Evaluation of the potential of artificial intelligence (AI) for the detection of incipient lung tissue damage using electrical impedance tomography (EIT) through long-term monitoring
Not Applicable
- Conditions
- incipient, potentially pathological changes of the lungs
- Registration Number
- DRKS00033243
- Lead Sponsor
- niversitätsmedizin Göttingen
- Brief Summary
Not available
- Detailed Description
Not available
Recruitment & Eligibility
- Status
- Pending
- Sex
- All
- Target Recruitment
- 20
Inclusion Criteria
healthy subjects
Exclusion Criteria
acute or chronic cardiopulmonary desease, pacemaker
Study & Design
- Study Type
- interventional
- Study Design
- Not specified
- Primary Outcome Measures
Name Time Method The primary endpoint is the distribution of the electrical resistivity of the lungs and their changes during the respiratory cycle. The aim is to test whether artificial intelligence (AI) using neural networks can, in principle, provide equally good or more reliable results in the imaging of continuously measuring absolute electrical impedance tomography (a-EIT) than an evaluation using conventional image reconstruction and interpretation.
- Secondary Outcome Measures
Name Time Method Ventilation, lung volume, fluid content of the lungs