Precision Anesthesia in 2050.

Over the last few decades, the field of anesthesia has advanced far beyond its humble beginnings. Today's anesthetics are better and safer than ever, thanks to innovations in drugs, monitors, equipment, and patient safety.1-4 At the same time, we remain limited by our herd approach to medicine. Each of our patients is unique, but health care today is based on a one-size-fits-all approach, while our patients grow older and more medically complex every year. By 2050, we believe that precision medicine will play a central role across all medical specialties, including anesthesia. In addition, we expect that health care and consumer technology will continually evolve to improve and simplify the interactions between patients, providers, and the health care system. As demonstrated by 2 hypothetical patient experiences, these advancements will enable more efficient and safe care, earlier and more accurate diagnoses, and truly personalized treatment plans.

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects.

Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision-based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty. Early research for machine vision in anesthesia has focused on automated recognition of anatomical structures during ultrasound-guided regional anesthesia or line insertion; recognition of the glottic opening and vocal cords during video laryngoscopy; prediction of the difficult airway using facial images; and clinical alerts for endobronchial intubation detected on chest radiograph. Current machine vision applications measuring the distance between endotracheal tube tip and carina have demonstrated noninferior performance compared to board-certified physicians. The performance and potential uses of machine vision for anesthesia will only grow with the advancement of underlying machine vision algorithm technical performance developed outside of medicine, such as convolutional neural networks and transfer learning. This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.

Effects of Cumulative Head Impact Exposure in Adolescent Male Contact and Collision Sport Student Athletes.

OBJECTIVE: This prospective cohort study aimed to investigate the association between head impact exposure (HIE) and neuropsychological sequelae in high school football and ice hockey players over 1 year. SETTING: Community sample. PARTICIPANTS: A cohort of 52 adolescent American football and ice hockey players were enrolled in the study, with a final study sample of 35 included in analyses. DESIGN: The study followed a prospective cohort design, with participants undergoing neuropsychological screening and accelerometer-based measurement of HIE over 1 season. MAIN MEASURES: Changes in cognition, emotions, behavior, and reported symptoms were assessed using standardized neuropsychological tests and self-reported questionnaires. RESULTS: Cumulative HIE was not consistently associated with changes in cognition, emotions, behavior, or reported symptoms. However, it was linked to an isolated measure of processing speed, showing inconsistent results based on the type of HIE. History of previous concussion was associated with worsened verbal memory recognition (ImPACT Verbal Memory) but not on a more robust measure of verbal memory (California Verbal Learning Test [CVLT]). Reported attention-deficit/hyperactivity disorder history predicted improved neurocognitive change scores. No associations were found between reported history of anxiety/depression or headaches/migraines and neuropsychological change scores. CONCLUSION: Overall, our findings do not support the hypothesis that greater HIE is associated with an increase in neuropsychological sequelae over time in adolescent football and ice hockey players. The results align with the existing literature, indicating that HIE over 1 season of youth sports is not consistently associated with significant neuropsychological changes. However, the study is limited by a small sample size, attrition over time, and the absence of performance validity testing for neurocognitive measures. Future studies with larger and more diverse samples, longer follow-up, and integration of advanced imaging and biomarkers are needed to comprehensively understand the relationship between HIE and neurobehavioral outcomes. Findings can inform guidelines for safe youth participation in contact sports while promoting the associated health and psychosocial benefits.

A machine-learning approach for decision support and risk stratification of pediatric perioperative patients based on the APRICOT dataset.

BACKGROUND: Pediatric anesthesia has evolved to a high level of patient safety, yet a small chance remains for serious perioperative complications, even in those traditionally considered at low risk. In practice, prediction of at-risk patients currently relies on the American Society of Anesthesiologists Physical Status (ASA-PS) score, despite reported inconsistencies with this method. AIMS: The goal of this study was to develop predictive models that can classify children as low risk for anesthesia at the time of surgical booking and after anesthetic assessment on the procedure day. METHODS: Our dataset was derived from APRICOT, a prospective observational cohort study conducted by 261 European institutions in 2014 and 2015. We included only the first procedure, ASA-PS classification I to III, and perioperative adverse events not classified as drug errors, reducing the total number of records to 30 325 with an adverse event rate of 4.43%. From this dataset, a stratified train:test split of 70:30 was used to develop predictive machine learning algorithms that could identify children in ASA-PS class I to III at low risk for severe perioperative critical events that included respiratory, cardiac, allergic, and neurological complications. RESULTS: Our selected models achieved accuracies of >0.9, areas under the receiver operating curve of 0.6-0.7, and negative predictive values >95%. Gradient boosting models were the best performing for both the booking phase and the day-of-surgery phase. CONCLUSIONS: This work demonstrates that prediction of patients at low risk of critical PAEs can be made on an individual, rather than population-based, level by using machine learning. Our approach yielded two models that accommodate wide clinical variability and, with further development, are potentially generalizable to many surgical centers.

Use of Polar Heliostats to Improve Levels of Natural Lighting inside Buildings with Little Access to Sunlight.

The growing need to increase environmental and energy sustainability in buildings (housing, offices, warehouses, etc.) requires the use of solar radiation as a renewable source of energy that can help to lower carbon footprint, making buildings more efficient and thereby contributing to a more sustainable planet, while enhancing the health and wellbeing of its occupants. One of the technologies deployed in the use of solar energy in buildings is heliostats. In this context, this paper presents an analysis of the performance of a heliostat illuminator to improve illumination in a classroom at the Campus of Rabanales of the University of Cordoba (Spain). A design of a system in charge of monitoring and measuring daylighting variables using Arduino hardware technology and free software is shown. This equipment develops the communications, programming and collection of lighting data. In parallel, installation of an artificial lighting system complementary to the natural lighting system is implemented. Finally, an analysis of the impact of the proposed solution on the improvement of energy efficiency is presented. Specifically, it is estimated that up to 64% of savings in artificial lighting can be achieved in spaces with heliostatic illuminators compared to those without them.

Machine Learning Applied to Registry Data: Development of a Patient-Specific Prediction Model for Blood Transfusion Requirements During Craniofacial Surgery Using the Pediatric Craniofacial Perioperative Registry Dataset.

BACKGROUND: Craniosynostosis is the premature fusion of ≥1 cranial sutures and often requires surgical intervention. Surgery may involve extensive osteotomies, which can lead to substantial blood loss. Currently, there are no consensus recommendations for guiding blood conservation or transfusion in this patient population. The aim of this study is to develop a machine-learning model to predict blood product transfusion requirements for individual pediatric patients undergoing craniofacial surgery. METHODS: Using data from 2143 patients in the Pediatric Craniofacial Surgery Perioperative Registry, we assessed 6 machine-learning classification and regression models based on random forest, adaptive boosting (AdaBoost), neural network, gradient boosting machine (GBM), support vector machine, and elastic net methods with inputs from 22 demographic and preoperative features. We developed classification models to predict an individual's overall need for transfusion and regression models to predict the number of blood product units to be ordered preoperatively. The study is reported according to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) checklist for prediction model development. RESULTS: The GBM performed best in both domains, with an area under receiver operating characteristic curve of 0.87 ± 0.03 (95% confidence interval) and F-score of 0.91 ± 0.04 for classification, and a mean squared error of 1.15 ± 0.12, R-squared (R) of 0.73 ± 0.02, and root mean squared error of 1.05 ± 0.06 for regression. GBM feature ranking determined that the following variables held the most information for prediction: platelet count, weight, preoperative hematocrit, surgical volume per institution, age, and preoperative hemoglobin. We then produced a calculator to show the number of units of blood that should be ordered preoperatively for an individual patient. CONCLUSIONS: Anesthesiologists and surgeons can use this continually evolving predictive model to improve clinical care of patients presenting for craniosynostosis surgery.

Audit of blood product utilization in the care of injured children.

BACKGROUND: Blood product utilization in injured children is poorly characterized; the decision to prepare products or transfuse patients can be difficult due to a lack of reliable evidence of transfusion needs across pediatric age-groups and injury types. We conducted an audit of transfusion practices in pediatric trauma based on age, injuries, and mechanism of injury. METHODS: We reviewed and cross-referenced blood product transfusion practice data from the trauma registry and the anesthesia transfusion record database at a level 1 pediatric trauma center over a 10-year period. Demographic data, injury severity scores, and survival statistics were obtained from the trauma registry. Transfusion rates are reported separately for hospital admission and for intraoperative transfusions for procedures performed during the first two hospital days. Descriptive statistical analysis was used to compare specific groups based on age, injury type, and mechanism of injury. RESULTS: We report 14 569 trauma admissions of 14 606 patients. The transfusion rate during the admission was 1.56% (227/14 569). 4591 (30.9%) admissions had surgical interventions in first two days of hospitalization with an intraoperative transfusion rate of 2.98%. Patients younger than one year had the highest transfusion rate during admission (2.8%), and the highest transfusion rate during surgical procedures performed in the first two days of the admission (18.87%). Admissions due to vascular injuries had the highest transfusion rates in infancy followed by hollow visceral injuries in adolescents (71.4% and 25%, respectively). Vascular injuries in most age-groups also had high transfusion rates ranging from 11% in 5- to 9-year age-group to 71% in infants. Mechanisms with the highest transfusion rates were firearm wounds in patients older than one year and vehicular accidents for patients younger than one year. CONCLUSIONS: The overall blood product needs in the pediatric trauma population are low (1.56%). Selected populations requiring higher rates of need include infants younger than one year, and children with thoracic and vascular injuries. Understanding transfusion patterns is important to optimize resource allocation.

Viscosity Measurement Sensor: A Prototype for a Novel Medical Diagnostic Method Based on Quartz Crystal Resonator.

Viscosity variation in human fluids, such as Synovial Fluid (SF) or Cerebrospinal Fluid (CSF), can be used as a diagnostic factor; however, the sample volume obtained for analysis is usually small, making it difficult to measure its viscosity. On the other hand, Quartz Crystal Resonators (QCR) have been used widely in sensing applications due to their accuracy, cost, and size. This work provides the design and validation of a new viscosity measurement system based on quartz crystal resonators for low volume fluids, leading to the development of a sensor called "ViSQCT" as a prototype for a new medical diagnostic tool. The proposed method is based on measuring the resonance frequency at the crystal's maximum conductance point through a frequency sweep, where crystals with 10 MHz fundamental resonance frequency were used. For validation purposes, artificial fluids were developed to simulate SFs and CFs in healthy and pathological conditions as experiment phantoms. A commercial QCR based system was also used for validation since its methodology differs from ours. A conventional rotational viscometer was used as a reference for calibration purposes. ViSQCT demonstrates the capability to measure the sample's viscosity differentiation between healthy and pathological fluid phantoms and shows that it can be used as a basis for a diagnostic method of several pathologies related to the studied biological fluids. However, some performance differences between both QCR-based systems compared to the reference system deserves further investigation.

Artificial intelligence, machine learning and the pediatric airway.

Artificial intelligence and machine learning are rapidly expanding fields with increasing relevance in anesthesia and, in particular, airway management. The ability of artificial intelligence and machine learning algorithms to recognize patterns from large volumes of complex data makes them attractive for use in pediatric anesthesia airway management. The purpose of this review is to introduce artificial intelligence, machine learning, and deep learning to the pediatric anesthesiologist. Current evidence and developments in artificial intelligence, machine learning, and deep learning relevant to pediatric airway management are presented. We critically assess the current evidence on the use of artificial intelligence and machine learning in the assessment, diagnosis, monitoring, procedure assistance, and predicting outcomes during pediatric airway management. Further, we discuss the limitations of these technologies and offer areas for focused research that may bring pediatric airway management anesthesiology into the era of artificial intelligence and machine learning.

Monitoring of Temperature in Retail Refrigerated Cabinets Applying IoT Over Open-Source Hardware and Software.

The control of refrigeration in the food chain is fundamental at all stages, with special emphasis on the retail stage. The implementation of information and communication technologies (IoT, open-source hardware and software, cloud computing, etc.) is representing a revolution in the operational paradigm of food control. This paper presents a low-cost IoT solution, based on free hardware and software, for monitoring the temperature in refrigerated retail cabinets. Specifically, the use of the ESP-8266-Wi-Fi microcontroller with DS18B20 temperature sensors is proposed. The ThingSpeak IoT platform is used to store and process data in the cloud. The solution presented is robust, affordable, and flexible, allowing to extend the scope of supervising other relevant parameters in the operating process (light control, energy efficiency, consumer presence, etc.).

Hypoxemia, Bradycardia, and Multiple Laryngoscopy Attempts during Anesthetic Induction in Infants: A Single-center, Retrospective Study.

BACKGROUND: The infant airway is particularly vulnerable to trauma from repeated laryngoscopy attempts. Complications associated with elective tracheal intubations in anesthetized infants may be underappreciated. We conducted this study of anesthetized infants to determine the incidence of multiple laryngoscopy attempts during routine tracheal intubation and assess the association of laryngoscopy attempts with hypoxemia and bradycardia. METHODS: We conducted a retrospective cross-sectional cohort study of anesthetized infants (age less than or equal to 12 months) who underwent direct laryngoscopy for oral endotracheal intubation between January 24, 2015, and August 1, 2016. We excluded patients with a history of difficult intubation and emergency procedures. Our primary outcome was the incidence of hypoxemia or bradycardia during induction of anesthesia. We evaluated the relationship between laryngoscopy attempts and our primary outcome, adjusting for age, weight, American Society of Anesthesiologists status, staffing model, and encounter location. RESULTS: A total of 1,341 patients met our inclusion criteria, and 16% (n = 208) had multiple laryngoscopy attempts. The incidence of hypoxemia was 35% (n = 469) and bradycardia was 8.9% (n = 119). Hypoxemia and bradycardia occurred in 3.7% (n = 50) of patients. Multiple laryngoscopy attempts were associated with an increased risk of hypoxemia (adjusted odds ratio: 1.78, 95% CI: 1.30 to 2.43, P < 0.001). There was no association between multiple laryngoscopy attempts and bradycardia (adjusted odds ratio: 1.23, 95% CI: 0.74 to 2.03, P = 0.255). CONCLUSIONS: In a quaternary academic center, healthy infants undergoing routine tracheal intubations had a high incidence of multiple laryngoscopy attempts and associated hypoxemia episodes.

STBUR: Sleep trouble breathing and unrefreshed questionnaire: Evaluation of screening tool for postanesthesia care and disposition.

BACKGROUND: The Snoring, Trouble Breathing, and Un-Refreshed (STBUR) questionnaire is a five-question screening tool for pediatric sleep-disordered breathing and risk for perioperative respiratory adverse events in children. The utility of this questionnaire as a preoperative risk-stratification tool has not been investigated. In view of limited availability of screening tools for preoperative pediatric sleep-disordered breathing, we evaluated the questionnaire's performance for postanesthesia adverse events that can impact postanesthesia care and disposition. METHODS: The retrospective study protocol was approved by the institutional research board. The data were analyzed using two different definitions for a positive screening based on a five-point scale: low threshold (scores 1 to 5) and high threshold (score of 5). The primary outcome was based on the following criteria: (a) supplemental oxygen therapy following postanesthesia care unit (PACU) stay until hospital discharge, (b) greater than two hours during phase 1 recovery, (c) anesthesia emergency activation in the PACU, and (d) unplanned hospital admission. RESULTS: About 6025 patients completed the questionnaire during the preoperative evaluation. And 1522 patients had a low threshold score and 270 had a high-threshold score. We found statistically significant associations in three outcomes based on the low threshold score: supplemental oxygen therapy (negative-predictive value [NPV] 0.97, 95% CI 0.97-98), PACU recovery time (NPV 0.99, 95% CI 0.99-0.99) and escalation of care (NPV 0.98, 95% CI 0.97-0.98). Positive-predictive values were statistically significant for all outcomes except anesthesia emergency in the PACU. CONCLUSION: The Snoring, Trouble Breathing, and Un-Refreshed questionnaire identified patients at higher risk for prolonged phase 1 recovery, oxygen therapy requirement, and escalation of care. The questionnaire's high-negative predictive value and specificity may make it useful as a screening tool to identify patients at low risk for prolonged stay in PACU.

Early identification of impending cardiac arrest in neonates and infants in the cardiovascular ICU: a statistical modelling approach using physiologic monitoring data.

OBJECTIVE: To develop a physiological data-driven model for early identification of impending cardiac arrest in neonates and infants with cardiac disease hospitalised in the cardiovascular ICU. METHODS: We performed a single-institution retrospective cohort study (11 January 2013-16 September 2015) of patients ≤1 year old with cardiac disease who were hospitalised in the cardiovascular ICU at a tertiary care children's hospital. Demographics and diagnostic codes of cardiac arrest were obtained via the electronic health record. Diagnosis of cardiac arrest was validated by expert clinician review. Minute-to-minute physiological monitoring data were recorded via bedside monitors. A generalized linear model was used to compute a minute by minute risk score. Training and test data sets both included data from patients who did and did not develop cardiac arrest. An optimal risk-score threshold was derived based on the model's discriminatory capacity for impending arrest versus non-arrest. Model performance measures included sensitivity, specificity, accuracy, likelihood ratios, and post-test probability of arrest. RESULTS: The final model consisting of multiple clinical parameters was able to identify impending cardiac arrest at least 2 hours prior to the event with an overall accuracy of 75% (sensitivity = 61%, specificity = 80%) and observed an increase in probability of detection of cardiac arrest from a pre-test probability of 9.6% to a post-test probability of 21.2%. CONCLUSIONS: Our findings demonstrate that a predictive model using physiologic monitoring data in neonates and infants with cardiac disease hospitalised in the paediatric cardiovascular ICU can identify impending cardiac arrest on average 17 hours prior to arrest.

Proposal for the Design of Monitoring and Operating Irrigation Networks Based on IoT, Cloud Computing and Free Hardware Technologies.

In recent decades, considerable efforts have been devoted to process automation in agriculture. Regarding irrigation systems, this demand has found several difficulties, including the lack of communication networks and the large distances to electricity supply points. With the recent implementation of LPWAN wireless communication networks (SIGFOX, LoraWan, and NBIoT), and the expanding market of electronic controllers based on free software and hardware (i.e., Arduino, Raspberry, ESP, etc.) with low energy requirements, new perspectives have appeared for the automation of agricultural irrigation networks. This paper presents a low-cost solution for automatic cloud-based irrigation. In this paper, it is proposed the design of a node network based on microcontroller ESP32-Lora and Internet connection through SIGFOX network. The results obtained show the stability and robustness of the designed system.

[Healthcare of transgenders by non-specialists in Chile]. / Atención de salud de personas transgéneros para médicos no especialistas en Chile.

The health care demand for transgenders has increased in Chile and worldwide. However, in Chile health care professionals are not trained to understand and face this problem. We herein review issues that should be considered in the training of non-specialist physicians to provide health care to transgenders, issues about terminology of body reassignment treatments and gender identity and the way Chilean professionals should deal with transgender persons.

The use of natural language processing on pediatric diagnostic radiology reports in the electronic health record to identify deep venous thrombosis in children.

Venous thromboembolism (VTE) is a potentially life-threatening condition that includes both deep vein thrombosis (DVT) and pulmonary embolism. We sought to improve detection and reporting of children with a new diagnosis of VTE by applying natural language processing (NLP) tools to radiologists' reports. We validated an NLP tool, Reveal NLP (Health Fidelity Inc, San Mateo, CA) and inference rules engine's performance in identifying reports with deep venous thrombosis using a curated set of ultrasound reports. We then configured the NLP tool to scan all available radiology reports on a daily basis for studies that met criteria for VTE between July 1, 2015, and March 31, 2016. The NLP tool and inference rules engine correctly identified 140 out of 144 reports with positive DVT findings and 98 out of 106 negative reports in the validation set. The tool's sensitivity was 97.2% (95% CI 93-99.2%), specificity was 92.5% (95% CI 85.7-96.7%). Subsequently, the NLP tool and inference rules engine processed 6373 radiology reports from 3371 hospital encounters. The NLP tool and inference rules engine identified 178 positive reports and 3193 negative reports with a sensitivity of 82.9% (95% CI 74.8-89.2) and specificity of 97.5% (95% CI 96.9-98). The system functions well as a safety net to screen patients for HA-VTE on a daily basis and offers value as an automated, redundant system. To our knowledge, this is the first pediatric study to apply NLP technology in a prospective manner for HA-VTE identification.

Interactive pediatric emergency checklists to the palm of your hand - How the Pedi Crisis App traveled around the world.

BACKGROUND: Cognitive aids help clinicians manage critical events and have been shown to improve outcomes by providing critical information at the point of care. Critical event guidelines, such as the Society of Pediatric Anesthesia's Critical Events Checklists described in this article, can be distributed globally via interactive smartphone apps. From October 1, 2013 to January 1, 2014, we performed an observational study to determine the global distribution and utilization patterns of the Pedi Crisis cognitive aid app that the Society for Pediatric Anesthesia developed. We analyzed distribution and utilization metrics of individuals using Pedi Crisis on iOS (Apple Inc., Cupertino, CA) devices worldwide. We used Google Analytics software (Google Inc., Mountain View, CA) to monitor users' app activity (eg, screen views, user sessions). METHODS: The primary outcome measurement was the number of user-sessions and geographic locations of Pedi Crisis user sessions. Each user was defined by the use of a unique Apple ID on an iOS device. RESULTS: Google Analytics correlates session activity with geographic location based on local Internet service provider logs. Pedi Crisis had 1 252 active users (both new and returning) and 4 140 sessions across 108 countries during the 3-month study period. Returning users used the app longer and viewed significantly more screens that new users (mean screen views: new users 1.3 [standard deviation +/-1.09, 95% confidence interval 1.22-1.55]; returning users 7.6 [standard deviation +/-4.19, 95% confidence interval 6.73-8.39]P<.01) CONCLUSIONS: Pedi Crisis was used worldwide within days of its release and sustained utilization beyond initial publication. The proliferation of handheld electronic devices provides a unique opportunity for professional societies to improve the worldwide dissemination of guidelines and evidence-based cognitive aids.

The timing and prevalence of intraoperative hypotension in infants undergoing laparoscopic pyloromyotomy at a tertiary pediatric hospital.

BACKGROUND: Intraoperative hypotension may be associated with adverse outcomes in children undergoing surgery. Infants and neonates under 6 months of age have less autoregulatory cerebral reserve than older infants, yet little information exists regarding when and how often intraoperative hypotension occurs in infants. AIMS: To better understand the epidemiology of intraoperative hypotension in infants, we aimed to determine the prevalence of intraoperative hypotension in a generally uniform population of infants undergoing laparoscopic pyloromyotomy. METHODS: Vital sign data from electronic records of infants who underwent laparoscopic pyloromyotomy with general anesthesia at a children's hospital between January 1, 1998 and October 4, 2013 were analyzed. Baseline blood pressure (BP) values and intraoperative BPs were identified during eight perioperative stages based on anesthesia event timestamps. We determined the occurrence of relative (systolic BP <20% below baseline) and absolute (mean arterial BP <35 mmHg) intraoperative hypotension within each stage. RESULTS: A total of 735 full-term infants and 82 preterm infants met the study criteria. Relative intraoperative hypotension occurred in 77%, 72%, and 58% of infants in the 1-30, 31-60, and 61-90 days age groups, respectively. Absolute intraoperative hypotension was seen in 21%, 12%, and 4% of infants in the 1-30, 31-60, and 61-90 days age groups, respectively. Intraoperative hypotension occurred primarily during surgical prep and throughout the surgical procedure. Preterm infants had higher rates of absolute intraoperative hypotension than full-term infants. CONCLUSIONS: Relative intraoperative hypotension was routine and absolute intraoperative hypotension was common in neonates and infants under 91 days of age. Preterm infants and infants under 61 days of age experienced the highest rates of absolute and relative intraoperative hypotension, particularly during surgical prep and throughout surgery.

Neural Network Classifier for Automatic Detection of Invasive Versus Noninvasive Airway Management Technique Based on Respiratory Monitoring Parameters in a Pediatric Anesthesia.

Children undergoing general anesthesia require airway monitoring by an anesthesia provider. The airway may be supported with noninvasive devices such as face mask or invasive devices such as a laryngeal mask airway or an endotracheal tube. The physiologic data stored provides an opportunity to apply machine learning algorithms distinguish between these modes based on pattern recognition. We retrieved three data sets from patients receiving general anesthesia in 2015 with either mask, laryngeal mask airway or endotracheal tube. Patients underwent myringotomy, tonsillectomy, adenoidectomy or inguinal hernia repair procedures. We retrieved measurements for end-tidal carbon dioxide, tidal volume, and peak inspiratory pressure and calculated statistical features for each data element per patient. We applied machine learning algorithms (decision tree, support vector machine, and neural network) to classify patients into noninvasive or invasive airway device support. We identified 300 patients per group (mask, laryngeal mask airway, and endotracheal tube) for a total of 900 patients. The neural network classifier performed better than the boosted trees and support vector machine classifiers based on the test data sets. The sensitivity, specificity, and accuracy for neural network classification are 97.5%, 96.3%, and 95.8%. In contrast, the sensitivity, specificity, and accuracy of support vector machine are 89.1%, 92.3%, and 88.3% and with the boosted tree classifier they are 93.8%, 92.1%, and 91.4%. We describe a method to automatically distinguish between noninvasive and invasive airway device support in a pediatric surgical setting based on respiratory monitoring parameters. The results show that the neural network classifier algorithm can accurately classify noninvasive and invasive airway device support.