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Medical AI
Heart

Congenital Heart Disease Detection

Medical AI · Cardiology

Seeing What
Hearts Hide

Deep learning meets echocardiography — our AI parses ultrasound signals in real time, flagging structural defects the eye might miss. Early. Precise. Actionable.

0
% Accuracy
0
Sec Detection
0
CHD Types
🔊Echo Probe
──▶
📡Signal
──▶
🧠AI Model
──▶
📋Report
+ve 0 −ve
AI Diagnosis
DefectASD Type II
SeverityModerate
EF%62%
HR74 bpm
Confidence0%
Transducer
2.5 MHz · A4C View
✓Clinician Verified
2cm 4cm 6cm 8cm SVC IVC Ao PA PV LV RV LA RA ASD ↗
ASD Detected

Congenital heart disease affects roughly 1 in 100 live births, and early, accurate detection materially changes surgical outcomes. Most diagnostic AI in this space is trained to answer a narrow question in isolation: is this view normal or abnormal. We build the full pipeline instead, from raw echocardiography acquisition through view classification, structural measurement, and surgical outcome prediction, so a clinician gets a single coherent read rather than a stack of disconnected model outputs to reconcile themselves.

Our models are developed with clinicians trained at MIT and Harvard, published in peer-reviewed venues supported by the NIH and the American Heart Association, and validated against real surgical outcomes, not just retrospective image labels. That grounding matters in a clinical setting: a system used in fetal and perinatal cardiology has to be explainable to the physician making the call, auditable after the fact, and precise enough that its output changes a real decision rather than adding another dashboard to check.

Research Pipeline

How Our AI Works

01
Fetal Echo & Perinatal Diagnosis
End-to-end AI pipeline from image acquisition to clinical outcome prediction
⟳
Image Acquisition
Guided sonography optimization
⊙
Auto Measurement
Cardiac structure sizing
⊘
Outlier Detection
Anomaly flagging
◈
CHD Classification
ASD · VSD · TOF · CoA
◉
Outcome Prediction
Risk stratification
02
Automated View Classification & Segmentation
Deep learning steps through recognition, segmentation, and diagnosis
VIEW CLASSIFIER INPUT A4C PLAX PSAX SUBC CONFIDENCE SCORES A4C 94% PLAX 40% PSAX 20% SUBC 9% PREDICTION ✓ A4C — Apical 4-Chamber Confidence: 94.3% · Latency: 12 ms DEEP LEARNING VIEW CLASSIFIER
Standard view recognition

The model identifies which echocardiographic view is being captured — A4C, PLAX, PSAX, subcostal — enabling downstream algorithms to apply view-specific analysis without operator labelling.

CNN classifier 4 standard views Real-time
SEGMENTATION MASK LV LA RV RA AUTO-MEASUREMENTS LV Vol 88 mL RV Vol 74 mL Wall T. 9.2 mm EF% 62% U-NET MULTI-STRUCTURE MASK
Cardiac structure segmentation

A segmentation model delineates all four chambers simultaneously — LV, RV, LA, RA — enabling precise automated measurement of volumes, wall thickness, and chamber dimensions throughout the cardiac cycle.

U-Net architecture 4-chamber mask Frame-by-frame
CHD DETECTION OUTPUT ASD ↗ Atrial Septal Defect VSD Ventricular Septal DETECTION CONFIDENCE ASD 94.7% VSD 71.2% TOF 8.1% MULTI-LABEL CHD CLASSIFIER
Automated CHD flagging

The diagnostic head runs multi-label classification across CHD types — ASD, VSD, Tetralogy of Fallot, hypoplastic left heart syndrome, coarctation of aorta — returning confidence scores and localised bounding boxes for each suspected defect.

Multi-label Bounding box TOF · HLHS · CoA
03
Functional Assessment & Risk Prediction
From ventricular metrics to outcome modelling and phenotypic clustering
Functional Metrics
EF%
62%
RV strain
−18%
LV Vol
88 mL
TAPSE
14 mm
ML Risk Model
Input layer
Hidden ×3
Attention
Output
Predictions
RV failure risk
Moderate ▲
Post-surgical EF
58–65%
Phenotype cluster
Group B · Fontan
Unsupervised phenotypic clustering — CHD population
Group A · ToF Group B · Fontan Group C · ASD/VSD Group D · HLHS
Every heartbeat tells a story. We listen with AI.
FDA-grade pipeline
Real-time inference
ASD · VSD · TOF · CoA