Value Proposition:
· Enhances Diagnostic Capabilities: Captures, stores, and analyzes spectral Doppler audio data to improve real-time and retrospective evaluations.
· Non-Invasive and Real-Time: Provides real-time monitoring of blood flow, tissue elasticity, and other biological attributes without disrupting standard ultrasound procedures.
· AI and Machine Learning Integration: Potential to use AI-driven analytics for automated detection of pathological tissue and early disease diagnosis.
· Broad Clinical Applications: Can be applied to various organs, including the heart, liver, brain, spinal cord, and lungs, improving diagnostic precision.
Unmet Need:
Current ultrasound systems lack the ability to store and process real-time spectral Doppler audio data, leading to diagnostic inefficiencies and potential data loss. Sonographers rely on live Doppler audio feedback during examinations, but existing systems do not allow for post-scan playback or computational analysis. Additionally, conventional imaging-based diagnostics focus on visual waveforms rather than leveraging acoustic signals for deeper physiological insights. This technology addresses the growing demand for real-time, AI-enhanced diagnostic tools in cardiology, radiology, and critical care medicine, where accurate blood flow analysis is crucial for detecting vascular diseases, tumors, and organ dysfunction.
Technology Description:
Researchers at Johns Hopkins University have developed a novel ultrasound system that:
- Records and stores spectral Doppler ultrasound audio to enable retrospective analysis and AI-driven diagnostics.
- Uses machine learning algorithms to assess blood flow, tissue compliance, and vascular abnormalities.
- Determines key biological attributes such as blood pressure, tissue elasticity, and organ health status using real-time audio processing.
- Applies to multiple clinical settings, including cardiology, neurology, and emergency medicine, for enhanced diagnostic precision.
- Supports real-time visualization, displaying waveforms and analyzed data in an intuitive format for medical professionals.
This next-generation ultrasound system integrates with existing imaging platforms, allowing seamless adoption into hospital and outpatient settings.
Stage of Development:
· Prototype Developed: Working model successfully records, stores, and processes spectral Doppler audio.
· Current Focus: Optimizing user interface and AI analytics to extract more clinically relevant insights.
· Next Steps: Clinical trials to validate accuracy, reliability, and impact on diagnostic decision-making.
Data Availability:
· Data available upon request.
Select Publications:
Kerensky, Max J., et al. "Tethered spinal cord tension assessed via ultrasound elastography in computational and intraoperative human studies." Communications Medicine 4.1 (2024): 4. https://doi.org/10.1038/s43856-023-00430-6.
