Artificial intelligence, machine learning and the pediatric airway

Emergency front-of-neck access in pediatric anesthesia: A narrative review

BACKGROUND AND OBJECTIVES

Children undergoing airway management during general anesthesia may experience airway complications resulting in a rare but life-threatening situation known as "Can't Intubate, Can't Oxygenate". This situation requires immediate recognition, advanced airway management, and ultimately emergency front-of-neck access. The absence of standardized procedures, lack of readily available equipment, inadequate knowledge, and training often lead to failed emergency front-of-neck access, resulting in catastrophic outcomes. In this narrative review, we examined the latest evidence on emergency front-of-neck access in children.

METHODS

A comprehensive literature was performed the use of emergency front-of-neck access (eFONA) in infants and children.

RESULTS

Eighty-six papers were deemed relevant by abstract. Finally, eight studies regarding the eFONA technique and simulations in animal models were included. For all articles, their primary and secondary outcomes, their specific animal model, the experimental design, the target participants, and the equipment were reported.

CONCLUSION

Based on the available evidence, we propose a general approach to the eFONA technique and a guide for implementing local protocols and training. Additionally, we introduce the application of innovative tools such as 3D models, ultrasound, and artificial intelligence, which can improve the precision, safety, and training of this rare but critical procedure.

Artificial intelligence in the NICU to predict extubation success in prematurely born infants

OBJECTIVES

Mechanical ventilation in prematurely born infants, particularly if prolonged, can cause long term complications including bronchopulmonary dysplasia. Timely extubation then is essential, yet predicting its success remains challenging. Artificial intelligence (AI) may provide a potential solution.

CONTENT

A narrative review was undertaken to explore AI's role in predicting extubation success in prematurely born infants. Across the 11 studies analysed, the range of reported area under the receiver operator characteristic curve (AUC) for the selected prediction models was between 0.7 and 0.87. Only two studies implemented an external validation procedure. Comparison to the results of clinical predictors was made in two studies. One group reported a logistic regression model that outperformed clinical predictors on decision tree analysis, while another group reported clinical predictors outperformed their artificial neural network model (AUCs: ANN 0.68 vs. clinical predictors 0.86). Amongst the studies there was an heterogenous selection of variables for inclusion in prediction models, as well as variations in definitions of extubation failure.

SUMMARY

Although there is potential for AI to enhance extubation success, no model's performance has yet surpassed that of clinical predictors.

OUTLOOK

Future studies should incorporate external validation to increase the applicability of the models to clinical settings.

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.

Health Care Professionals' and Parents' Perspectives on the Use of AI for Pain Monitoring in the Neonatal Intensive Care Unit: Multisite Qualitative Study

BACKGROUND

The use of artificial intelligence (AI) for pain assessment has the potential to address historical challenges in infant pain assessment. There is a dearth of information on the perceived benefits and barriers to the implementation of AI for neonatal pain monitoring in the neonatal intensive care unit (NICU) from the perspective of health care professionals (HCPs) and parents. This qualitative analysis provides novel data obtained from 2 large tertiary care hospitals in Canada and the United Kingdom.

OBJECTIVE

The aim of the study is to explore the perspectives of HCPs and parents regarding the use of AI for pain assessment in the NICU.

METHODS

In total, 20 HCPs and 20 parents of preterm infants were recruited and consented to participate from February 2020 to October 2022 in interviews asking about AI use for pain assessment in the NICU, potential benefits of the technology, and potential barriers to use.

RESULTS

The 40 participants included 20 HCPs (17 women and 3 men) with an average of 19.4 (SD 10.69) years of experience in the NICU and 20 parents (mean age 34.4, SD 5.42 years) of preterm infants who were on average 43 (SD 30.34) days old. Six themes from the perspective of HCPs were identified: regular use of technology in the NICU, concerns with regard to AI integration, the potential to improve patient care, requirements for implementation, AI as a tool for pain assessment, and ethical considerations. Seven parent themes included the potential for improved care, increased parental distress, support for parents regarding AI, the impact on parent engagement, the importance of human care, requirements for integration, and the desire for choice in its use. A consistent theme was the importance of AI as a tool to inform clinical decision-making and not replace it.

CONCLUSIONS

HCPs and parents expressed generally positive sentiments about the potential use of AI for pain assessment in the NICU, with HCPs highlighting important ethical considerations. This study identifies critical methodological and ethical perspectives from key stakeholders that should be noted by any team considering the creation and implementation of AI for pain monitoring in the NICU.

Development of a Machine Learning Model for Predicting Weaning Outcomes Based Solely on Continuous Ventilator Parameters during Spontaneous Breathing Trials

Discontinuing mechanical ventilation remains challenging. We developed a machine learning model to predict weaning outcomes using only continuous monitoring parameters obtained from ventilators during spontaneous breathing trials (SBTs). Patients who received mechanical ventilation in the medical intensive care unit at a tertiary university hospital from 2019-2021 were included in this study. During the SBTs, three waveforms and 25 numerical data were collected as input variables. The proposed convolutional neural network (CNN)-based weaning prediction model extracts features from input data with diverse lengths. Among 138 enrolled patients, 35 (25.4%) experienced weaning failure. The dataset was randomly divided into training and test sets (8:2 ratio). The area under the receiver operating characteristic curve for weaning success by the prediction model was 0.912 (95% confidence interval [CI], 0.795-1.000), with an area under the precision-recall curve of 0.767 (95% CI, 0.434-0.983). Furthermore, we used gradient-weighted class activation mapping technology to provide visual explanations of the model's prediction, highlighting influential features. This tool can assist medical staff by providing intuitive information regarding readiness for extubation without requiring any additional data collection other than SBT data. The proposed predictive model can assist clinicians in making ventilator weaning decisions in real time, thereby improving patient outcomes.

New Devices, Innovative Technologies, and Non-Standard Techniques for Airway Management: A Narrative Review

A wide range of airway devices and techniques have been created to enhance the safety of airway management. However, airway management remains a challenge. All techniques are susceptible to failure. Therefore, it is necessary to have and know the greatest number of alternatives to treat even the most challenging airway successfully. The aim of this narrative review is to describe some new devices, such as video laryngeal masks, articulated stylets, and non-standard techniques, for laryngeal mask insertion and endotracheal intubation that are not applied in daily practice, but that could be highly effective in overcoming a difficulty related to airway management. Artificial intelligence and 3D technology for airway management are also discussed.

The future of neonatal lung ultrasound: Validation of an artificial intelligence model for interpreting lung scans. A multicentre prospective diagnostic study

BACKGROUND

Artificial intelligence (AI) is a promising field in the neonatal field. We focused on lung ultrasound (LU), a useful tool for the neonatologist. Our aim was to train a neural network to create a model able to interpret LU.

METHODS

Our multicentric, prospective study included newborns with gestational age (GA) ≥ 33 + 0 weeks with early tachypnea/dyspnea/oxygen requirements. For each baby, three LU were performed: within 3 h of life (T0), at 4-6 h of life (T1), and in the absence of respiratory support (T2). Each scan was processed to extract the region of interest used to train a neural network to classify it according to the LU score (LUS). We assessed sensitivity, specificity, positive and negative predictive value of the AI model's scores in predicting the need for respiratory assistance with nasal continuous positive airway pressure and for surfactant, compared to an already studied and established LUS.

RESULTS

We enrolled 62 newborns (GA = 36 ± 2 weeks). In the prediction of the need for CPAP, we found a cutoff of 6 (at T0) and 5 (at T1) for both the neonatal lung ultrasound score (nLUS) and AI score (AUROC 0.88 for T0 AI model, 0.80 for T1 AI model). For the outcome "need for surfactant therapy", results in terms of area under receiver operator characteristic (AUROC) are 0.84 for T0 AI model and 0.89 for T1 AI model. In the prediction of surfactant therapy, we found a cutoff of 9 for both scores at T0, at T1 the nLUS cutoff was 6, while the AI's one was 5. Classification accuracy was good both at the image and class levels.

CONCLUSIONS

This is, to our knowledge, the first attempt to use an AI model to interpret early neonatal LUS and can be extremely useful for neonatologists in the clinical setting.

Emerging Robotic Innovations and Artificial Intelligence in Endotracheal Intubation and Airway Management: Current State of the Art

Robotic sciences have rapidly advanced and revolutionized various aspects of medicine, including the field of airway management. Robotic endotracheal intubation is an innovative method that utilizes robotic systems to aid in the accurate placement of an endotracheal tube within the trachea. This cutting-edge technique shows great promise in improving procedural precision and ensuring patient safety. In this comprehensive overview, we delve into the present status of robotic-assisted endotracheal intubation, examining its advantages, obstacles, and the potential implications it holds for the future. In addition, this review encompasses a comprehensive analysis of the existing literature and references on recent advances in robotic technology and artificial intelligence related to airway management.

Automated location of orofacial landmarks to characterize airway morphology in anaesthesia via deep convolutional neural networks

BACKGROUND

A reliable anticipation of a difficult airway may notably enhance safety during anaesthesia. In current practice, clinicians use bedside screenings by manual measurements of patients' morphology.

OBJECTIVE

To develop and evaluate algorithms for the automated extraction of orofacial landmarks, which characterize airway morphology.

METHODS

We defined 27 frontal + 13 lateral landmarks. We collected n=317 pairs of pre-surgery photos from patients undergoing general anaesthesia (140 females, 177 males). As ground truth reference for supervised learning, landmarks were independently annotated by two anaesthesiologists. We trained two ad-hoc deep convolutional neural network architectures based on InceptionResNetV2 (IRNet) and MobileNetV2 (MNet), to predict simultaneously: (a) whether each landmark is visible or not (occluded, out of frame), (b) its 2D-coordinates (x,y). We implemented successive stages of transfer learning, combined with data augmentation. We added custom top layers on top of these networks, whose weights were fully tuned for our application. Performance in landmark extraction was evaluated by 10-fold cross-validation (CV) and compared against 5 state-of-the-art deformable models.

RESULTS

With annotators' consensus as the 'gold standard', our IRNet-based network performed comparably to humans in the frontal view: median CV loss L=1.277·10^-3, inter-quartile range (IQR) [1.001, 1.660]; versus median 1.360, IQR [1.172, 1.651], and median 1.352, IQR [1.172, 1.619], for each annotator against consensus, respectively. MNet yielded slightly worse results: median 1.471, IQR [1.139, 1.982]. In the lateral view, both networks attained performances statistically poorer than humans: median CV loss L=2.141·10^-3, IQR [1.676, 2.915], and median 2.611, IQR [1.898, 3.535], respectively; versus median 1.507, IQR [1.188, 1.988], and median 1.442, IQR [1.147, 2.010] for both annotators. However, standardized effect sizes in CV loss were small: 0.0322 and 0.0235 (non-significant) for IRNet, 0.1431 and 0.1518 (p<0.05) for MNet; therefore quantitatively similar to humans. The best performing state-of-the-art model (a deformable regularized Supervised Descent Method, SDM) behaved comparably to our DCNNs in the frontal scenario, but notoriously worse in the lateral view.

CONCLUSIONS

We successfully trained two DCNN models for the recognition of 27 + 13 orofacial landmarks pertaining to the airway. Using transfer learning and data augmentation, they were able to generalize without overfitting, reaching expert-like performances in CV. Our IRNet-based methodology achieved a satisfactory identification and location of landmarks: particularly in the frontal view, at the level of anaesthesiologists. In the lateral view, its performance decayed, although with a non-significant effect size. Independent authors had also reported lower lateral performances; as certain landmarks may not be clear salient points, even for a trained human eye.

Predicting the risk of inappropriate depth of endotracheal intubation in pediatric patients using machine learning approaches

Endotracheal tube (ET) misplacement is common in pediatric patients, which can lead to the serious complication. It would be helpful if there is an easy-to-use tool to predict the optimal ET depth considering in each patient's characteristics. Therefore, we plan to develop a novel machine learning (ML) model to predict the appropriate ET depth in pediatric patients. This study retrospectively collected data from 1436 pediatric patients aged < 7 years who underwent chest x-ray examination in an intubated state. Patient data including age, sex, height weight, the internal diameter (ID) of the ET, and ET depth were collected from electronic medical records and chest x-ray. Among these, 1436 data were divided into training (70%, n = 1007) and testing (30%, n = 429) datasets. The training dataset was used to build the appropriate ET depth estimation model, while the test dataset was used to compare the model performance with the formula-based methods such as age-based method, height-based method and tube-ID method. The rate of inappropriate ET location was significantly lower in our ML model (17.9%) compared to formula-based methods (35.7%, 62.2%, and 46.6%). The relative risk [95% confidence interval, CI] of an inappropriate ET location compared to ML model in the age-based, height-based, and tube ID-based method were 1.99 [1.56-2.52], 3.47 [2.80-4.30], and 2.60 [2.07-3.26], respectively. In addition, compared to ML model, the relative risk of shallow intubation tended to be higher in the age-based method, whereas the risk of the deep or endobronchial intubation tended to be higher in the height-based and the tube ID-based method. The use of our ML model was able to predict optimal ET depth for pediatric patients only with basic patient information and reduce the risk of inappropriate ET placement. It will be helpful to clinicians unfamiliar with pediatric tracheal intubation to determine the appropriate ET depth.

Use of artificial intelligence in paediatric anaesthesia: a systematic review

Objectives

Although the development of artificial intelligence (AI) technologies in medicine has been significant, their application to paediatric anaesthesia is not well characterised. As the paediatric operating room is a data-rich environment that requires critical clinical decision-making, this systematic review aims to characterise the current use of AI in paediatric anaesthesia and to identify barriers to the successful integration of such technologies.

Methods

This review was registered with PROSPERO (CRD42022304610), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in five electronic databases (Embase, Medline, Central, Scopus, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for paediatric anaesthesia (<18 yr old) within the perioperative setting.

Results

From 3313 records identified in the initial search, 40 were included in this review. Identified applications of AI were described for patient risk factor prediction (24 studies; 60%), anaesthetic depth estimation (2; 5%), anaesthetic medication/technique decision guidance (2; 5%), intubation assistance (1; 2.5%), airway device selection (3; 7.5%), physiological variable monitoring (6; 15%), and operating room scheduling (2; 5%). Multiple domains of AI were discussed including machine learning, computer vision, fuzzy logic, and natural language processing.

Conclusion

There is an emerging literature regarding applications of AI for paediatric anaesthesia, and their clinical integration holds potential for ultimately improving patient outcomes. However, multiple barriers to their clinical integration remain including a lack of high-quality input data, lack of external validation/evaluation, and unclear generalisability to diverse settings.

Systematic review protocol

CRD42022304610 (PROSPERO).

Artificial Intelligence in Surgical Learning

(1) Background: Artificial Intelligence (AI) is transforming healthcare on all levels. While AI shows immense potential, the clinical implementation is lagging. We present a concise review of AI in surgical learning; (2) Methods: A non-systematic review of AI in surgical learning of the literature in English is provided; (3) Results: AI shows utility for all components of surgical competence within surgical learning. AI presents with great potential within robotic surgery specifically (4) Conclusions: Technology will evolve in ways currently unimaginable, presenting us with novel applications of AI and derivatives thereof. Surgeons must be open to new modes of learning to be able to implement all evidence-based applications of AI in the future. Systematic analyses of AI in surgical learning are needed.

New technologies in airway management: A review

The evolution of medical knowledge and technological growth have contributed to the development of different techniques and devices for airway management. These appear to play a role in optimizing the number of attempts and overall success, ultimately reducing the negative consequences of airway manipulation. In this literature review, we highlight the recent evidence regarding new technologies applied to airway management. Before intubation, every patient should have an individualized structured airway management plan. Technology can help with both airway evaluation and tracheal intubation. Point-of-care cervical ultrasound and artificial intelligence models with automated facial analysis have been used to predict difficult airways. Various devices can be used in airway management. This includes a robotic video endoscope that guides intubation based on real image recognition, a laryngeal mask with a non-inflatable cuff that tries to reduce local complications, video laryngeal masks that are able to confirm the correct position and facilitate intubation, Viescope™, a videolaryngoscope developed for combat medicine with a unique circular blade, a system that uses cervical transillumination for glottis identification in difficult airways and Vivasight SL™ tracheal tube, which has a high-resolution camera at its tip guaranteeing visual assurance of tube position as well as guiding bronchial blocker position. To conclude, we detailed the challenges in airway management outside the operating room as well as described suction-assisted laryngoscopy and airway decontamination technique for contaminated airways. Further research in the clinical setting is recommended to better support the use of these technologies.

Where Is the Artificial Intelligence Applied in Dentistry? Systematic Review and Literature Analysis

This literature research had two main objectives. The first objective was to quantify how frequently artificial intelligence (AI) was utilized in dental literature from 2011 until 2021. The second objective was to distinguish the focus of such publications; in particular, dental field and topic. The main inclusion criterium was an original article or review in English focused on dental utilization of AI. All other types of publications or non-dental or non-AI-focused were excluded. The information sources were Web of Science, PubMed, Scopus, and Google Scholar, queried on 19 April 2022. The search string was “artificial intelligence” AND (dental OR dentistry OR tooth OR teeth OR dentofacial OR maxillofacial OR orofacial OR orthodontics OR endodontics OR periodontics OR prosthodontics). Following the removal of duplicates, all remaining publications were returned by searches and were screened by three independent operators to minimize the risk of bias. The analysis of 2011–2021 publications identified 4413 records, from which 1497 were finally selected and calculated according to the year of publication. The results confirmed a historically unprecedented boom in AI dental publications, with an average increase of 21.6% per year over the last decade and a 34.9% increase per year over the last 5 years. In the achievement of the second objective, qualitative assessment of dental AI publications since 2021 identified 1717 records, with 497 papers finally selected. The results of this assessment indicated the relative proportions of focal topics, as follows: radiology 26.36%, orthodontics 18.31%, general scope 17.10%, restorative 12.09%, surgery 11.87% and education 5.63%. The review confirms that the current use of artificial intelligence in dentistry is concentrated mainly around the evaluation of digital diagnostic methods, especially radiology; however, its implementation is expected to gradually penetrate all parts of the profession.

Clinical Application of Artificial Intelligence: Auto-Discerning the Effectiveness of Lidocaine Concentration Levels in Osteosarcoma Femoral Tumor Segment Resection

Adolescents and children worldwide are threatened by osteosarcoma, a tumor that predominantly affects the long bone epiphysis. Osteosarcoma is the most common and highly malignant bone tumor in youngsters. Early tumor detection is the key to effective treatment of this disease. The discovery of biomarkers and the growing understanding of molecules and their complex interactions have improved the outcome of clinical trials in osteosarcoma. This article describes biomarkers of osteosarcoma with the aim of positively influencing the progress of clinical treatment of osteosarcoma. Femoral bone tumor is a typical condition of osteosarcoma. Due to the wide range of femoral stem types, complexities in the distal femur, and tumors in the rotor part of femur, physicians following the traditional clinical approach face difficulties in removing the lesion and fixing the femur with resection of the tumor segment. In this paper, the effect of small doses of different concentrations of lidocaine anesthesia in patients undergoing lumpectomy for osteosarcoma femoral tumor segments is investigated. A computer-based artificial intelligence method for automated determination of different concentration levels of lidocaine anesthesia and amputation of osteosarcoma femoral tumor segment is proposed. Statistical analysis is carried on the empirical data including intraoperative bleeding, intraoperative and postoperative pain scores, surgical operation time, postoperative complications, patient satisfaction, and local anesthetic dose. The results showed that the patients in the study group had low intraoperative bleeding, short operation time, low postoperative hematoma formation rate, high patient satisfaction, higher dosage of anesthetic solution, and low dosage of lidocaine. Results revealed that mean arterial pressure and heart rate in extubating and intubating were significantly lower in the observation group than in the control group, and a significant difference (P < 0.05) was observed between the two groups. This proves that the proposed algorithm can adequately reduce bleeding, alleviate postoperative pain, shorten operation time, reduce complications, accelerate recovery, and ensure better treatment results.

Future of machine learning in paediatrics

Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to learn without being explicitly programmed, through a combination of statistics and computer science. It encompasses a variety of techniques used to analyse and interpret extremely large amounts of data, which can then be applied to create predictive models. Such applications of this technology are now ubiquitous in our day-to-day lives: predictive text, spam filtering, and recommendation systems in social media, streaming video and e-commerce to name a few examples. It is only more recently that ML has started to be implemented against the vast amount of data generated in healthcare. The emerging role of AI in refining healthcare delivery was recently highlighted in the 'National Health Service Long Term Plan 2019'. In paediatrics, workforce challenges, rising healthcare attendance and increased patient complexity and comorbidity mean that demands on paediatric services are also growing. As healthcare moves into this digital age, this review considers the potential impact ML can have across all aspects of paediatric care from improving workforce efficiency and aiding clinical decision-making to precision medicine and drug development.

Shape Prediction of Nasal Bones by Digital 2D-Photogrammetry of the Nose Based on Convolution and Back-Propagation Neural Network

In rhinoplasty, it is necessary to consider the correlation between the anthropometric indicators of the nasal bone, so that it prevents surgical complications and enhances the patient's satisfaction. The penetrating form of high-energy electromagnetic radiation is highly impacted on human health, which has often raised concerns of alternative method for facial analysis. The critical stage to assess nasal morphology is the nasal analysis on its anthropology that is highly reliant on the understanding of the structural features of the nasal radix. For example, the shape and size of nasal bone features, skin thickness, and also body factors aggregated from different facial anthropology values. In medical diagnosis, however, the morphology of the nasal bone is determined manually and significantly relies on the clinician's expertise. Furthermore, the evaluation anthropological keypoint of the nasal bone is nonrepeatable and laborious, also finding widely differ and intralaboratory variability in the results because of facial soft tissue and equipment defects. In order to overcome these problems, we propose specialized convolutional neural network (CNN) architecture to accurately predict nasal measurement based on digital 2D photogrammetry. To boost performance and efficacy, it is deliberately constructed with many layers and different filter sizes, with less filters and optimizing parameters. Through its result, the back-propagation neural network (BPNN) indicated the correlation between differences in human body factors mentioned are height, weight known as body mass index (BMI), age, gender, and the nasal bone dimension of the participant. With full of parameters could the nasal morphology be diagnostic continuously. The model's performance is evaluated on various newest architecture models such as DenseNet, ConvNet, Inception, VGG, and MobileNet. Experiments were directly conducted on different facials. The results show the proposed architecture worked well in terms of nasal properties achieved which utilize four statistical criteria named mean average precision (mAP), mean absolute error (MAE), R-square (R²), and T-test analyzed. Data has also shown that the nasal shape of Southeast Asians, especially Vietnamese, could be divided into different types in two perspective views. From cadavers for bony datasets, nasal bones can be classified into 2 morphological types in the lateral view which “V” shape was presented by 78.8% and the remains were “S” shape evaluated based on Lazovic (2015). With 2 angular dimension averages are 136.41 ± 7.99 and 104.25 ± 5.95 represented by the nasofrontal angle (g-n-prn) and the nasomental angle (n-prn-sn), respectively. For frontal view, classified by Hwang, Tae-Sun, et al. (2005), nasal morphology of Vietnamese participants could be divided into three types: type A was present in 57.6% and type B was present in 30.3% of the noses. In particular, types C, D, and E were not a common form of Vietnamese which includes the remaining number of participants. In conclusion, the proposed model performed the potential hybrid of CNN and BPNN with its application to give expected accuracy in terms of keypoint localization and nasal morphology regression. Nasal analysis can replace MRI imaging diagnostics that are reflected by the risk to human body.

Pediatric airway management

PURPOSE OF REVIEW

Children are at risk of severe hypoxemia in the perioperative period owing to their unique anatomy and physiology. Safe and effective airway management strategies are therefore key to the practice of pediatric anesthesia. The goal of this review is to highlight recent publications (2019-2021) aimed to advance pediatric airway safety and to highlight a proposed simple, pediatric-specific, universal framework to guide clinical practice.

RECENT FINDINGS

Recent investigations demonstrate that infants with normal and difficult airways experience high incidences of multiple laryngoscopy attempts and resulting hypoxemia. Video laryngoscopy may improve tracheal intubation first attempt success rate in infants with normal airways. In infants with difficult airways, standard blade video laryngoscopy is associated with higher first attempt success rates over non-standard blade video laryngoscopy. Recent studies in children with Pierre Robin sequence and mucopolysaccharidoses help guide airway equipment and technique selection. Department airway leads and hospital difficult airway services are necessary to disseminate knowledge, lead quality improvement initiatives, and promote evidence-based practice guidelines.

SUMMARY

Pediatric airway management morbidity is a common problem in pediatric anesthesia. Improvements in individual practitioner preparation and management strategies as well as systems-based policies are required. A simple, pediatric-specific, universal airway management framework can be adopted for safe pediatric anesthesia practice.