Hemoptysis, Pulmonary Disease, Tumor
Conditions
Keywords
bronchoscope
Brief summary
The primary aim is to develop a software algorithm that has the capacity to detect the normal 18 anatomical structures of the lung by using the position of the scope during the bronchoscopy procedure and using existing bronchoscopy technology.
Detailed description
2.000 subjects, preferable 150-200 number of patients per site. The 2.000 videos will be divided into an 80%/20% split (training and testing). It is a wish to obtain a spread of videos coming from across Europe, preferable from Germany, France and Denmark, with up to 800 videos coming from Denmark The patient population indicated are patients indicated for full airway bronchoscopy. Of this group it is the aim to enrol the following: 1. Patients with obstructing tumors (when tumor blocks the view of the camera) - no more than 5% (120 patients) of studies 2. Bronchoscopies on indication of haemoptysis (active bleeding) - no more than 5% (120 patients) of studies 3. Patients with stents or valves - no more than 5% (120 patients) of studies bronchoscopies. 4. Patients with former lung operations, partial or full resections - no more than 2% (48 patients).
Interventions
DIAGNOSTIC_TESTBronchoscopy
All subjects who are scheduled for a bronchoscopy may be eligible.
Sponsors
Ambu A/S
Qmed Consulting A/S
Study design
Observational model
OTHER
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
1. Oral and written participant information has been given (NA for Denmark) 2. The subject has signed informed consent (NA for Denmark) 3. Patients indicated for full airway bronchoscopy (identifying all 18 anatomical segments) 4. Patients older than 18
Exclusion criteria
1. Subject with serious lung disease which investigator considers not appropriate for this examination. 2. Lack of patient cooperation for bronchoscopy (patient with mental disorders, dysgnosia, psychological disorder…) 3. Subjects unable to give consent (NA for Denmark)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of anatomical segments accessed during bronchoscopy | 1 day | accurate photo documentation of all 18 anatomical segments +/- abnormalities |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Number of anatomical segments identified | 1 day | Identified anatomical segments by the core lab/ Identified anatomical segments by the Machine Learning (after training, using the test videos). |
| Rate of total number of lesions detected | 1 day | total lesions detected by the core lab/ total lesions detected by Machine Learning (after training, using the test videos). |
| Number of sections and lesions detected by core lab and machine learning | 1 day | Anatomical sections + lesions detected by core lab versus anatomical sections + lesions detected by machine learning |
Countries
Denmark
Outcome results
None listed