The integration of artificial intelligence (AI) and machine learning (ML) is fundamentally transforming pediatric airway management by enhancing every stage of the process: from initial visualization and procedural guidance to post-intubation confirmation and continuous patient monitoring. These AI-driven tools aim to improve first-attempt success rates, reduce complications, and accelerate skill acquisition for healthcare professionals in high-stakes environments like the emergency department and intensive care unit.

The inherent challenges of pediatric airway intubation, characterized by smaller, more delicate, and rapidly changing anatomical structures, are being addressed through the application of computer vision and augmented reality (AR) technologies within video laryngoscopy (VL) systems [16]. (Table 1; Fig. 1).

Fig. 1

AI-Augmented Visualization and Monitoring Workflow

Automated Landmark Recognition and Visualization

AI-enhanced VL systems utilize advanced computer vision algorithms to provide real-time support during the procedure. The AI models are trained to reliably recognize and differentiate critical, often subtle, pediatric anatomical landmarks such as the omega-shaped epiglottis and the vocal cords. This capability is crucial for improving safety and procedural efficiency, especially in neonates and infants where anatomical structures are less distinct. Studies have demonstrated that machine learning models can accurately recognize the glottis and epiglottis from VL images, effectively enhancing the clinician’s understanding of the airway anatomy and supporting informed decision-making during difficult intubations [18].

Real-Time Procedural Guidance and Feedback

Augmented reality and sensor integration provide dynamic, real-time feedback to guide the intubation process and refine clinical technique. AR technology overlays real-time visual cues onto the VL image, offering guidance on the optimal endotracheal tube (ETT) trajectory, insertion depth, and angle. This support has been shown to reduce cognitive load, decrease the time required for successful intubation, and improve first-attempt success rates, particularly in patients with complex congenital anomalies like the Pierre Robin sequence [24]. AI-driven VL systems continuously monitor key procedural parameters, providing instant feedback to the clinician. Real-time monitoring of the laryngoscope blade angle ensures optimal glottic exposure and minimizes pressure on surrounding tissues, reducing the risk of anatomical trauma. Similarly, feedback on applied force helps clinicians adapt their technique to prevent complications such as dental injury or airway trauma. By tracking the duration of attempts, AI algorithms can analyze performance data and generate personalized recommendations for skill development, fostering continuous improvement in airway management techniques [25].

Impact on Clinical Outcomes and Training

The integration of VL, particularly with AI augmentation, has a demonstrable positive impact on clinical practice and professional training. VL significantly improves first-attempt intubation success rates [26] and is associated with reductions in complications, including hypoxemia and cardiac arrest, in critically ill patients. Furthermore, VL plays a pivotal role in accelerating the training of healthcare professionals by allowing non-anesthesiologists and trainees to practice repeatedly under varying conditions, leading to enhanced competency and higher intubation success rates in settings like the pediatric intensive care unit [32].

AI-Enhanced Confirmation and Continuous Monitoring

Beyond the initial intubation, AI is being leveraged to improve the accuracy of tube placement confirmation and to provide continuous, multi-modal monitoring for early detection of airway compromise. AI and deep learning (DL) models are being developed to automate the interpretation of standard confirmation techniques, which traditionally rely heavily on human expertise.

Capnography Waveforms

Waveform capnography is the gold standard for confirming tracheal intubation. However, interpreting subtle changes in waveform morphology that precede clinical deterioration can be challenging, especially in emergency pediatric settings. ML and DL models offer the potential for real-time automated interpretation by: Accurately segmentation and non-inhalation phases and automatically classifying waveforms (e.g., normal ventilation, probable esophageal placement, impending dislodgement, or partial obstruction) and issuing predictive alerts based on continuous tracking of waveform morphology changes, which may indicate tube migration or obstruction before overt oxygenation or end-tidal carbon dioxide EtCO₂ decline [33, 34].

Point-of-care Ultrasound (POCUS)

Point-of-care ultrasound (POCUS) is increasingly used for tracheal tube confirmation in children (for example, tracheal vs. esophageal intubation, depth of ETT, bilateral lung sliding). In the pediatric emergency airway, ultrasound has advantages of being radiation-free and rapid [35].

AI can extend this further by automating image/clip interpretation: for instance, an algorithm may identify the ETT in the trachea (vs. oesophagus), measure its depth relative to anatomical landmarks, assess bilateral lung sliding to confirm ventilation of both lungs, and flag if sliding is absent suggesting main-stem intubation or pneumothorax [36].

Smart Devices and Multi-Modal Data Fusion

The next generation of airway management involves smart devices and AI-driven data fusion to create a comprehensive, continuous monitoring ecosystem.

Intelligent ETT Systems with Embedded Sensors

In addition to machine learning algorithms for waveform and image interpretation, the development of next-generation endotracheal tubes (ETTs) and related devices with embedded sensors is advancing the field of continuous airway monitoring. These innovations include features such as micro-sensors that can detect tube displacement [37]. Cuff-pressure sensors are also integrated into some devices, providing continuous measurement and automated regulation of cuff inflation to prevent both over- and under-inflation, which is especially important for older pediatric patients using cuffed tubes [38]. These smart devices represent a significant step forward in ensuring the safety and stability of airway management, particularly in pediatric emergency settings. Such devices, when integrated into the monitoring ecosystem, allow earlier detection of tube dislodgement, inadvertent extubation, endobronchial intubation, or cuff leak, critical in emergency airway management.

Integration into Bedside Monitors: Multi-Modal Data Fusion & Predictive Alerts

AI in airway monitoring helps with data fusion: merging signals from capnography waveforms, ultrasound interpretation, ETT-sensor data, ventilator parameters (pressures, volumes), SpO₂/CO₂ trends, patient movement (via accelerometer), and clinical context (age, anatomy, tube size). An AI engine can process these heterogeneous data streams to issue predictive alerts: e.g., “ETT displacement imminent”, “bronchial intubation likely”, or “ventilation adequacy declining”.

In practice, a bedside monitor (or central monitoring station) might continuously compute a risk score (0–100) for airway failure/ETT malposition. Clinicians would receive a soft alert ahead of overt desaturation or ventilator alarm. Such predictive capability could significantly enhance emergency airway safety in pediatric settings.

Conceptual Framework: This figure highlights the real-time integration of AI as a predictive model, and during the visualization process.

Challenges and Future Directions

Despite the immense potential, the successful translation of AI-enhanced tools into routine pediatric emergency airway care faces several unique challenges.

Pediatric-Specific Constraints

The distinct characteristics of the pediatric population pose significant hurdles for AI development and implementation.

The rapidly changing and smaller airway anatomy in children, particularly infants and neonates, means that algorithms developed using adult data often fail to generalize effectively. Minor tube position shifts can have critical consequences, necessitating pediatric-specific training data for robust model performance [37]. Children’s tendency to move, cry, or resist airway devices increases the likelihood of motion artifacts in monitoring data (e.g., capnography, SpO₂). AI models must be highly resilient to this noise, requiring specialized training and validation [39].

The ethical and logistical constraints of collecting large, high-quality pediatric datasets—especially in emergency settings—hinder the development and validation of robust ML models. Many current algorithms rely on adult or mixed-population data, limiting their reliability in the pediatric context [40].

Implementation and Ethical Concerns

The adoption of these advanced technologies introduces practical and ethical considerations that must be proactively managed. There is a concern regarding the “automation paradox,” where over-reliance on AI technologies could lead to the deskilling of healthcare professionals. Clinicians may become dependent on automated systems, eroding the essential human judgment and manual proficiency required to manage complex or unpredictable cases when technology fails. Maintaining clinical oversight and ongoing manual skills training is paramount [29].

The high cost of implementing and maintaining sophisticated AI systems risks exacerbating existing disparities between high-resource and low-resource healthcare settings. AI solutions must be adaptable, cost-effective, and user-friendly to ensure equitable access to advanced care [41]. Integrating AI into clinical practice raises significant ethical and regulatory concerns regarding patient safety and accountability. Validation in pediatric populations is subject to stricter regulatory scrutiny to ensure the safety and efficacy of these systems in emergency scenarios [31].