ClinicalTrials.gov
public full text
Public Domain
Novel Sensors and Artificial Intelligence for Detection of Acute Pulmonary Exacerbations in Cystic Fibrosis and Bronchiectasis
The goal of this observational study is to learn about new sensors that measure changes in the body that happen during a chest infection, such as breathing rate and heart rate. This study will be with adults who have either cystic fibrosis or bronchiectasis who start treatment with intravenous antibiotics (usually for around 2 weeks). The main question it aims to answer is the change in sensor measure (such as heart rate) between the start and end of treatment for a chest infection. No follow up visits are required for this study.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Wide Scale Monitoring for Acute Respiratory Infection Using a Mobile-Based Study Platform#833823
- Expert system for COVID-19 diagnosis#705814
- Artificial Intelligence - SARS-CoV-2 (COVID-19) Risk Evaluation#1164031
- Implementing artificial intelligence in chest diagnostics for lung disease: A mixed-methods evaluation#1751
- Assessment of Oral and Nasal Breathing With Sensors Analyzing Algorithm#1032843
Record · ID 853604
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive