Creation of an artificial intelligence model for intubation difficulty classification by deep learning (convolutional neural network) using face images: an observational study

Abstract

Background Tracheal intubation is the gold standard for securing the airway, and it is not uncommon to encounter intubation difficulties in intensive care units and emergency rooms. Currently, there is a need for an objective measure to assess intubation difficulties in emergency situations by physicians, residents, and paramedics who are unfamiliar with tracheal intubation. Artificial intelligence (AI) is currently used in medical imaging owing to advanced performance. We aimed to create an AI model to classify intubation difficulties from the patient’s facial image using a convolutional neural network (CNN), which links the facial image with the actual difficulty of intubation. Methods Patients scheduled for surgery at Yamagata University Hospital between April and August 2020 were enrolled. Patients who underwent surgery with altered facial appearance, surgery with altered range of motion in the neck, or intubation performed by a physician with less than 3 years of anesthesia experience were excluded. Sixteen different facial images were obtained from the patients since the day after surgery. All images were judged as “Easy”/“Difficult” by an anesthesiologist, and an AI classification model was created using deep learning by linking the patient’s facial image and the intubation difficulty. Receiver operating characteristic curves of actual intubation difficulty and AI model were developed, and sensitivity, specificity, and area under the curve (AUC) were calculated; median AUC was used as the result. Class activation heat maps were used to visualize how the AI model classifies intubation difficulties. Results The best AI model for classifying intubation difficulties from 16 different images was generated in the supine-side-closed mouth-base position. The accuracy was 80.5%; sensitivity, 81.8%; specificity, 83.3%; AUC, 0.864; and 95% confidence interval, [0.731-0.969], indicating that the class activation heat map was concentrated around the neck regardless of the background; the AI model recognized facial contours and identified intubation difficulties. Conclusion This is the first study to apply deep learning (CNN) to classify intubation difficulties using an AI model. We could create an AI model with an AUC of 0.864. Our AI model may be useful for tracheal intubation performed by inexperienced medical staff in emergency situations or under general anesthesia.

Artificial intelligence and anesthesia: a narrative review

“…Furthermore, in this case too, models with high sensitivity (90% for BRF) and models with high specificity and accuracy, respectively 91% and 83%, if we consider light gradient boosting machines (LGBM), have been identified. Finally, Hayasaka et al ( 15 ) developed a convolutional neural network (CNN) algorithm capable of evaluating the difficulty of intubation with an excellent AUC of 0.864 just by evaluating patients’ facial pictures, making it a promising tool for predicting these casualties in advice.…”

Predicting difficult airway intubation in thyroid surgery using multiple machine learning and deep learning algorithms

“…( 24 ); Studies have also shown that difficult airways can be distinguished from frontal images using depth learning model sets ( 25 ). Likewise, it has been shown that the CNN algorithm can classify difficult airways ( 26 ). Our study is the first to use a large number of machine learning algorithms and deep learning algorithms simultaneously to predict difficult airways in patients with thyroid problems.…”

New technologies in airway management: A review

“…The AI model recognized facial contours and then identified expected intubation difficulties, with 80.5% accuracy, 81.8% sensitivity, 83.3% specificity and an AUC of 0.864 (CI 95%, 0.731–0.969). [20]…”

“…The AI model recognized facial contours and then identified expected intubation difficulties, with 80.5% accuracy, 81.8% sensitivity, 83.3% specificity and an AUC of 0.864 (CI 95%, 0.731-0.969). [20] Other studies have approached the role of machine learning as a complement to the physical exam, performing automatic facial analysis and detecting morphological traits related to difficult airways. [21,22] It has also been applied to monitoring pediatric airways, enhancing the detection of critical incidents and providing early warnings to the clinician.…”