Sector
Health Tech & Life SciencesDigital HealthcareMedical Decision Support
Target Customer
Healthcare & Life SciencesHealthcareProviders
Core Technology
Artificial IntelligenceMachine Learning
Tags (3)
digital-healthcaretestingpulmonary
Geographic Markets
CanadaEuropeNorth AmericaChina
Medibyt Financials
Undisclosed
Capital raised
Cumulative Funding Raised Over Time ($)
Medibyt Lifecycle
Cumulative Funding Raised Over Time
Medibyt News
3 articles
Transitioning from stress electrocardiogram to cardiopulmonary exercise testing: a paradigm shift toward comprehensive medical evaluation of exercise function - European Journal of Applied Physiology
The article is a comprehensive literature review focusing on cardiopulmonary exercise testing (CPET) and its applications in various medical conditions. It highlights the importance of CPET in diagnosing and managing diseases like chronic obstructive pulmonary disease (COPD), cardiovascular diseases, and neuromuscular disorders. The review also discusses the integration of machine learning and artificial intelligence in enhancing the interpretation and predictive capabilities of CPET data. The article underscores the growing interest in using CPET for risk prediction and rehabilitation, particularly in cardiac and pulmonary conditions. Additionally, it addresses the ethical considerations of AI in medicine and the need for standardized guidelines in CPET application.
A Machine Learning Approach to Classify Exercise Limitation Severity Using Cardiopulmonary Exercise Testing - Development and Validation
The article is a collection of references to various studies and papers on the topic of Cardiopulmonary Exercise Testing (CPET) and the use of machine learning and artificial neural networks in interpreting and predicting outcomes from these tests. It discusses the value of CPET in understanding dyspnea and exercise intolerance in respiratory diseases, and the development of machine learning models for predicting outcomes from submaximal exercise tests. The references also include studies on the use of machine learning in predicting hypertension from cardiorespiratory fitness data, and the design and validation of interpretative strategies for CPET.
Not applicable
A Machine Learning Approach to the Interpretation of
Cardiopulmonary Exercise Tests: Development and Validation
Medibyt Team
Or Inbar
CEO & Co-founder
Founder
Niv Caspi
CTO & Co founder
Founder
Employee Info
| Employees (range) | 1-10 |
| Exact count | 3 |
| Team members | 3 |
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