Assessment of nasogastric tube placement using deep learning
Research type
Research Study
Full title
Application of deep learning to chest radiographs for the assessment of nasogastric tube placement
IRAS ID
229082
Contact name
Dermot Mallon
Contact email
Sponsor organisation
Imperial College NHS Trust
Clinicaltrials.gov Identifier
17SM4053, Sponsor reference
Duration of Study in the UK
1 years, 11 months, 31 days
Research summary
The first aim of this study is to develop a deep learning algorithm to identify incorrectly positioned nasogastric (NG) tubes. NG tubes are commonly used to provide enteral intake to acutely unwell patients. Nasogastric tubes are generally inserted at the bedside without specialised guidance equipment. Feeding through an incorrectly placed NG tube is a so-called “never event“ due to the potential to cause significant morbidity and mortality. Therefore, numerous bedside and radiological tests are performed to ensure satisfactory positioning prior to use. Despite these precautions, there remain cases where the position of an NG tube is incorrectly assessed on the chest radiograph.\n\nDeep learning represents a potentially fast, cheap and reliable method of determining NG tube position. The deep learning algorithm will be trained on chest radiographs that have already been performed and assessed by a radiologist at our centre. The accuracy of the the deep learning algorithm will be compared with assessment by clinicians and radiologists.\n\nUsing the methods developed in the above study, the ability of the deep learning algorithm when applied to other radiological investigations to predict further clinical outcomes such as length of stay and mortality, which cannot be determined by the radiologist, will be assessed.
REC name
South West - Frenchay Research Ethics Committee
REC reference
17/SW/0210
Date of REC Opinion
19 Sep 2017
REC opinion
Further Information Favourable Opinion