Defining difficult laryngoscopy findings by using multiple parameters: A machine learning approach

The Future of Artificial Intelligence Using Images and Clinical Assessment for Difficult Airway Management

Artificial intelligence (AI) algorithms, particularly deep learning, are automatic and sophisticated methods that recognize complex patterns in imaging data providing high qualitative assessments. Several machine-learning and deep-learning models using imaging techniques have been recently developed and validated to predict difficult airways. Despite advances in AI modeling. In this review article, we describe the advantages of using AI models. We explore how these methods could impact clinical practice. Finally, we discuss predictive modeling for difficult laryngoscopy using machine-learning and the future approach with intelligent intubation devices.

Identification of difficult laryngoscopy using an optimized hybrid architecture

BACKGROUND

Identification of difficult laryngoscopy is a frequent demand in cervical spondylosis clinical surgery. This work aims to develop a hybrid architecture for identifying difficult laryngoscopy based on new indexes.

METHODS

Initially, two new indexes for identifying difficult laryngoscopy are proposed, and their efficacy for predicting difficult laryngoscopy is compared to that of two conventional indexes. Second, a hybrid adaptive architecture with convolutional layers, spatial extraction, and a vision transformer is proposed for predicting difficult laryngoscopy. The proposed adaptive hybrid architecture is then optimized by determining the optimal location for extracting spatial information.

RESULTS

The test accuracy of four indexes using simple model is 0.8320. The test accuracy of optimized hybrid architecture using four indexes is 0.8482.

CONCLUSION

The newly proposed two indexes, the angle between the lower margins of the second and sixth cervical spines and the vertical direction, are validated to be effective for recognizing difficult laryngoscopy. In addition, the optimized hybrid architecture employing four indexes demonstrates improved efficacy in detecting difficult laryngoscopy.

TRIAL REGISTRATION

Ethics permission for this research was obtained from the Medical Scientific Research Ethics Committee of Peking University Third Hospital (IRB00006761-2015021) on 30 March 2015. A well-informed agreement has been received from all participants. Patients were enrolled in this research at the Chinese Clinical Trial Registry ( http://www.chictr.org.cn , identifier: ChiCTR-ROC-16008598) on 6 June 2016.

Predisposing Factors of Difficult Tracheal Intubation Among Adult Patients in Aliabad Teaching Hospital in Kabul, Afghanistan – A Prospective Observational Study

Background

Airway management may be a considerable challenge for anesthesiologists. Currently used preoperative screening tests are known to lack sufficient specificity and sensitivity. Nevertheless, preoperative screenings and the combination of various tests are highly recommended to reduce the risk of unexpected difficult or failed airway management.

Purpose

This study aims to determine if socio-demographic characteristics can predict difficult intubation among adult patients scheduled for elective surgeries under general anesthesia in Aliabad Teaching Hospital, Kabul, Afghanistan.

Methods

A total of 341 patients were selected based on consecutive sampling method. Informed consent forms were obtained before inclusion in the study. Data were collected using a data collection form. Age, gender, ASA physical status and ethnicity were recorded for each participant. Airway assessment tests such as mouth opening (MO), thyromental distance (TMD), and Mallampati classes, inability to prognath (AP) and neck mobility and size (NM) category were conducted by research team. Data were initially entered into an Excel data sheet and then exported to SPSS Statistics version 22 for analysis.

Results

From 28 October 2018 to 30 January 2019, a total of 341 patients included in the study. Of these, 193 (56.6%) were male and 148 (43.4%) were female. The mean age of the subjects was 36.98 ± 15.048 years. More than half (54.5%) of the study population were Tajiks. Patients from the Hazara ethnicity, female patients, older patients and those suffering from systemic diseases found to be more difficult to intubate. We recognized that, Mallampati classes ≥3, small MO, short TMD, AP, reduced NM were also associated with difficult intubation. Multiple logistic regression analysis of the associated factors determined that increased age more than 40 years, AP and small MO were independent predictors of difficult intubation.

Conclusion

The study findings show that Hazara ethnicity, female patients, increasing age and systemic disease have significant associations with difficult intubation. Mallampati classes III and IV, MO ≤4 cm, TMD ≤6 cm, and reduced NM had higher risks of difficult intubation. Multiple logistic regression analysis determined that increased age, AP and MO were independent predictors for difficult intubation.

Multivariable prediction models for difficult direct laryngoscopy: Systematic review and literature metasynthesis

CONTEXT

The difficult airway is an important scenario in anaesthesia due to the impact of its potential complications, and the difficulty in predicting its presence in current clinical practice.

METHODS

Systematic review of articles in English and Spanish retrieved from MEDLINE (Ovid), LILACS and EMBASE up to March 2018. The search strategy was defined by the authors. The reviewers uploaded the studies to specially designed tables in order to qualitatively analyse the results of each paper.

RESULTS

A total of 3602 studies were identified. Thirty-four of these were included in the qualitative review. The most commonly used definition of difficulty was the Cormack-Lehane 3 or 4 classification, with a weighted mean incidence of 7.23%. The most relevant finding was the methodological weaknesses in obtaining these scales.

CONCLUSIONS

Available prediction models show limited discrimination, and weaknesses were detected in the methodology used to develop these prediction rules.

Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study

Background

There is still room for improvement in the modified LEMON (look, evaluate, Mallampati, obstruction, neck mobility) criteria for difficult airway prediction and no prediction tool for first-pass success in the emergency department (ED).

Objective

We applied modern machine learning approaches to predict difficult airways and first-pass success.

Methods

In a multicenter prospective study that enrolled consecutive patients who underwent tracheal intubation in 13 EDs, we developed 7 machine learning models (eg, random forest model) using routinely collected data (eg, demographics, initial airway assessment). The outcomes were difficult airway and first-pass success. Model performance was evaluated using c-statistics, calibration slopes, and association measures (eg, sensitivity) in the test set (randomly selected 20% of the data). Their performance was compared with the modified LEMON criteria for difficult airway success and a logistic regression model for first-pass success.

Results

Of 10,741 patients who underwent intubation, 543 patients (5.1%) had a difficult airway, and 7690 patients (71.6%) had first-pass success. In predicting a difficult airway, machine learning models—except for k-point nearest neighbor and multilayer perceptron—had higher discrimination ability than the modified LEMON criteria (all, P≤.001). For example, the ensemble method had the highest c-statistic (0.74 vs 0.62 with the modified LEMON criteria; P<.001). Machine learning models—except k-point nearest neighbor and random forest models—had higher discrimination ability for first-pass success. In particular, the ensemble model had the highest c-statistic (0.81 vs 0.76 with the reference regression; P<.001).

Conclusions

Machine learning models demonstrated greater ability for predicting difficult airway and first-pass success in the ED.

Machine Learning, Deep Learning, and Closed Loop Devices-Anesthesia Delivery

With the tremendous volume of data captured during surgeries and procedures, critical care, and pain management, the field of anesthesiology is uniquely suited for the application of machine learning, neural networks, and closed loop technologies. In the past several years, this area has expanded immensely in both interest and clinical applications. This article provides an overview of the basic tenets of machine learning, neural networks, and closed loop devices, with emphasis on the clinical applications of these technologies.

Predictive model for difficult laryngoscopy using machine learning: retrospective cohort study

BACKGROUND

Both predictions and predictors of difficult laryngoscopy are controversial. Machine learning is an excellent alternative method for predicting difficult laryngoscopy. This study aimed to develop and validate practical predictive models for difficult laryngoscopy through machine learning.

METHODS

Variables for the prediction of difficult laryngoscopy included age, Mallampati grade, body mass index, sternomental distance, and neck circumference. Difficult laryngoscopy was defined as grade 3 and 4 by the Cormack-Lehane classification. Pre-anesthesia and anesthesia data of 616 patients who had undergone anesthesia at a single center were included. The dataset was divided into a base training set (n = 492) and a base test set (n = 124), with equal distribution of difficult laryngoscopy. Training data sets were trained with six algorithms (multilayer perceptron, logistic regression, supportive vector machine, random forest, extreme gradient boosting, and light gradient boosting machine), and cross-validated. The model with the highest area under the receiver operating characteristic curve (AUROC) was chosen as the final model, which was validated with the test set.

RESULTS

The results of cross-validation were best using the light gradient boosting machine algorithm with Mallampati score x age and sternomental distance as predictive model parameters. The predicted AUROC for the difficult laryngoscopy class was 0.71 (95% confidence interval, 0.59-0.83; p =  0.014), and the recall (sensitivity) was 0.85.

CONCLUSION

Predicting difficult laryngoscopy is possible with three parameters. Severe damage resulting from failure to predict difficult laryngoscopy with high recall is small with the reported model. The model's performance can be further enhanced by additional data training.

Multivariable prediction models for difficult direct laryngoscopy: Systematic review and literature metasynthesis

CONTEXT

The difficult airway is an important scenario in anaesthesia due to the impact of its potential complications, and the difficulty in predicting its presence in current clinical practice.

METHODS

Systematic review of articles in English and Spanish retrieved from MEDLINE (Ovid), LILACS and Embase up to March 2018. The search strategy was defined by the authors. The reviewers uploaded the studies to specially designed tables in order to qualitatively analyse the results of each paper.

RESULTS

A total of 3602 studies were identified. Thirty-four of these were included in the qualitative review. The most commonly used definition of difficulty was the Cormack-Lehane3 or 4 classification, with a weighted mean incidence of 7.23%. The most relevant finding was the methodological weaknesses in obtaining these scales.

CONCLUSIONS

Available prediction models show limited discrimination, and weaknesses were detected in the methodology used to develop these prediction rules.

Development and validation of a difficult laryngoscopy prediction model using machine learning of neck circumference and thyromental height

Background

Predicting difficult airway is challengeable in patients with limited airway evaluation. The aim of this study is to develop and validate a model that predicts difficult laryngoscopy by machine learning of neck circumference and thyromental height as predictors that can be used even for patients with limited airway evaluation.

Methods

Variables for prediction of difficulty laryngoscopy included age, sex, height, weight, body mass index, neck circumference, and thyromental distance. Difficult laryngoscopy was defined as Grade 3 and 4 by the Cormack-Lehane classification. The preanesthesia and anesthesia data of 1677 patients who had undergone general anesthesia at a single center were collected. The data set was randomly stratified into a training set (80%) and a test set (20%), with equal distribution of difficulty laryngoscopy. The training data sets were trained with five algorithms (logistic regression, multilayer perceptron, random forest, extreme gradient boosting, and light gradient boosting machine). The prediction models were validated through a test set.

Results

The model’s performance using random forest was best (area under receiver operating characteristic curve = 0.79 [95% confidence interval: 0.72–0.86], area under precision-recall curve = 0.32 [95% confidence interval: 0.27–0.37]).

Conclusions

Machine learning can predict difficult laryngoscopy through a combination of several predictors including neck circumference and thyromental height. The performance of the model can be improved with more data, a new variable and combination of models.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12871-021-01343-4.

Artificial Intelligence in Anesthesiology: Current Techniques, Clinical Applications, and Limitations

Artificial intelligence has been advancing in fields including anesthesiology. This scoping review of the intersection of artificial intelligence and anesthesia research identified and summarized six themes of applications of artificial intelligence in anesthesiology: (1) depth of anesthesia monitoring, (2) control of anesthesia, (3) event and risk prediction, (4) ultrasound guidance, (5) pain management, and (6) operating room logistics. Based on papers identified in the review, several topics within artificial intelligence were described and summarized: (1) machine learning (including supervised, unsupervised, and reinforcement learning), (2) techniques in artificial intelligence (e.g., classical machine learning, neural networks and deep learning, Bayesian methods), and (3) major applied fields in artificial intelligence.The implications of artificial intelligence for the practicing anesthesiologist are discussed as are its limitations and the role of clinicians in further developing artificial intelligence for use in clinical care. Artificial intelligence has the potential to impact the practice of anesthesiology in aspects ranging from perioperative support to critical care delivery to outpatient pain management.

Artificial Intelligence: A New Tool in Operating Room Management. Role of Machine Learning Models in Operating Room Optimization

We conducted a systematic review of literature to better understand the role of new technologies in the perioperative period; in particular we focus on the administrative and managerial Operating Room (OR) perspective. Studies conducted on adult (≥ 18 years) patients between 2015 and February 2019 were deemed eligible. A total of 19 papers were included. Our review suggests that the use of Machine Learning (ML) in the field of OR organization has many potentials. Predictions of the surgical case duration were obtain with a good performance; their use could therefore allow a more precise scheduling, limiting waste of resources. ML is able to support even more complex models, which can coordinate multiple spaces simultaneously, as in the case of the post-anesthesia care unit and operating rooms. Types of Artificial Intelligence could also be used to limit another organizational problem, which has important economic repercussions: cancellation. Random Forest has proven effective in identifing surgeries with high risks of cancellation, allowing to plan preventive measures to reduce the cancellation rate accordingly. In conclusion, although data in literature are still limited, we believe that ML has great potential in the field of OR organization; however, further studies are needed to assess the effective role of these new technologies in the perioperative medicine.