{"id":"https://openalex.org/W7225751256","doi":"https://doi.org/10.3389/fdgth.2026.1946730","title":"SynForge-Cardio: a unified multimodal synthetic dataset generation framework for privacy-preserving cardiovascular AI","display_name":"SynForge-Cardio: a unified multimodal synthetic dataset generation framework for privacy-preserving cardiovascular AI","publication_year":2026,"publication_date":"2026-11-10","ids":{"openalex":"https://openalex.org/W7225751256","doi":"https://doi.org/10.3389/fdgth.2026.1946730"},"language":"en","primary_location":{"id":"doi:10.3389/fdgth.2026.1946730","doi":"https://doi.org/10.3389/fdgth.2026.1946730","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdgth.2026.1946730","pdf_url":"https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1946730/pdf","source":{"id":"https://openalex.org/S4210170686","display_name":"Frontiers in Digital Health","issn_l":"2673-253X","issn":["2673-253X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-4","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Digital Health","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1946730/pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5127880972","display_name":"Sreyas Sanil Kumar","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Sreyas Sanil Kumar","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161394787","display_name":"Kishore N.","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Kishore N.","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5161372689","display_name":"Amirthalakshmi T. M.","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Amirthalakshmi T. M.","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham","institution_ids":["https://openalex.org/I81556334"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5161382422","display_name":"Rahul S. G.","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I81556334","display_name":"Amrita Vishwa Vidyapeetham","ror":"https://ror.org/03am10p12","country_code":"IN","type":"education","lineage":["https://openalex.org/I81556334"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Rahul S. G.","raw_affiliation_strings":["Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electronics and Communication Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham","institution_ids":["https://openalex.org/I81556334"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5161372689"],"corresponding_institution_ids":["https://openalex.org/I81556334"],"apc_list":{"value":2550,"currency":"CHF","value_usd":3077},"apc_paid":{"value":2550,"currency":"CHF","value_usd":3077},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"8","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.8744999766349792,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.8744999766349792,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.031700000166893005,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.017400000244379044,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8050000071525574},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5418999791145325},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.535099983215332},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.49959999322891235},{"id":"https://openalex.org/C160920958","wikidata":"https://www.wikidata.org/wiki/Q7662746","display_name":"Synthetic data","level":2,"score":0.45329999923706055},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45170000195503235},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4325000047683716},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.41780000925064087},{"id":"https://openalex.org/C2779903281","wikidata":"https://www.wikidata.org/wiki/Q6888026","display_name":"Modalities","level":2,"score":0.3977000117301941},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.3896999955177307},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.3846000134944916},{"id":"https://openalex.org/C167966045","wikidata":"https://www.wikidata.org/wiki/Q5532625","display_name":"Generative model","level":3,"score":0.3781999945640564},{"id":"https://openalex.org/C136643341","wikidata":"https://www.wikidata.org/wiki/Q1361526","display_name":"Reachability","level":2,"score":0.37299999594688416},{"id":"https://openalex.org/C23224414","wikidata":"https://www.wikidata.org/wiki/Q176769","display_name":"Hidden Markov model","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C39890363","wikidata":"https://www.wikidata.org/wiki/Q36108","display_name":"Generative grammar","level":2,"score":0.358599990606308},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.3386000096797943},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.3321000039577484},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.32850000262260437},{"id":"https://openalex.org/C38677869","wikidata":"https://www.wikidata.org/wiki/Q724168","display_name":"Petri net","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C2988773926","wikidata":"https://www.wikidata.org/wiki/Q25104379","display_name":"Generative adversarial network","level":3,"score":0.325300008058548},{"id":"https://openalex.org/C2778707766","wikidata":"https://www.wikidata.org/wiki/Q202064","display_name":"Phone","level":2,"score":0.32030001282691956},{"id":"https://openalex.org/C2780910867","wikidata":"https://www.wikidata.org/wiki/Q1952416","display_name":"Multimodality","level":2,"score":0.3061999976634979},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2944999933242798},{"id":"https://openalex.org/C2779545769","wikidata":"https://www.wikidata.org/wiki/Q5135364","display_name":"Closeness","level":2,"score":0.28760001063346863},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2849000096321106},{"id":"https://openalex.org/C20136886","wikidata":"https://www.wikidata.org/wiki/Q749647","display_name":"Interoperability","level":2,"score":0.28220000863075256},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.28040000796318054},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C37736160","wikidata":"https://www.wikidata.org/wiki/Q1801315","display_name":"Adversarial system","level":2,"score":0.2766999900341034},{"id":"https://openalex.org/C194541083","wikidata":"https://www.wikidata.org/wiki/Q457174","display_name":"Workaround","level":2,"score":0.26109999418258667},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.25929999351501465},{"id":"https://openalex.org/C159423971","wikidata":"https://www.wikidata.org/wiki/Q177251","display_name":"Associative property","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25440001487731934}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.3389/fdgth.2026.1946730","doi":"https://doi.org/10.3389/fdgth.2026.1946730","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdgth.2026.1946730","pdf_url":"https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1946730/pdf","source":{"id":"https://openalex.org/S4210170686","display_name":"Frontiers in Digital Health","issn_l":"2673-253X","issn":["2673-253X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-4","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Digital Health","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.3389/fdgth.2026.1946730","doi":"https://doi.org/10.3389/fdgth.2026.1946730","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdgth.2026.1946730","pdf_url":"https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2026.1946730/pdf","source":{"id":"https://openalex.org/S4210170686","display_name":"Frontiers in Digital Health","issn_l":"2673-253X","issn":["2673-253X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-20","russia-white-list-4","vabb-shw"],"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Digital Health","raw_type":"journal-article"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225751256.pdf","grobid_xml":"https://content.openalex.org/works/W7225751256.grobid-xml"},"referenced_works_count":15,"referenced_works":["https://openalex.org/W2194775991","https://openalex.org/W2565167788","https://openalex.org/W2611650229","https://openalex.org/W2911964244","https://openalex.org/W2954996726","https://openalex.org/W2963446712","https://openalex.org/W3008872739","https://openalex.org/W3015471667","https://openalex.org/W3027572331","https://openalex.org/W4312933868","https://openalex.org/W4313439128","https://openalex.org/W4313644512","https://openalex.org/W4383823329","https://openalex.org/W4390873054","https://openalex.org/W7163660880"],"related_works":[],"abstract_inverted_index":{"Multimodal":[0],"cardiovascular":[1,434],"AI":[2],"systems":[3],"benefit":[4,385],"from":[5,171],"jointly":[6],"available":[7],"chest":[8,99],"radiographs,":[9],"12-lead":[10,102],"electrocardiograms":[11],"(ECGs),":[12],"and":[13,33,67,83,104,126,137,145,159,182,205,222,240,244,256,296,361,372,437],"structured":[14],"electronic":[15],"health":[16],"record":[17],"(EHR)":[18],"variables.":[19],"In":[20],"practice,":[21],"multi-institutional":[22],"model":[23],"development":[24],"is":[25,195,365,381,424],"constrained":[26],"by":[27,321,334,340,373],"privacy":[28,242],"regulations,":[29],"institutional":[30],"data":[31,43,316,435],"silos,":[32],"class":[34],"imbalance":[35],"for":[36,93,123,135,149,336,375,396,404,432],"less":[37],"common":[38],"findings.":[39],"Existing":[40],"open-source":[41],"synthetic":[42,95,315,347],"tools":[44],"are":[45],"largely":[46],"modality-specific:":[47],"tabular":[48],"libraries":[49],"such":[50,70],"as":[51,71,197,426],"Synthetic":[52],"Data":[53],"Vault":[54],"(SDV)/Conditional":[55],"Tabular":[56],"Generative":[57,73],"Adversarial":[58,74],"Network":[59,75],"(CTGAN),":[60],"image":[61],"generators":[62],"based":[63],"on":[64,208,249,287],"latent":[65],"diffusion,":[66],"time-series":[68],"methods":[69],"Time-series":[72],"(TimeGAN)":[76],"each":[77],"require":[78],"its":[79],"own":[80],"setup,":[81],"configuration,":[82],"validation.":[84],"This":[85,193],"study":[86],"presents":[87],"SynForge-Cardio,":[88],"a":[89,110,151,156,160,198,202,209,264,310,427],"multimodality":[90],"orchestration":[91,217],"framework":[92,175,276,431],"prompt-driven":[94],"dataset":[96],"generation":[97,285],"across":[98,191,325],"X-ray":[100],"(CXR),":[101],"ECG,":[103],"EHR":[105,376,419],"modalities.":[106,192],"Rather":[107],"than":[108],"introducing":[109],"new":[111],"generative":[112,370],"backbone,":[113],"SynForge-Cardio":[114,423],"coordinates":[115],"established":[116],"components\u2014latent":[117],"diffusion":[118],"with":[119,201,270,314],"optional":[120,132],"low-rank":[121],"adaptation":[122],"CXR;":[124],"parametric":[125],"bounded":[127],"morphology-preserving":[128],"augmentation":[129,436],"(with":[130],"an":[131,246,367],"TimeGAN":[133],"path)":[134],"ECG;":[136],"size-aware":[138],"routing":[139],"among":[140],"jitter":[141],"augmentation,":[142,345],"Gaussian":[143],"copula,":[144],"CTGAN/Tabular":[146],"Variational":[147],"Autoencoder":[148],"EHR\u2014behind":[150],"shared":[152],"clinical":[153],"prompt":[154],"interface,":[155],"rule-based":[157],"router,":[158],"postgeneration":[161],"quality/privacy":[162],"reporting":[163],"layer.":[164],"The":[165],"three":[166,326],"modalities":[167],"were":[168,307,421],"generated":[169],"independently":[170,368],"separate":[172],"datasets;":[173],"the":[174,216,257,275,288,318,387,397],"does":[176],"not":[177,366,382,410],"perform":[178],"patient-level":[179],"joint":[180],"synthesis":[181],"makes":[183],"no":[184],"claim":[185],"of":[186,384],"anatomical":[187],"or":[188],"physiological":[189],"consistency":[190],"system":[194],"implemented":[196],"FastAPI":[199],"backend":[200],"React":[203],"frontend":[204],"can":[206,349],"run":[207],"single":[210],"workstation":[211],"GPU.":[212],"Herein,":[213],"we":[214,278],"formalize":[215],"objective,":[218],"specify":[219],"modality-wise":[220],"models":[221],"metrics":[223],"[Fr\u00e9chet":[224],"Inception":[225],"Distance":[226],"(FID),":[227],"dynamic":[228],"time":[229],"warping":[230],"(DTW),":[231],"power":[232],"spectral":[233],"density":[234],"similarity,":[235],"Kolmogorov\u2013Smirnov":[236],"statistics,":[237],"correlation":[238],"preservation,":[239],"distance-to-closest-record":[241],"screening],":[243],"describe":[245],"evaluation":[247],"protocol":[248],"public":[250],"corpora":[251],"(NIH":[252],"Chest":[253],"X-ray14,":[254],"PTB-XL,":[255],"UCI":[258],"Cleveland":[259],"Heart":[260],"Disease":[261],"dataset),":[262],"including":[263],"four-regime":[265],"train-on-synthetic/test-on-real":[266],"(TSTR)":[267],"utility":[268],"analysis":[269],"five":[271,332],"random":[272],"seeds.":[273],"On":[274],"alone,":[277],"measured":[279],"94.6%":[280],"modality-routing":[281],"accuracy,":[282],"100%":[283,297],"direct-vs.-routed":[284],"equivalence":[286],"evaluated":[289],"cases,":[290],"59":[291],"unnecessary":[292],"generator":[293],"calls":[294],"avoided,":[295],"validation/reporting":[298],"completeness":[299],"(1,105/1,108":[300],"tests":[301],"pass,":[302],"3":[303],"skipped).":[304],"Downstream-utility":[305],"results":[306,420],"mixed:":[308],"augmenting":[309],"25%-real":[311],"training":[312],"set":[313,390],"changed":[317],"held-out":[319],"AUROC":[320],"\u22120.041":[322],"to":[323,352],"+0.050":[324],"CXR":[327],"tasks":[328],"(none":[329],"significant":[330],"at":[331,357],"seeds);":[333],"+0.019":[335],"ECG":[337],"MI-vs.-NORM":[338],"[produced":[339],"mode":[341,363],"B":[342,364],"(bounded":[343],"real-waveform":[344],"whose":[346],"samples":[348],"be":[350],"matched":[351],"their":[353],"true":[354],"source":[355],"recording":[356],"top-1":[358],"\u2248":[359],"42.7\u201343.4%,":[360],"hence,":[362],"learned":[369],"model)];":[371],"+0.017":[374],"heart":[377],"disease":[378],"classification,":[379],"which":[380],"evidence":[383],"given":[386],"61-record":[388],"hold-out":[389],"(the":[391],"test-set":[392],"bootstrap":[393],"95%":[394],"CI":[395],"difference":[398],"includes":[399],"zero).":[400],"Medical":[401],"Information":[402],"Mart":[403],"Intensive":[405],"Care":[406],"IV":[407],"acquisition":[408],"was":[409,415],"completed":[411],"because":[412],"credentialed":[413],"access":[414],"unavailable;":[416],"all":[417],"reported":[418],"UCI-based.":[422],"intended":[425],"practical,":[428],"reproducible":[429],"research":[430],"privacy-conscious":[433],"prototype":[438],"development.":[439]},"counts_by_year":[],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2026-11-11T00:00:00"}
