Congenital heart defects (CHD) are structural abnormalities of the heart that are present at birth. They can range from mild conditions with minimal impact on health to complex defects requiring surgical intervention. Early diagnosis and appropriate medical care are crucial to managing congenital heart defects and improving long-term outcomes.
"To date, the global prevalence of CHD is ∼9 per 1000 live births, with considerable geographical variation. "
Current methods for early detection of congenital heart disease include prenatal ultrasound and fetal echocardiography, which can detect structural abnormalities before birth. In addition, advances in genetic testing have made it possible to identify certain genetic mutations associated with congenital heart defects, allowing early intervention and treatment.
Auscultation, the process of listening to the heart with a stethoscope, is a commonly used method of detecting certain congenital heart defects by identifying abnormal heart murmurs or sounds. Monitoring blood oxygen saturation, often using pulse oximetry, is another valuable tool for early detection, as it can reveal lower levels of oxygen in the blood, indicating potential heart abnormalities and the need for further evaluation.
AI Detects Defects
While more serious heart defects are detected early, smaller defects, such as small ventricular septal defects or atrial septal defects, may not cause symptoms until later in life and are often discovered incidentally during routine check-ups or when other health problems are being investigated.
The aim is to detect most defects, whether severe or not, in the first few days after birth in hospital. Major defects trigger immediate investigation, whereas routine screening for minor defects would require more time and special skills.
However, time is of the essence because the earlier a defect is detected, the easier it is to repair. This is particularly true for septal defects, which can be detected by auscultation.
HS Analysis creates an AI-assisted stethoscope that can quickly identify the need for further, more thorough examination.
An electronic stethoscope, enhanced with artificial intelligence (AI), can help detect congenital heart defects in a fascinating way. When the stethoscope is placed on a person’s chest, it captures the sounds produced by their heart. These sounds are then analyzed by AI algorithms that have been trained to recognize patterns associated with specific heart abnormalities.
The AI algorithms listen for irregularities, such as abnormal heart murmurs or sounds, that could indicate the presence of a congenital heart defect. By comparing the captured heart sounds to a vast database of known heart conditions, the AI can provide valuable insights and alert healthcare professionals if there is a potential issue.
This technology empowers doctors to make more accurate diagnoses, even in the absence of obvious symptoms, enabling early intervention and appropriate treatment. It’s like having a highly trained expert listening to the heart, helping identify potential heart defects that might have otherwise gone unnoticed.
By harnessing the power of AI, electronic stethoscopes are transforming the way we detect congenital heart defects, providing a non-invasive, accessible, and efficient method for early detection and improved patient care.
Use of Trained Models In Hardware
Once trained using the HSA KIT, AI models can be deployed in various ways according to the device’s form factor, power requirements, and local infrastructure. These deployments include:
- On-Site Edge Deployment:
- AI models are executed directly on dedicated devices at the edge.
- This is ideal for scenarios like on-premises diagnosis where immediate, real-time analysis is crucial.
- Hardware indicators notify a physician to perform further, more sophisticated and costly tests.
- Centralized Deployment:
- The device connects to a more powerful machine hosting and executing the model centrally.
- This setup suits use cases that involve centralized logging or Hospital Information System (HIS) integration.
- Feedback to the device allows the same hardware notification options as mentioned above.
HSA KIT Integration
Case List
The first step in our Heart Sounds Analysis module involves organizing patient cases into a well-structured Case List view. This view automatically sorts and groups cases, presenting essential metadata—such as patient demographics, heart sound types, and analysis progress—in a clear and accessible manner. By offering a bird’s-eye view of all patients, this feature allows clinicians to efficiently manage and monitor their caseload, ensuring that each case is addressed in a timely and informed manner. This streamlined organization aids in quick case retrieval, prioritization, and overall workflow efficiency.
Case Viewer
After selecting a case, users gain access to a comprehensive detail view that combines heart sound audio data with the AI-detected annotations. This view is designed for transparency, enabling users to trace each step of the AI’s analysis and verify the results against the original recordings. The intuitive interface allows for seamless verification, empowering clinicians to confirm the accuracy of the automated findings and make any necessary adjustments with confidence.
Suggested Actions
For every case, the HSA KIT processes the data and detections to derive suggested actions, guiding clinicians on potential next steps. These recommendations are crafted from the AI’s interpretation of the heart sounds, offering a data-driven approach to patient management. The system ensures that users receive timely and relevant advice, enhancing the efficiency and effectiveness of clinical decision-making.
Raw Results
For full transparency, the HSA KIT offers users the ability to view the underlying data associated with each case. This feature allows clinicians to delve into the raw heart sound files and the AI’s analytical processes, giving them complete visibility into how conclusions and suggested actions were derived. This openness supports thorough evaluation and reinforces trust in the system’s outputs.
Model Training
In the HSA KIT, model training is built on a foundation of well-organized ground truth data, which users can manage and collect in a centralized repository. The platform offers a no-code training solution, making it accessible to users of all technical levels.
Post-training, detailed metrics are displayed, enabling users to assess the model’s performance thoroughly. This data-driven approach allows for iterative training sessions, where users can experiment with different settings and compare results to achieve the best outcomes. All training parameters are automatically logged and documented, providing a clear history of the model’s development.
Once optimized, the trained model is integrated into the central AI model overview in HSA KIT, where it can be selected for future heart sound analyses, ensuring that the most effective models are readily available.
Creation of Ground Truth Data
Each AI model needs ground truth data to be trained on; reliable and truthful data that is provided by humans.
Our advanced heart sound annotation technology enables precise segmentation of heartbeats into distinct sections, facilitating the detection of pathological conditions with exceptional accuracy. By meticulously identifying and categorizing each component of the cardiac cycle—from the initial heart sound (S1) through systole and diastole—we empower medical professionals to isolate and analyze specific segments of interest.
This allows for the early detection of abnormalities, such as murmurs or arrhythmia, ensuring prompt diagnosis and intervention. Our tool is designed to support clinicians in delivering superior patient care by providing clear, reliable insights into heart sound patterns.
In addition to the precise annotation and segmentation of heartbeats, our technology leverages advanced visual features such as the Spectrogram and Wave Meter to enhance acoustic analysis.
- The Spectrogram provides a detailed visual representation of the frequency content of heart sounds over time, allowing clinicians to observe subtle variations in pitch and intensity that may indicate underlying pathologies.
- Meanwhile, the Wave Meter offers a clear visualization of the amplitude and timing of heart sounds, making it easier to identify irregularities or interruptions in the cardiac cycle.
These visual tools work in tandem with acoustic analysis, offering a comprehensive approach to heart sound evaluation that significantly improves diagnostic accuracy and aids in the early detection of cardiovascular anomalies.
Conclusion
The integration of AI in medical devices like electronic stethoscopes revolutionizes the detection and treatment of congenital heart defects. By enabling early, precise, and non-invasive diagnosis, this technology greatly enhances patient care and outcomes.