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| Name | Class |
|---|---|
| National University of Singapore | OTHER |
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We're developing a new AI, which uses advanced computer technology to help doctors identify patients who might have a difficult airway before surgery or emergency procedures. Sometimes, when a person needs help breathing, doctors have to insert a tube into their airway. This can be challenging for some people due to the shape of their mouth, throat, or neck. We hope that AI will look at a patient's facial features to predict if there might be any difficulties.
AINFAS (Artificial Intelligence-enabled system for airway assessment) is an innovative AI system we're developing to revolutionize airway management in healthcare. This advanced artificial intelligence is designed to analyze patient characteristics and predict potential difficulties in airway management before any medical procedure that might require breathing assistance. Using sophisticated machine learning algorithms, AINFAS processes data such as facial structure and neck anatomy to assess the likelihood of a difficult airway. This non-invasive, rapid assessment tool has potential applications across various healthcare settings, including pre-operative assessments, emergency departments, intensive care units, and even pre-hospital care. By providing early identification of potential airway challenges, AINFAS aims to enhance patient safety, improve resource allocation, and provide a standardized, objective method of airway assessment. Currently in development and testing, this AI system represents a significant step forward in using technology to enhance clinical decision-making and patient safety in critical aspects of medical care.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| General anaesthesia surgery |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| AI-Assisted Preoperative Airway Risk Assessment | Other | participants having their photograph taken using a tablet device following a standardized protocol to capture relevant facial and neck features. These photographs will then be analyzed using software, which assesses the images for potential indicators of a difficult airway. |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of AINFAS in Predicting Difficult Airways | To identify and characterize the key predictive parameters that contribute to the AI system's overall ability to detect difficult airways. | Assessment will occur from the time of preoperative AI analysis through to the completion of the intubation procedure, typically within 48 hours. |
| Measure | Description | Time Frame |
|---|---|---|
| Identification of Novel Predictors of Difficult Airways | To identify and characterize new predictive parameters that contribute to the AI system's overall ability to determine the likelihood of a difficult airway. | Data analysis will be conducted after completion of all participant procedures, typically within 2 years of the last participant's assessment. |
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Inclusion Criteria:
Exclusion Criteria:
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Patients that undergoing surgery in National University Hospital Singapore Only.
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| Name | Affiliation | Role |
|---|---|---|
| Will Ne-Hooi Loh | National University Hospital, Singapore | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| National University Hospital Singapore | Singapore | 119074 | Singapore |
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