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Model Description | 模型說明

KaliSense AI is a CNN-Transformer deep learning model that predicts serum potassium (K⁺) anomalies directly from 12-lead ECG waveforms — without any blood draw.

The model classifies ECG inputs into three categories:

✅ Normal — K⁺ within normal range (3.5–5.0 mEq/L)
⚠️ Hypokalemia — Low potassium risk (K⁺ < 3.5 mEq/L)
🚨 Hyperkalemia — High potassium risk (K⁺ > 5.5 mEq/L)

Clinical Background | 臨床背景

Hyperkalemia and hypokalemia are among the most dangerous electrolyte imbalances, especially in:

Chronic Kidney Disease (CKD) patients
Hemodialysis patients (9万+ in Taiwan alone)
Patients on ACE inhibitors, ARBs, or potassium-sparing diuretics

Traditional serum potassium testing requires venous blood draw and lab processing (30 min – hours). KaliSense AI enables real-time, non-invasive potassium risk screening from ECG alone.

Model Architecture | 模型架構

Input: 12-lead ECG (500 Hz sampling rate)
    │
    ▼
CNN Encoder (local feature extraction)
• T-wave amplitude detection
• QRS complex width measurement  
• PR interval analysis
• QTc calculation
    │
    ▼
Transformer Encoder (global temporal context)
• Multi-head self-attention (8 heads)
• Positional encoding for temporal sequence
    │
    ▼
Classification Head
• 3-class output: Normal / Hypokalemia / Hyperkalemia
• Softmax probability scores

Performance | 模型效能

Validated on clinical dataset from nephrology and hemodialysis centers (IRB approved):

Metric	Value
Overall Accuracy	91.2%
Hyperkalemia Sensitivity	88.0%
Hyperkalemia Specificity	93.5%
Hypokalemia Sensitivity	85.4%
AUC (Hyperkalemia)	0.947
AUC (Hypokalemia)	0.921

Input Format | 輸入格式

Property	Specification
ECG Leads	12-lead (or 1/3/6-lead)
Sampling Rate	500 Hz (recommended)
Duration	10 seconds standard
File Formats	.CSV, .EDF, HL7

Intended Use | 適用範圍

✅ Intended for:

Clinical potassium risk screening support
Hemodialysis patient continuous monitoring
Rural clinic and remote care settings
Emergency department rapid assessment

❌ Not intended for:

Replacing laboratory serum potassium testing
Standalone diagnosis without physician review
Pediatric patients (not validated)

Limitations & Disclaimer | 限制與免責聲明

⚠️ This model is intended as a clinical decision support tool only.
All outputs must be confirmed by a licensed physician before clinical action.
Currently under TFDA SaMD regulatory review for medical device software certification.

Privacy & Compliance | 隱私與法規

Model trained on IRB-approved, de-identified clinical ECG data
Core model weights and training pipeline stored in private repository under TFDA SaMD compliance controls
Patient data is never stored beyond the analysis session
Compliant with ISO 27001 information security standards

Pre-training Datasets | 預訓練資料集

Dataset	Records	Usage
PhysioNet PTB-XL	21,799 ECGs	Pre-training
MIMIC-IV-ECG	800,000+ ECGs	Feature extraction pre-training
Internal Clinical (IRB)	2,847 ECGs	Fine-tuning & validation

Citation | 引用

If you reference this work, please cite:

bibtex

@software{kalisense2025,
  title     = {KaliSense AI: Non-Invasive Serum Potassium Anomaly Detection},
  author    = {Precise Intelligent Biotech Co., Ltd.},
  year      = {2025},
  url       = {https://huggingface.co/precise-biotech/kalisense-ecg-potassium}
}

Contact & Collaboration | 合作洽詢

🏢 Precise Bigdata生技股份有限公司 | Precise Intelligent Biotech Co., Ltd.
📧 contact@precisebiotech.ai
🌐 www.precisebiotech.ai
💻 GitHub: github.com/curry1982/kalisense-ai