Medibyt Overview
Medibyt offers an AI-powered platform for cardiopulmonary exercise testing (CPET) that automates interpretation and streamlines clinical workflows. Its flagship product, CPETWise, transforms complex physiological data into structured, clinician-ready reports within minutes, reducing analysis time from nearly an hour to just a few minutes. Compatible with existing metabolic systems, the cloud-based solution supports remote diagnostics and integrates seamlessly into hospital and outpatient settings. Developed with input from leading cardiopulmonary experts, CPETWise enhances diagnostic accuracy, increases testing throughput, and enables broader access to CPET in routine care and clinical research
Cumulative Funding Raised Over Time ($)
—May 2024
Latest News
Not Applicable
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.
Not applicable
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
| Sector | Health Tech & Life Sciences |
Funding
| Total funding | Undisclosed |
| Last funding | Undisclosed |
| Stage | Pre-Seed |
| Rounds | 1 |
| Investors | 0 |
Team Members
3
Employees: 1-10
Web & Social Links
| Website | www.medibyt.com/ |
Locations
Greenwork Business Park Building D, 1st floor, Yakum, 6097200, Israel
Photos & Videos
No files yet
Medibyt Business
Business models
B2B
Product stage
R&D
Employees
1-10
Sector
Health Tech & Life SciencesDigital HealthcareMedical Decision SupportTarget Customer
Healthcare & Life SciencesHealthcareProvidersCore Technology
Artificial IntelligenceMachine LearningTags (3)
digital-healthcaretestingpulmonaryGeographic Markets
CanadaEuropeNorth AmericaChinaMedibyt Financials
Undisclosed
Total funding
Undisclosed
Capital raised
Undisclosed
Last funding
Pre-Seed
Funding stage
1
Total rounds
0
Investors
Cumulative Funding Raised Over Time ($)
—May 2024
Private Equity Funding
Pre-Seed
May 2024
Undisclosed
Medibyt Lifecycle
Cumulative Funding Raised Over Time
—May 2024
All Events
Pre-Seed
May 2024
Undisclosed
Medibyt News
3 articles
Not Applicable
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.
Not applicable
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
Employee Info
| Employees (range) | 1-10 |
| Exact count | 3 |
| Team members | 3 |
Similar Companies
Similar companies data is computed dynamically and not stored in the entity profile. This section requires implementing a similarity query (e.g. by classification overlap, sector, district).
This entity has 8 classification IDs that could be used for matching.
Medibyt Internal
Recent Updates
No update log entries.
Note: Free-text notes visible in the original Finder "Internal" tab are not present in the imported profile data. Only the update audit log is available.
Admin Info
| Confidence | 80/100 |
| Missing | sector, video or image, markets, external profiles, not claimed |
| BI Verification | Yanina Wainscheinker |
| Registrar ID | 516971959 |
| Phone | +972-544531477 |
| Creator | Or Inbar |
| Creator email | or@medibyt.com |
| Last update | 2024-07-02T00:00:00.000Z |
| Created | 2024-07-02T00:00:00.000Z |
| Claimed | Yes |