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

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 - KI-EIT

Status

Active, not recruiting

Phases

Unknown

Study type

Interventional

Source

DRKS

Registry ID

DRKS00033243

Enrollment

Registered

2024-01-18

Start date

2024-05-01

Completion date

Unknown

Last updated

2025-04-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

incipient, potentially pathological changes of the lungs

Interventions

Group 1: All subjects are examined using MRI of the thorax in order to determine the individual body shape and the exact position and shape of the organs. Immediately afterwards, electrical impedance

Eligibility

Sex/Gender

All

Age

18 Years to No maximum

Inclusion criteria

Inclusion criteria: healthy subjects

Exclusion criteria

Exclusion criteria: acute or chronic cardiopulmonary desease, pacemaker

Design outcomes

Primary

Measure	Time frame
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.	—

Measure

Time frame

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

Measure	Time frame
Ventilation, lung volume, fluid content of the lungs	—

Countries

Germany

Contacts

Public ContactLeif Saager

Universitätsmedizin Göttingen, Klinik für Anästhesiologie

leif.saager@med.uni-goettingen.de+49 5513967710

Outcome results

None listed

Source: DRKS (via WHO ICTRP) · Data processed: Feb 4, 2026