{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"tool_pmcardio","slug":"pmcardio","name":"PMcardio","type":"product","url":"https://www.powerfulmedical.com","page_url":"https://unfragile.ai/pmcardio","categories":["data-analysis"],"tags":[],"pricing":{"model":"freemium","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"tool_pmcardio__cap_0","uri":"capability://image.visual.ai.assisted.cardiovascular.imaging.interpretation.with.diagnostic.confidence.scoring","name":"ai-assisted cardiovascular imaging interpretation with diagnostic confidence scoring","description":"PMcardio analyzes cardiac imaging data (echocardiography, CT, MRI, angiography) using deep learning models trained on large-scale annotated cardiovascular datasets to detect structural abnormalities, functional impairments, and disease patterns. The system generates structured diagnostic reports with confidence scores and anatomical measurements, integrating computer vision feature extraction with clinical decision logic to flag critical findings and quantify diagnostic certainty for clinician review.","intents":["Reduce inter-observer variability in cardiac imaging interpretation across multiple cardiologists","Accelerate diagnostic workflow by automating initial image screening and measurement extraction","Obtain quantitative confidence metrics to support clinical decision-making and risk stratification","Identify subtle or early-stage cardiovascular pathology that might be missed on visual inspection alone"],"best_for":["Cardiologists and interventional specialists in high-volume imaging centers seeking to standardize diagnostic protocols","Smaller cardiology practices lacking access to multiple specialist readers for image review","Healthcare systems implementing quality assurance programs to reduce diagnostic variability"],"limitations":["Accuracy heavily dependent on image quality, acquisition protocol, and alignment with training data distribution — poor image quality degrades model performance","Regulatory clearance status and clinical validation evidence not publicly detailed, creating uncertainty about FDA/CE mark approval and clinical trial data","Model interpretability limited — confidence scores may not correlate with actual diagnostic accuracy; 'black box' predictions require clinician override capability","Freemium tier likely restricts access to advanced pathology detection or multi-modality analysis, requiring paid upgrade for comprehensive diagnostic coverage"],"requires":["DICOM-compatible cardiovascular imaging data (echocardiography, CT, MRI, angiography)","Integration with existing hospital PACS (Picture Archiving and Communication System) or imaging workflow","Clinician review and validation — AI output is decision support only, not autonomous diagnosis","Regulatory compliance framework (HIPAA, GDPR, local medical device regulations)"],"input_types":["DICOM medical imaging files","Cardiac ultrasound video sequences","CT/MRI volumetric imaging data","Angiography image series"],"output_types":["Structured diagnostic reports with findings and measurements","Confidence scores per detected pathology","Quantitative metrics (ejection fraction, chamber dimensions, valve areas)","Flagged critical findings for clinician attention","Risk stratification scores"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pmcardio__cap_1","uri":"capability://planning.reasoning.cardiovascular.disease.risk.stratification.and.treatment.planning.recommendation","name":"cardiovascular disease risk stratification and treatment planning recommendation","description":"PMcardio synthesizes imaging findings, clinical parameters, and patient history into structured risk assessments and treatment pathway recommendations using rule-based clinical logic and machine learning models trained on cardiovascular outcome data. The system generates evidence-based treatment suggestions (medical management, intervention timing, device therapy) with risk-benefit analysis to support shared decision-making between clinician and patient.","intents":["Generate standardized risk scores (e.g., SYNTAX, EuroSCORE variants) from imaging and clinical data to guide intervention decisions","Recommend optimal treatment pathways (medical vs. percutaneous vs. surgical) based on disease severity and patient factors","Identify high-risk patients requiring urgent intervention or closer monitoring","Support evidence-based clinical decision-making with outcome probability estimates"],"best_for":["Interventional cardiologists deciding between percutaneous coronary intervention (PCI) vs. coronary artery bypass grafting (CABG)","Heart failure specialists optimizing medical therapy and device selection","Multidisciplinary heart teams conducting case conferences for complex coronary or structural disease"],"limitations":["Risk models are population-derived and may not generalize to underrepresented demographics or rare presentations","Treatment recommendations are advisory only — clinical judgment and patient preferences must override algorithmic suggestions","Freemium tier likely excludes advanced risk calculators or multi-condition analysis, limiting utility for complex cases","Outcome prediction accuracy depends on completeness and accuracy of input clinical data; missing data reduces recommendation reliability"],"requires":["Complete patient clinical data (age, comorbidities, lab values, prior interventions)","Imaging findings from AI-assisted interpretation or manual entry","Integration with electronic health record (EHR) for automated data extraction","Clinician validation and override capability for non-standard cases"],"input_types":["Structured clinical parameters (age, ejection fraction, troponin, BNP)","Imaging findings (coronary anatomy, stenosis severity, wall motion)","Patient history (prior MI, revascularization, comorbidities)","Functional test results (stress test, catheterization data)"],"output_types":["Risk stratification scores (SYNTAX, EuroSCORE, TIMI, etc.)","Treatment pathway recommendations with evidence citations","Outcome probability estimates (mortality, morbidity, symptom relief)","Intervention timing recommendations (urgent, elective, conservative)","Structured clinical decision support reports"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pmcardio__cap_2","uri":"capability://tool.use.integration.pacs.and.ehr.integration.with.automated.imaging.workflow.routing","name":"pacs and ehr integration with automated imaging workflow routing","description":"PMcardio integrates with hospital Picture Archiving and Communication Systems (PACS) and electronic health records (EHR) via HL7/FHIR standards and DICOM protocols to automatically retrieve imaging studies, populate patient context, and route results back to clinician workflows. The system handles DICOM file ingestion, metadata extraction, and result delivery without requiring manual data transfer, minimizing workflow disruption and enabling seamless embedding into existing clinical processes.","intents":["Automatically pull cardiac imaging studies from PACS without manual file export or transfer","Populate patient demographics and clinical context from EHR to inform AI analysis","Route AI-generated reports and recommendations directly into clinician worklists and EHR","Maintain audit trails and compliance with medical imaging standards (DICOM, HL7, HIPAA)"],"best_for":["Large healthcare systems with mature PACS/EHR infrastructure seeking to integrate AI diagnostics into existing workflows","Imaging centers processing high volumes of cardiac studies where manual data transfer creates bottlenecks","Hospitals implementing quality assurance programs requiring standardized, auditable diagnostic workflows"],"limitations":["Integration complexity varies significantly based on PACS/EHR vendor and local IT infrastructure — custom integration work may be required","DICOM standard compliance depends on imaging equipment and acquisition protocols; non-standard formats or metadata may cause integration failures","Data governance and privacy controls must be configured per institution — default settings may not meet local compliance requirements","Freemium tier likely lacks advanced integration features (e.g., bi-directional EHR sync, custom workflow routing), requiring enterprise license"],"requires":["PACS system with DICOM query/retrieve (C-FIND, C-MOVE) capability","EHR with HL7 v2 or FHIR API support for patient data exchange","Network connectivity and firewall rules allowing secure communication between systems","IT infrastructure for managing API credentials, SSL certificates, and audit logging","HIPAA-compliant data handling and encryption (TLS for transit, encryption at rest)"],"input_types":["DICOM imaging studies from PACS","Patient demographics and clinical data from EHR","HL7/FHIR messages for clinical context","Imaging metadata (modality, acquisition parameters, timestamps)"],"output_types":["Structured diagnostic reports in HL7/FHIR format","DICOM secondary capture images with annotations","EHR-compatible result messages for clinician review","Audit logs and compliance reports"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pmcardio__cap_3","uri":"capability://image.visual.multi.modality.cardiovascular.imaging.analysis.with.cross.modal.correlation","name":"multi-modality cardiovascular imaging analysis with cross-modal correlation","description":"PMcardio processes multiple cardiac imaging modalities (echocardiography, CT, MRI, angiography, nuclear imaging) in a single analysis session and correlates findings across modalities to provide comprehensive disease assessment. The system aligns anatomical landmarks across different imaging types, identifies discrepancies between modalities, and synthesizes multi-modal evidence into unified diagnostic conclusions, enabling clinicians to leverage complementary imaging strengths.","intents":["Correlate findings across echocardiography, CT, and MRI to confirm diagnoses and resolve ambiguous findings","Detect discrepancies between imaging modalities that may indicate technical artifacts or disease progression","Provide comprehensive anatomical assessment by combining structural (CT/MRI) and functional (echo/nuclear) information","Reduce need for repeat imaging by maximizing diagnostic yield from available studies"],"best_for":["Academic medical centers and large hospitals with access to multiple imaging modalities and complex patient cases","Cardiologists managing patients with prior imaging studies from different institutions requiring comparative analysis","Structural heart disease specialists (e.g., transcatheter valve replacement planning) requiring precise multi-modal anatomy"],"limitations":["Cross-modal registration accuracy depends on image quality and temporal alignment — studies acquired at different time points may show disease progression rather than technical discrepancy","Computational complexity increases significantly with multiple modalities, potentially adding latency to analysis pipeline","Freemium tier likely supports only single-modality analysis, requiring paid upgrade for multi-modal correlation","Requires availability of multiple imaging studies per patient, which may not exist for all cases or institutions"],"requires":["Multiple cardiac imaging studies (minimum 2 modalities) in DICOM format","Temporal alignment or explicit time-point documentation if studies acquired at different dates","Sufficient image quality across all modalities for reliable registration and analysis","Clinician expertise to interpret multi-modal findings and resolve apparent discrepancies"],"input_types":["Echocardiography video sequences and measurements","CT coronary angiography (CCTA) volumetric data","Cardiac MRI cine and delayed-enhancement sequences","Invasive angiography image series","Nuclear imaging (SPECT, PET) perfusion maps"],"output_types":["Unified diagnostic report synthesizing multi-modal findings","Cross-modal correlation maps showing anatomical alignment","Discrepancy alerts highlighting inconsistencies between modalities","Integrated measurements and quantification across modalities","Confidence assessment for multi-modal consensus findings"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pmcardio__cap_4","uri":"capability://data.processing.analysis.quantitative.cardiac.measurement.extraction.with.anatomical.landmark.detection","name":"quantitative cardiac measurement extraction with anatomical landmark detection","description":"PMcardio automatically detects cardiac anatomical landmarks (chamber boundaries, valve annuli, coronary ostia) and extracts quantitative measurements (chamber volumes, ejection fraction, wall thickness, stenosis severity) from imaging data using deep learning-based segmentation and landmark localization models. The system generates standardized measurement reports compatible with clinical reporting standards, reducing manual measurement burden and improving reproducibility.","intents":["Automatically measure left ventricular ejection fraction (LVEF) and chamber volumes from echocardiography or MRI","Quantify coronary artery stenosis severity from angiography or CCTA for intervention planning","Extract valve area and gradient measurements for valvular disease assessment","Generate standardized measurement reports for clinical documentation and follow-up comparison"],"best_for":["Echocardiography labs processing high volumes of studies where manual measurement is time-consuming","Interventional cardiology teams requiring rapid stenosis quantification during catheterization","Heart failure clinics tracking serial LVEF measurements for therapy optimization"],"limitations":["Measurement accuracy depends on image quality and standard acquisition planes — non-standard views or poor image quality degrade segmentation performance","Automated measurements require clinician validation and may need manual correction for non-standard anatomy or technical artifacts","Freemium tier likely provides basic measurements only (e.g., LVEF), requiring paid upgrade for comprehensive multi-parameter quantification","Reproducibility may vary across different imaging equipment and acquisition protocols, requiring periodic recalibration"],"requires":["High-quality cardiac imaging in standard acquisition planes (apical 4-chamber, parasternal long-axis for echo; short-axis for MRI)","DICOM metadata with acquisition parameters and patient orientation information","Clinician review and validation of automated measurements before clinical use"],"input_types":["Echocardiography cine loops in standard views","Cardiac MRI short-axis and long-axis cine sequences","CT coronary angiography (CCTA) axial and curved-planar reformats","Invasive angiography image sequences"],"output_types":["Quantitative measurements (volumes, ejection fraction, areas, gradients)","Anatomical landmark coordinates and segmentation masks","Measurement confidence scores and quality assessment","Standardized measurement reports (ASE, EACVI formats)","Trend analysis for serial measurements"],"categories":["data-processing-analysis","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pmcardio__cap_5","uri":"capability://data.processing.analysis.diagnostic.variability.reduction.through.standardized.reporting.and.inter.observer.agreement.metrics","name":"diagnostic variability reduction through standardized reporting and inter-observer agreement metrics","description":"PMcardio generates standardized diagnostic reports using structured templates aligned with clinical guidelines (ACC/AHA, ESC) and provides inter-observer agreement metrics (kappa, ICC) comparing AI findings with clinician interpretations. The system tracks diagnostic consistency across multiple readers and imaging sessions, enabling quality assurance programs to identify sources of variability and standardize interpretation protocols.","intents":["Reduce inter-observer variability in cardiac imaging interpretation by providing standardized diagnostic templates","Quantify diagnostic agreement between AI and clinician readers to assess reliability and identify discrepancies","Track diagnostic consistency across multiple cardiologists and imaging sessions for quality assurance","Generate evidence for clinical validation studies demonstrating diagnostic accuracy and reproducibility"],"best_for":["Imaging centers implementing quality assurance programs to standardize diagnostic protocols","Healthcare systems conducting clinical validation studies to assess AI diagnostic accuracy","Cardiology practices seeking to reduce diagnostic variability and improve consistency"],"limitations":["Inter-observer agreement metrics require multiple independent clinician interpretations, adding workflow burden and cost","Standardized templates may not capture nuanced clinical findings or rare presentations, requiring free-text additions","Freemium tier likely lacks advanced reporting features or agreement metrics, requiring paid upgrade for quality assurance programs","Agreement metrics are retrospective — they identify variability but don't automatically correct it without protocol changes"],"requires":["Multiple independent clinician interpretations of same imaging studies (gold standard for agreement calculation)","Structured data capture for diagnostic findings to enable quantitative comparison","Clinical guideline templates (ACC/AHA, ESC) for standardized reporting","Quality assurance infrastructure to track and act on variability metrics"],"input_types":["Cardiac imaging studies with multiple clinician interpretations","Structured diagnostic findings from AI and clinician readers","Clinical guideline templates for standardized reporting"],"output_types":["Standardized diagnostic reports using guideline-aligned templates","Inter-observer agreement metrics (kappa, ICC, percent agreement)","Discrepancy reports highlighting AI vs. clinician disagreements","Quality assurance dashboards tracking diagnostic consistency","Trend analysis for variability reduction over time"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"tool_pmcardio__cap_6","uri":"capability://automation.workflow.freemium.tiered.access.model.with.feature.gating.for.enterprise.capabilities","name":"freemium tiered access model with feature gating for enterprise capabilities","description":"PMcardio implements a freemium business model offering basic AI-assisted diagnostic capabilities (single-modality analysis, standard measurements, basic risk scoring) in free tier, with advanced features (multi-modality analysis, advanced risk calculators, enterprise integration, priority support) restricted to paid tiers. The system uses feature flags and license-based access control to gate functionality, enabling cost-effective entry for smaller practices while monetizing advanced capabilities for larger institutions.","intents":["Enable independent practitioners and small clinics to access AI diagnostic support without upfront investment","Provide upgrade path for practices growing from basic to advanced diagnostic needs","Monetize advanced features (multi-modality, enterprise integration) for large healthcare systems","Reduce adoption barriers for new users while capturing revenue from power users"],"best_for":["Solo cardiologists and small practices with limited budgets seeking to adopt AI diagnostics","Healthcare systems evaluating AI tools before committing to enterprise-wide deployment","Practices with variable imaging volumes where freemium tier provides cost-effective baseline"],"limitations":["Freemium tier limitations not publicly detailed, creating uncertainty about feature restrictions and upgrade costs","Enterprise features likely require significant investment, potentially creating cost barriers for mid-size practices","Feature gating may create workflow friction if users frequently encounter restricted features requiring upgrade","Pricing transparency lacking — unclear whether freemium tier has usage limits, storage quotas, or support restrictions"],"requires":["User account creation and authentication","License key or subscription activation for paid tiers","Compliance with terms of service and data usage policies","Internet connectivity for license validation and cloud-based features"],"input_types":["User account credentials","License/subscription information","Imaging data (varies by tier)"],"output_types":["Feature access permissions","Usage metrics and billing information","Upgrade recommendations based on usage patterns"],"categories":["automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":41,"verified":false,"data_access_risk":"low","permissions":["DICOM-compatible cardiovascular imaging data (echocardiography, CT, MRI, angiography)","Integration with existing hospital PACS (Picture Archiving and Communication System) or imaging workflow","Clinician review and validation — AI output is decision support only, not autonomous diagnosis","Regulatory compliance framework (HIPAA, GDPR, local medical device regulations)","Complete patient clinical data (age, comorbidities, lab values, prior interventions)","Imaging findings from AI-assisted interpretation or manual entry","Integration with electronic health record (EHR) for automated data extraction","Clinician validation and override capability for non-standard cases","PACS system with DICOM query/retrieve (C-FIND, C-MOVE) capability","EHR with HL7 v2 or FHIR API support for patient data exchange"],"failure_modes":["Accuracy heavily dependent on image quality, acquisition protocol, and alignment with training data distribution — poor image quality degrades model performance","Regulatory clearance status and clinical validation evidence not publicly detailed, creating uncertainty about FDA/CE mark approval and clinical trial data","Model interpretability limited — confidence scores may not correlate with actual diagnostic accuracy; 'black box' predictions require clinician override capability","Freemium tier likely restricts access to advanced pathology detection or multi-modality analysis, requiring paid upgrade for comprehensive diagnostic coverage","Risk models are population-derived and may not generalize to underrepresented demographics or rare presentations","Treatment recommendations are advisory only — clinical judgment and patient preferences must override algorithmic suggestions","Freemium tier likely excludes advanced risk calculators or multi-condition analysis, limiting utility for complex cases","Outcome prediction accuracy depends on completeness and accuracy of input clinical data; missing data reduces recommendation reliability","Integration complexity varies significantly based on PACS/EHR vendor and local IT infrastructure — custom integration work may be required","DICOM standard compliance depends on imaging equipment and acquisition protocols; non-standard formats or metadata may cause integration failures","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.36666666666666664,"quality":0.7300000000000001,"ecosystem":0.15000000000000002,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.1,"match_graph":0.35,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:32.437Z","last_scraped_at":"2026-04-05T13:23:42.551Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=pmcardio","compare_url":"https://unfragile.ai/compare?artifact=pmcardio"}},"signature":"uh5K06WYoV2ZWy9SzTUef511AzXumpWxZ43QYttdft4dXtngRmNauNSrwoktLtqoSRDv5k32XV1NYSj9+VgkCA==","signedAt":"2026-06-21T11:34:10.886Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/pmcardio","artifact":"https://unfragile.ai/pmcardio","verify":"https://unfragile.ai/api/v1/verify?slug=pmcardio","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}