An AI-powered system for pre-operative airway assessment improves risk prediction by combining landmark detection with explainable outputs. This model enhances clinical accuracy, supports safer surgeries, and offers scalable deployment. It represents a key advancement in integrating AI with real-world medical workflows.
-- A new advancement in pre-surgical care is using artificial intelligence to improve how clinicians assess the airway, which is a critical factor in all surgeries requiring general anesthesia. The project, titled Towards Artificial Intelligence-enabled Medical Pre-operative Airway Assessment, introduces a machine learning model that offers consistent and explainable predictions, helping identify difficult airway cases before they become life-threatening challenges in the operating room.
Traditionally, pre-operative airway assessment relies on physical examinations and clinician judgment to evaluate risk. However, such evaluations can vary widely in accuracy depending on practitioner experience, lighting, and patient anatomy. The new system uses convolutional neural networks (CNNs) to detect facial and neck landmarks such as the mentum, thyroid notch, and suprasternal notch. These are the same markers used in current clinical practice, but they are now able to be detected with higher precision and reliability.
The model is trained using a method that repeatedly tests its accuracy on different parts of the dataset, which helps ensure the results apply well to new patients. An advanced optimization technique, Adabelief, is used to fine-tune how the model learns. Together, these methods help the model stay accurate and stable, with errors kept consistently low throughout testing.
What makes this work stand out is its alignment with the real demands of healthcare. Most AI systems in medicine do not get used in real settings because they make predictions without showing the logic behind them. By contrast, this approach delivers traceable and explainable predictions that mirror the logic of clinicians, allowing for easier integration into existing workflows and improved trust in real-world deployment.
A key contributor to the group behind this study is Jinshuo Zhang. Zhang is a master’s student in Mechanical Engineering at the National University of Singapore, with academic grounding in machine vision, data-driven engineering, and intelligent control systems. His hands-on contributions included model development, dataset annotation, and performance tuning. These skills are further enforced through academic research and applied experience in real-time embedded systems during his internship at EDAG Engineering and Design.
By combining domain knowledge in AI with a practical understanding of clinical constraints, this project sets a new benchmark for pre-operative airway assessment tools. It marks a move away from variable, manual evaluations and toward a standardized intelligent system that improves both patient safety and clinical preparedness.
Contact Info:
Name: Jinshuo Zhang
Email: Send Email
Organization: Jinshuo Zhang
Website: https://scholar.google.com/citations?hl=en&user=f35LC2oAAAAJ
Release ID: 89165308
If there are any deficiencies, discrepancies, or concerns regarding the information presented in this press release, we kindly request that you promptly inform us by contacting error@releasecontact.com (it is important to note that this email is the authorized channel for such matters, sending multiple emails to multiple addresses does not necessarily help expedite your request). Our dedicated team is committed to addressing any identified issues within 8 hours to guarantee the delivery of accurate and reliable content to our esteemed readers.