Reconciles wearable streams, device readings and patient-reported signals into a clinician-ready summary
↳ Decompensation signature · 4-signal convergence
Escalate
Readable data
Reconciles wearable streams, device readings and patient-reported signals into a clinician-ready summary
↳ HR trend ↓ 8 bpm · activity ↑
Stable
Transparent Clinical Suggestions
Care-plan drafts with the signals and reasoning made visible, grounded in the latest guidelines.
Clinic visit ≤ 48h. Multi-signal pattern, weight still stable.
Approve
Transparent Clinical Suggestions
Care-plan drafts with the signals and reasoning made visible, grounded in the latest guidelines.
Clinic visit ≤ 48h. Multi-signal pattern, weight still stable.
Approve
The portfolio
KAI orchestrates a portfolio of proprietary models running today on Kento's devices. Performance numbers reference internal validation against published comparators and clinically-accepted benchmarks.
Deployed1 model
KAI
Deployed
The clinical orchestrator and personalization agent across the model portfolio.
Outperforms Zignoli (2020) and tracheal-sound methods
Kento
KENTO | QRS
Validated
Proprietary R-peak detector. The foundation under everything else in the platform.
Performance measured in progress (initial benchmark outperforms state-of-the-art) for a 2 leads or less ecg
Kento
In ValidationPredict family
KENTO | Predict
In Validation
A family of longitudinal prediction models trained on Kento's proprietary multi-source ECG-outcome corpus. Each model targets a distinct clinical decision.
KENTO | BCG
In Validation
Beat-to-beat cardiac mechanics from ballistocardiography fused with ECG, respiration, and motion. No echo, no cuff.
AUC = 0.78
Heart failure decompensation prediction
Extending Aydemir, Shandhi, Inan et al., IEEE TBME 2020
KENTO | Predict Cardiac Age
In Validation
Biological cardiac age from continuous ECG.
State-of-the-art MAE
On par with full 12-lead ECG-derived cardiac age
KENTO | Predict Vascular Age
In Validation
Vascular biological age from integrated sensor analysis.
the sensitivity at 95% specificity or above is >90%
KENTO | Predict Abnormalities
In Validation
Multi-class classifier across 37 ECG abnormalities.
KAI orchestrates a portfolio of proprietary models running today on Kento's devices. Performance numbers reference internal validation against published comparators and clinically-accepted benchmarks.
Deployed1 model
KAI
Deployed
The clinical orchestrator and personalization agent across the model portfolio.
Outperforms Zignoli (2020) and tracheal-sound methods
Kento
KENTO | QRS
Validated
Proprietary R-peak detector. The foundation under everything else in the platform.
Performance measured in progress (initial benchmark outperforms state-of-the-art) for a 2 leads or less ecg
Kento
In ValidationPredict family
KENTO | Predict
In Validation
A family of longitudinal prediction models trained on Kento's proprietary multi-source ECG-outcome corpus. Each model targets a distinct clinical decision.
KENTO | BCG
In Validation
Beat-to-beat cardiac mechanics from ballistocardiography fused with ECG, respiration, and motion. No echo, no cuff.
AUC = 0.78
Heart failure decompensation prediction
Extending Aydemir, Shandhi, Inan et al., IEEE TBME 2020
KENTO | Predict Cardiac Age
In Validation
Biological cardiac age from continuous ECG.
State-of-the-art MAE
On par with full 12-lead ECG-derived cardiac age
KENTO | Predict Vascular Age
In Validation
Vascular biological age from integrated sensor analysis.
the sensitivity at 95% specificity or above is >90%
KENTO | Predict Abnormalities
In Validation
Multi-class classifier across 37 ECG abnormalities.
Predicts optimal treatment trajectories for personalized care planning
KENTO | Predict Pathway
In Validation
Predicts which cardiopulmonary program transitions yield best outcomes, and identifies in-program behaviors that accelerate clinical improvement.
Predicts optimal treatment trajectories for personalized care planning
KENTO | Predict Lead12
In Validation
Full 12-lead ECG reconstructed for clinical-grade diagnostic use. Usable at home or in clinic.
r > 0.957
Precordial leads V1-V6 reconstruction accuracy
α Research4 active threads
Pulmonary artery pressure
α Research
Non-invasive estimation
α
Aortic valve health
α Research
S2/S1 balance analysis
α
Pressure-Volume loop estimation
α Research
α
No cuff blood pressure
α Research
<5 mmHg accuracy
α
The portfolio
KAI orchestrates a portfolio of proprietary models running today on Kento's devices. Performance numbers reference internal validation against published comparators and clinically-accepted benchmarks.
Deployed1 model
KAI
Deployed
The clinical orchestrator and personalization agent across the model portfolio.
Outperforms Zignoli (2020) and tracheal-sound methods
Kento
KENTO | QRS
Validated
Proprietary R-peak detector. The foundation under everything else in the platform.
Performance measured in progress (initial benchmark outperforms state-of-the-art) for a 2 leads or less ecg
Kento
In ValidationPredict family
KENTO | Predict
In Validation
A family of longitudinal prediction models trained on Kento's proprietary multi-source ECG-outcome corpus. Each model targets a distinct clinical decision.
KENTO | BCG
In Validation
Beat-to-beat cardiac mechanics from ballistocardiography fused with ECG, respiration, and motion. No echo, no cuff.
AUC = 0.78
Heart failure decompensation prediction
Extending Aydemir, Shandhi, Inan et al., IEEE TBME 2020
KENTO | Predict Cardiac Age
In Validation
Biological cardiac age from continuous ECG.
State-of-the-art MAE
On par with full 12-lead ECG-derived cardiac age
KENTO | Predict Vascular Age
In Validation
Vascular biological age from integrated sensor analysis.
the sensitivity at 95% specificity or above is >90%
KENTO | Predict Abnormalities
In Validation
Multi-class classifier across 37 ECG abnormalities.