Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models

Tavolara, Thomas E.; Gürcan, Metin N.; Segal, Scott; Niazi, M. Khalid Khan

doi:10.1016/j.compbiomed.2021.104737

Cited by 34 publications

(25 citation statements)

References 20 publications

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“…The use of AI could be useful also in this area. Tavolara et al ( 13 ), starting from frontal facial images, developed a DL model capable of identifying difficult to intubate patients, with performances superior to two conventional tests, Mallampati test and thyromental distance. In addition, the model can work at high sensitivity and low specificity (0.9079 and 0.4474) or low sensitivity and high specificity (0.3684 and 0.9605), exceeding the limits of low sensitivity of current tests.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence and anesthesia: a narrative review

Carnà¹,

Russo²,

Vincenzo³

et al. 2022

Ann Transl Med

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Background and Objective The aim of this narrative review is to analyze whether or not artificial intelligence (AI) and its subsets are implemented in current clinical anesthetic practice, and to describe the current state of the research in the field. AI is a general term which refers to all the techniques that enable computers to mimic human intelligence. AI is based on algorithms that gives machines the ability to reason and perform functions such as problem-solving, object and word recognition, inference of world states, and decision-making. It includes machine learning (ML) and deep learning (DL). Methods We performed a narrative review of the literature on Scopus, PubMed and Cochrane databases. The research string comprised various combinations of “artificial intelligence”, “machine learning”, “anesthesia”, “anesthesiology”. The databases were searched independently by two authors. A third reviewer would mediate any disagreement the results of the two screeners. Key Content and Findings The application of AI has shown excellent results in both anesthesia and in operating room (OR) management. In each phase of the perioperative process, pre-, intra- and postoperative ones, it is able to perform different and specific tasks, using various techniques. Conclusions Thanks to the use of these new technologies, even anesthesia, as it is happening for other disciplines, is going through a real revolution, called Anesthesia 4.0. However, AI is not free from limitations and open issues. Unfortunately, the models created, provided they have excellent performance, have not yet entered daily practice. Clinical impact analyzes and external validations are needed before this happens. Therefore, qualitative research will be needed to better understand the ethical, cultural, and societal implications of integrating AI into clinical workflows.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence and anesthesia: a narrative review

Carnà¹,

Russo²,

Vincenzo³

et al. 2022

Ann Transl Med

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“…For example, studies have shown that modern machine learning methods can be used to predict difficult airways in the E.R. ( 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 ).…”

Section: Discussionmentioning

confidence: 99%

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

Zhou

Wang

Xue

et al. 2022

Front. Public Health

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BackgroundIn this paper, we examine whether machine learning and deep learning can be used to predict difficult airway intubation in patients undergoing thyroid surgery.MethodsWe used 10 machine learning and deep learning algorithms to establish a corresponding model through a training group, and then verify the results in a test group. We used R for the statistical analysis and constructed the machine learning prediction model in Python.ResultsThe top 5 weighting factors for difficult airways identified by the average algorithm in machine learning were age, sex, weight, height, and BMI. In the training group, the AUC values and accuracy and the Gradient Boosting precision were 0.932, 0.929, and 100%, respectively. As for the modeled effects of predicting difficult airways in test groups, among the models constructed by the 10 algorithms, the three algorithms with the highest AUC values were Gradient Boosting, CNN, and LGBM, with values of 0.848, 0.836, and 0.812, respectively; In addition, among the algorithms, Gradient Boosting had the highest accuracy with a value of 0.913; Additionally, among the algorithms, the Gradient Boosting algorithm had the highest precision with a value of 100%.ConclusionAccording to our results, Gradient Boosting performed best overall, with an AUC >0.8, an accuracy >90%, and a precision of 100%. Besides, the top 5 weighting factors identified by the average algorithm in machine learning for difficult airways were age, sex, weight, height, and BMI.

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“…In conclusion, we believe that our novel model provides an efficient and reliable method to screen MHCs from a large number of protein sequences. In the future, we will pay more attention to deep learning classifiers and evolution strategies ( Tahoces et al, 2021 ; Tandel et al, 2021 ; Tavolara et al, 2021 ; Togacar, 2021 ; Tsiknakis et al, 2021 ; Turki and Taguchi, 2021 ; Usman et al, 2021 ; Vafaeezadeh et al, 2021 ; Wang et al, 2021 ; Watanabe et al, 2021 ; Yap et al, 2021 ; Yildirim et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

Chen

2022

Front. Genet.

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The major histocompatibility complex (MHC) is a large locus on vertebrate DNA that contains a tightly linked set of polymorphic genes encoding cell surface proteins essential for the adaptive immune system. The groups of proteins encoded in the MHC play an important role in the adaptive immune system. Therefore, the accurate identification of the MHC is necessary to understand its role in the adaptive immune system. An effective predictor called PredMHC is established in this study to identify the MHC from protein sequences. Firstly, PredMHC encoded a protein sequence with mixed features including 188D, APAAC, KSCTriad, CKSAAGP, and PAAC. Secondly, three classifiers including SGD, SMO, and random forest were trained on the mixed features of the protein sequence. Finally, the prediction result was obtained by the voting of the three classifiers. The experimental results of the 10-fold cross-validation test in the training dataset showed that PredMHC can obtain 91.69% accuracy. Experimental results on comparison with other features, classifiers, and existing methods showed the effectiveness of PredMHC in predicting the MHC.

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Identification of difficult to intubate patients from frontal face images using an ensemble of deep learning models

Cited by 34 publications

References 20 publications

Artificial intelligence and anesthesia: a narrative review

Artificial intelligence and anesthesia: a narrative review

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

PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features

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