Artificial intelligence methods on lung ultrasound images is able to predict the presence of lung related signs of COVID-19 with a sensitivity around 90%. Artificial intelligence methods on lung ultrasound images is able to predict the presence of…
ID
Bron
Verkorte titel
Aandoening
COVID19
Ondersteuning
Onderzoeksproduct en/of interventie
Uitkomstmaten
Primaire uitkomstmaten
Lung ultrasound sensitivity to predict COVID-19 diagnosis
Achtergrond van het onderzoek
We will investigate the value of ultrasound as a first-line examination tool in the process of diagnosing COVID-19. We aim to estimate the test characteristics of artificial intelligence methods on lung ultrasound images for diagnosis of COVID-19 on patients entering the hospital emergency department with COVID-19 symptoms. The outcomes of this study will be relevant for hospitals, but also for any other situations or regions where PCR testing or CT scanning is less available.
Doel van het onderzoek
Artificial intelligence methods on lung ultrasound images is able to predict the presence of lung related signs of COVID-19 with a sensitivity around 90%. Artificial intelligence methods on lung ultrasound images is able to predict the presence of lung related signs of COVID-19 with a specificity around 80%.
Onderzoeksopzet
after entering the emergency department
Onderzoeksproduct en/of interventie
Lung ultrasound
Algemeen / deelnemers
Wetenschappers
Belangrijkste voorwaarden om deel te mogen nemen (Inclusiecriteria)
- patients who enter the emergency department
- 18 years of age or older
- capacitated (able to make a reasonable judgement of their own interests with regards to the study)
- with (newly developed) symptoms of COVID-19 (fever or chills, cough, shortness of breath or difficulty breathing, new loss of taste or smell, sore throat, congestion or runny nose)
- signed informed consent
Belangrijkste redenen om niet deel te kunnen nemen (Exclusiecriteria)
- pregnancy
- contra-indications for ultrasound
Opzet
Deelname
Voornemen beschikbaar stellen Individuele Patiënten Data (IPD)
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In overige registers
| Register | ID |
|---|---|
| NTR-new | NL9247 |
| Ander register | Not-WMO METC azM : METC2020-2229 |