AI_ECG_Interpretation_Presentation_by_Huseini_Kamara.pptx

This presentation, prepared by Mr. Huseini Kamara, a Cardiovascular Technologist and Researcher, explores the transformative role of Artificial Intelligence (AI) in electrocardiogram (ECG) interpretation and cardiac diagnostics.

The slides discuss how AI enhances diagnostic accuracy, speeds up ECG analysis, and supports clinical decision-making in hospitals and remote care settings. It also highlights real-world AI ECG platforms such as Mayo Clinic AI-ECG, GE CardioSoft AI, Philips IntelliSpace ECG, AliveCor KardiaPro, and Anumana AI

1. Artificial Intelligence in ECG Interpretation: Clinical Applications and the Future of Cardiac Care Facilitator: Mr. Huseini Kamara Cardiovascular Technologist & Researcher Bhai Gurdas Institutes of Allied Sciences Punjab state , India

2. The Rise of AI in Cardiac Diagnostics Cardiovascular diseases remain the world’s leading cause of mortality. Manual ecg interpretation is time-consuming and prone to variability. Artificial I ntelligence (AI) offers automation, precision, and predictive insights. AI is not replacing humans – it enhances human expertise .

3. What is AI in ECG Interpretation? AI uses machine learning (ML) and deep learning (DL) to analyze ECG waveforms. Convolutional Neural Networks ( CNNs) identify spatial waveform patterns. Recurrent Neural Networks (RNNS)/LSTMS recognize temporal patterns over heartbeats. These models are trained on massive ecg datasets ( Mit-bih , Physionet ).

4. Why AI Matters in Clinical ECG Practice Improves Diagnostic Accuracy And Consistency. Enables Rapid Triage And Early Detection Of Life-threatening Events. Integrates With Hospital Ecg Systems For Real-time Decision Support. Reduces Workload For Technologists And Cardiologists .

5. Clinical Applications of AI in ECG Interpretation Arrhythmia detection: identifies >20 rhythm abnormalities (Afib, VT, AV block, etc.) With >95% accuracy. Myocardial Infarction detection: detects subtle ST-T changes preceding MI and activates emergency response. Heart failure prediction: detects LV dysfunction using AI models like mayo clinic AI-ECG .

6. Clinical Workflow Integration AI ecgs integrated into Hospital Information Systems (HIS) and Electronic Health Records (EHRs). Automatically flags abnormal ecgs for urgent review. Supports telecardiology and remote consultations. Example: Philips Intellispace ecg, Ge C ardiosoft AI .

7. AI-Powered Wearables and Remote Monitoring Fitbit Watch Apple Watch

8. CONT’D Samsung Galaxy Watch, Alivecor ECG.

9. CONT’D These Wearable Devices; Enable Real-time Ecg Monitoring And Cloud Upload Early Detection Of Arrhythmias And Ischemic Changes. Expands Cardiac Care To Rural And Remote Areas

10. AI Software and Platforms in Use Software Developer Clinical Use Mayo Clinic AI-ECG Mayo Clinic Detects heart failure, LV dysfunction GE CardioSoft AI GE Healthcare ECG waveform classification Philips IntelliSpace ECG Philips Centralized ECG with AI triage Anumana AI-ECG Mayo Clinic + nference Predicts cardiomyopathy, LV dysfunction AliveCor KardiaPro AliveCor Mobile ECG arrhythmia detection Eko AI Eko Health Combines AI ECG with phonocardiogram CardioSignal Precordior Smartphone-based heart failure detection Qaly AI Qaly Inc. FDA-approved AI ECG analysis MedioSmart AI B- SmartMed Predictive hospital ECG triage

11. Ethical and Regulatory Considerations Ethical AI Requires Fairness, Transparency, And Oversight. Technologists Validate AI Outputs For Accuracy And Bias. FDA And CE-Certified Ai/Ml SaMD Frameworks Ensure Safety. Patient Data Privacy And Security Must Be Maintained.

12. Role of Cardiovascular Technologists 1. Understanding AI Tools: Interpreting AI Outputs And Identifying Errors. 2. Quality Assurance: Ensuring Accuracy Of AI Results. 3. Data Management: Curating ECG Datasets. 4. Patient Education: Explaining Ai-based ECG Reports. 5. Professional Growth: Staying Updated With Evolving Technologies .

13. Future of AI in Cardiac Diagnostics Explainable AI (XAI) Will Make Reasoning Transparent. Integration With Multimodal Data (Echo, CT, Clinical History). AI-embedded Handheld Ecg Devices For Point-of-care Use. Expansion To Home-based And Preventive Cardiac Care .

14. Conclusion AI Transforms ECG Interpretation With Speed, Precision, And Insight. Enhances Clinicians, Not Replaces Them. Technologists Are Key To Ethical Ai Use. The Future Is Collaboration Between Human Expertise And Machine Intelligence.

15. Acknowledgement Presented by: Mr. Huseini Kamara Cardiovascular Technologist & Researcher Email: hus [email protected] WhatsApp: [phone]