Development of an Artificial Intelligence-Based Image Recognition System for Time-Sequence Analysis of Tracheal Intubation.

BACKGROUND: Total intubation time (TIT) is an objective indicator of tracheal intubation (TI) difficulties. However, large variations in TIT because of diverse initial and end targets make it difficult to compare studies. A video laryngoscope (VLS) can capture images during the TI process. By using artificial intelligence (AI) to detect airway structures, the start and end points can be freely selected, thus eliminating the inconsistencies. Further deconstructing the process and establishing time-sequence analysis may aid in gaining further understanding of the TI process. METHODS: We developed a time-sequencing system for analyzing TI performed using a #3 Macintosh VLS. This system was established and validated on 30 easy TIs performed by specialists and validated using TI videos performed by a postgraduate-year (PGY) physician. Thirty easy intubation videos were selected from a cohort approved by our institutional review board (B-ER-107-088), and 6 targets were labeled: the lip, epiglottis, laryngopharynx, glottic opening, tube tip, and a black line on the endotracheal tube. We used 887 captured images to develop an AI model trained using You Only Look Once, Version 3 (YOLOv3). Seven cut points were selected for phase division. Seven experts selected the cut points. The expert cut points were used to validate the AI-identified cut points and time-sequence data. After the removal of the tube tip and laryngopharynx images, the durations between 5 identical cut points and sequentially identified the durations of 4 intubation phases, as well as TIT. RESULTS: The average and total losses approached 0 within 150 cycles of model training for target identification. The identification rate for all cut points was 92.4% (194 of 210), which increased to 99.4% (179 of 180) after the removal of the tube tip target. The 4 phase durations and TIT calculated by the AI model and those from the expert exhibited strong Pearson correlation (phase I, r = 0.914; phase II, r = 0.868; phase III, r = 0.964; and phase IV, r = 0.949; TIT, r = 0.99; all P < .001). Similar findings were obtained for the PGY's observations (r > 0.95; P < .01). CONCLUSIONS: YOLOv3 is a powerful tool for analyzing images recorded by VLS. By using AI to detect the airway structures, the start and end points can be freely selected, resolving the heterogeneity resulting from the inconsistencies in the TIT cut points across studies. Time-sequence analysis involving the deconstruction of VLS-recorded TI images into several phases should be conducted in further TI research.

An initial learning experience of tracheal intubation with video laryngoscope: Experiences from a novice PGY.

ABSTRACT: Tracheal intubation is an essential technique for many healthcare professionals and one of the mega code simulations in advanced cardiac life support. In recent years, video laryngoscopy (VL) has provided a rescue for difficult airways during intubation and has proven to have higher success rates. Moreover, VL facilitates a more rapid learning curve for inexperienced doctors.In this article, we report 16 cases intubated with VL by a novice doctor of postgraduate year 1, who shared the learning experience and the difficulties encountered in this case series. We also conducted a statistical analysis to evaluate the learning outcomes of the trainee after 1 month.Our results showed that the overall first-shot success rate was 81.3% for the 16 objectives. Over time, improvements in intubation performance measures, including shortened duration and lower Intubation Difficulty Scale score, have been observed. In this learning project, we found that limitation of mouth opening (<2.5 fingers wide) is an important risk factor for predicting the initial difficulty of tracheal intubation on the novice trainee.For inexperienced doctors, VL produces high first-shot success rates for tracheal intubation and may be useful for training their performance in a short period of time. In addition, mouth opening <3 fingers wide may result in difficult intubation by novice doctors.