None listed
Conditions
Brief summary
When a patient has surgery under general anaesthesia, it is one of the jobs of the anaesthetist to secure and maintain a patent airway. Placing a breathing tube into a patients’ windpipe is one of the most commonly performed procedures to secure the patient’s airway. Though this so-called intubation is usually an easy task for the anaesthetist, sometimes it can be difficult. This leads to a potentially life-threatening situation. Hence, it is part of the routine preoperative anaesthetic assessment to examine a patients’ airway in order to attempt to predict how difficult it will be to intubate them. There are a number of examination techniques and tools that have been developed for this, but none are sensitive enough to rely on. Deep learning algorithms are able to learn to map complex and subtle relationships between input variables to a known output. This relationship is learned from the data, and the algorithm can then be used to predict outputs for future inputs. Deep learning algorithms have been successfully applied to a wide range of computer vision tasks This research will apply deep learning to patients basic demographics and photographs of patients face and neck in order to predict how difficult they will be to intubate. We will recruit patients from the perioperative anaesthetic assessment clinic at Royal Perth Hospital who are expected to require intubation for their surgery. We will take 3-dimensional stereophotographs of the patients’ face and neck in various positions. The difficulty of intubation will be recorded at the time of surgery. We train the deep learning model using simple patient data such as age, gender height, weight, and the images as an input, and the intubation difficulty as an output. We will then attempt to predict how difficult intubation will be for patients given their images as an input. We will also compare the results of the deep learning algorithm to anaethetists predictions.
Interventions
Patients will be recruited from the perioperative anaesthetic assessment clinic at Royal Perth Hospital (RPH) over a 6-month period. Following patients' informed consent we will take a 3-Dimensional digital photographs of their face front on and side on. We will record basic demographics including age, gender, weight, and height. When the patient undergoes surgery the responsible anaesthetist will complete a data collection that assess the difficulty of their intubation. The timing of the 3D pho
Patients will be recruited from the perioperative anaesthetic assessment clinic at Royal Perth Hospital (RPH) over a 6-month period. Following patients' informed consent we will take a 3-Dimensional digital photographs of their face front on and side on. We will record basic demographics including age, gender, weight, and height. When the patient undergoes surgery the responsible anaesthetist will complete a data collection that assess the difficulty of their intubation. The timing of the 3D photographs in relationship to the surgery will be variable given the heterogeneous group of patients that are seen at the pre-anaesthetic clinic, but in general surgery is expected to follow around 1 to 3 months following image acquisition. We will use this information to develop and to train a deep learning algorithm which uses patient demographics and 3D photograph as an input, and predict difficulty of intubation as an output.
Sponsors
Dr Jonathon Stewart
Eligibility
Sex/Gender
All
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
Adult patients undergoing elective surgery that are anticipated to require intubation.
Exclusion criteria
Patients will be excluded if after enrollment they do not undergo intubation at surgery, their surgery is cancelled, or their data collection form is not completed by the treating anaesthetist.
Outcome results
None listed