmer A, Combs C, Moyle M, Yemini E, Liu H, Liu Z, Benedetto A, La Riviere P, Colón-Ramos D, Shroff H. Deep learning-based aberration compensation improves contrast and resolution in fluorescence microscopy. Nature Communications 2025, 16: 313. PMID: 39747824, PMCID: PMC11697233, DOI: 10.1038/s41467-024-55267-x.Peer-Reviewed Original ResearchConceptsAdaptive optics techniquesMulti-photonDeep learning-based strategyAberration compensationLearning-based strategyTrained neural networkImprove image qualityOptical aberrationsNeural networkImage quantitationOptical techniquesDiverse datasetsSuper-resolution microscopyLight sheetRestore dataImage qualityImage signalNetworkImage inspectionImage acquisitionImage stacksOpticsResolutionImagesFluorescence microscopy
2024
Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction
Huang X, Arora J, Erzurumluoglu A, Stanhope S, Lam D, Arora J, Erzurumluoglu A, Lam D, Khoueiry P, Jensen J, Cai J, Lawless N, Kriegl J, Ding Z, de Jong J, Zhao H, Ding Z, Wang Z, de Jong J. Enhancing patient representation learning with inferred family pedigrees improves disease risk prediction. Journal Of The American Medical Informatics Association 2024, 32: 435-446. PMID: 39723811, PMCID: PMC11833479, DOI: 10.1093/jamia/ocae297.Peer-Reviewed Original ResearchConceptsElectronic health recordsDisease risk predictionElectronic health record researchFamily health historyGenetic aspects of diseaseRisk predictionInflammatory bowel disease subtypeHealth recordsHealth historyAspects of diseaseFamily relationsHealthcare researchPatient's disease riskInfluence of geneticsDisease riskDiagnosis dataFamily pedigreeEnvironmental exposuresRisk factorsDisease dependencyPatient representation learningClinical profileFamilyDisease subtypesRiskDeep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability
Zhou X, Kedia S, Meng R, Gerstein M. Deep learning analysis of fMRI data for predicting Alzheimer’s Disease: A focus on convolutional neural networks and model interpretability. PLOS ONE 2024, 19: e0312848. PMID: 39630834, PMCID: PMC11616848, DOI: 10.1371/journal.pone.0312848.Peer-Reviewed Original ResearchConceptsConvolutional neural networkNeural networkAlzheimer's diseaseConvolutional neural network modelMultimodal medical datasetsDeep learning methodsPotential of deep learningGenetic risk factorsMedical datasetsAlzheimer's Disease Neuroimaging InitiativeAD predictionDeep learningDeep learning analysisLearning methodsMedical imagesPredicting Alzheimer's diseaseDetection of Alzheimer's diseaseModel interpretationEarly detection of Alzheimer's diseaseAccuracy levelGenetic factorsDatasetEarly detection of ADNetworkDetection of ADA multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data
Bi Y, Abrol A, Fu Z, Calhoun V. A multimodal vision transformer for interpretable fusion of functional and structural neuroimaging data. Human Brain Mapping 2024, 45: e26783. PMID: 39600159, PMCID: PMC11599617, DOI: 10.1002/hbm.26783.Peer-Reviewed Original ResearchConceptsCross-attention mechanismVision transformerDeep learning modelsBrain disordersCharacteristics of schizophreniaDiagnosis of schizophreniaStructural neuroimaging dataNetwork connectivity matrixData fusion approachAttention mapsMultimodal baselinesFunctional network connectivityFuse informationDeep learningICA algorithmFusion approachGrey matter mapsAI algorithmsFunctional network connectivity matricesLeverage multiple sources of informationGray matter imagesLearning modelsMultiple sources of informationBrain imaging modalitiesNetwork connectivityImaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders
Rahaman A, Garg Y, Iraji A, Fu Z, Kochunov P, Hong L, Van Erp T, Preda A, Chen J, Calhoun V. Imaging‐genomic spatial‐modality attentive fusion for studying neuropsychiatric disorders. Human Brain Mapping 2024, 45: e26799. PMID: 39562310, PMCID: PMC11576332, DOI: 10.1002/hbm.26799.Peer-Reviewed Original ResearchConceptsNeural networkDilated convolutional neural networkJoint learning frameworkAttention scoresState-of-the-artDeep neural networksNeural network decisionsConvolutional neural networkAttention fusionFusion moduleDiverse data sourcesArtificial intelligence modelsLearning frameworkAttention moduleJoint learningMultimodal clusteringNetwork decisionsInput streamMultimodal learningHigh-dimensionalIntermediate fusionFused dataSZ classificationIntelligence modelsContextual patternsNatural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure
Adejumo P, Thangaraj P, Dhingra L, Aminorroaya A, Zhou X, Brandt C, Xu H, Krumholz H, Khera R. Natural Language Processing of Clinical Documentation to Assess Functional Status in Patients With Heart Failure. JAMA Network Open 2024, 7: e2443925. PMID: 39509128, PMCID: PMC11544492, DOI: 10.1001/jamanetworkopen.2024.43925.Peer-Reviewed Original ResearchConceptsFunctional status assessmentArea under the receiver operating characteristic curveClinical documentationElectronic health record dataHF symptomsOptimal care deliveryHealth record dataAssess functional statusStatus assessmentClinical trial participationProcessing of clinical documentsFunctional status groupCare deliveryOutpatient careMain OutcomesMedical notesTrial participantsNew York Heart AssociationFunctional statusQuality improvementRecord dataHeart failureClinical notesDiagnostic studiesStatus groupsAssessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes
Bogaerts J, Steenbeek M, Bokhorst J, van Bommel M, Abete L, Addante F, Brinkhuis M, Chrzan A, Cordier F, Devouassoux‐Shisheboran M, Fernández‐Pérez J, Fischer A, Gilks C, Guerriero A, Jaconi M, Kleijn T, Kooreman L, Martin S, Milla J, Narducci N, Ntala C, Parkash V, de Pauw C, Rabban J, Rijstenberg L, Rottscholl R, Staebler A, Van de Vijver K, Zannoni G, van Zanten M, Bart J, Bentz J, Bosse T, Bulten J, Desouki M, Lastra R, Numan T, Schoolmeester J, Schwartz L, Shih I, Soong T, Turashvili G, Vang R, Volchek M, Aliredjo R, Kusters‐Vandevelde H, de Hullu J, Simons M, van der Laak J. Assessing the impact of deep‐learning assistance on the histopathological diagnosis of serous tubal intraepithelial carcinoma (STIC) in fallopian tubes. The Journal Of Pathology Clinical Research 2024, 10: e70006. PMID: 39439213, PMCID: PMC11496567, DOI: 10.1002/2056-4538.70006.Peer-Reviewed Original ResearchConceptsDeep learning modelsSerous tubal intraepithelial carcinomaArtificial intelligenceAI assistanceDiagnosis of serous tubal intraepithelial carcinomaTubal intraepithelial carcinomaReview timeFallopian tubeIntraepithelial carcinomaAI supportHigh-grade serous ovarian carcinomaSerous ovarian carcinomaStandalone performanceAverage sensitivityGroup of pathologistsAccuracyOvarian carcinomaHistopathological diagnosisPathologist performanceMixed-model analysisDiagnostic certaintyCarcinomaDiagnostic settingCommon and unique brain aging patterns between females and males quantified by large‐scale deep learning
Du Y, Yuan Z, Sui J, Calhoun V. Common and unique brain aging patterns between females and males quantified by large‐scale deep learning. Human Brain Mapping 2024, 45: e70005. PMID: 39225381, PMCID: PMC11369911, DOI: 10.1002/hbm.70005.Peer-Reviewed Original ResearchConceptsBrain functional changesFunctional connectivityCognitive controlBrain agingBrain functionPatterns of brain agingResting-state brain functional connectivityBrain functional interactionsBrain functional connectivityHuman brain functionBrain aging patternsGender commonalitiesAge-related changesDeep learningHealthy participantsNormal agingNegative connectionFunctional changesBrainPositive connectionDeep learning modelsFunctional domainsAge effectsFunctional interactionsCross-validation scheme
This site is protected by hCaptcha and its Privacy Policy and Terms of Service apply