Combining frontal and profile view facial images to predict difficult-to-intubate patients using AI

Ziyu Su; Thomas E. Tavolara; Usama Sajjad; Metin N. Gurcan; Scott Segal; M. Khalid Khan Niazi

doi:10.1117/12.3006465

3 April 2024 Combining frontal and profile view facial images to predict difficult-to-intubate patients using AI

Ziyu Su, Thomas E. Tavolara, Usama Sajjad, Metin N. Gurcan, Scott Segal, M. Khalid Khan Niazi

Author Affiliations +

Proceedings Volume 12927, Medical Imaging 2024: Computer-Aided Diagnosis; 129270L (2024) https://doi.org/10.1117/12.3006465
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

Previous studies suggest that respiratory events are the leading cause of airway management-related deaths. Successful prediction of difficult-to-intubate (DI) patients can help clinicians precisely allocate the respiratory resources for good airway management. However, the current clinical bedside tests are highly biased with respect to anesthesiologists’ experience and yield moderate accuracy with low sensitivity. Therefore, a diagnostic tool with high accuracy, especially sensitivity, is in critical need. For these reasons, we propose an AI-based method to predict DI patients based on their facial images from frontal and profile views. Our ensemble model based on frontal and profile views of the face achieve an AUC of 0.713 and a sensitivity of 68.5%. In addition, our ensemble model increases sensitivity of thyromental distance from 26.9% to 79.2% while maintaining an acceptable trade-off in specificity. Overall, our model can meaningfully augment the accuracy of current clinical tests for DI. We envision that our model may be embedded on smartphones to serve as a bedside test for DI and that our study provides a basis for future studies for AI-based methods on facial images for DI prediction.

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Citation Download Citation

Ziyu Su, Thomas E. Tavolara, Usama Sajjad, Metin N. Gurcan, Scott Segal, and M. Khalid Khan Niazi "Combining frontal and profile view facial images to predict difficult-to-intubate patients using AI", Proc. SPIE 12927, Medical Imaging 2024: Computer-Aided Diagnosis, 129270L (3 April 2024); https://doi.org/10.1117/12.3006465

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