Airway management remains one of the defining responsibilities of anaesthesiologists, and yet, despite advances in training, devices and algorithms, unanticipated difficult airways continue to pose significant risks. Despite better survival outcomes after airway- and respiratory-related cardiac arrests as per the recently published UK National Audit Programme 7, the price of survival was often high, with many patients experiencing irreversible hypoxaemic injury or extended critical care admission.1 Large national audits have repeatedly demonstrated that predictors of airway difficulty, though often present, are not recognised. This persistent gap highlights an uncomfortable reality: universally practised traditional bedside airway assessment is frequently insufficient, inconsistently standardised and has low sensitivities, high variability and limited predictive accuracy. Multivariate scoring systems, including the Wilson score, El-Ganzouri risk index and modified LEMON score, were designed to minimise inter-assessor variability and enhance the predictive accuracy of difficult airway assessment, yet significant limitations persist. Continuous training and timely skill upgradation of practising anaesthesiologists are therefore imperative. In this context, the narrative review ‘Diagnostic Imaging in Airway Assessment: Current Perspectives and Advances’2 provides a timely and comprehensive overview of how imaging modalities are reshaping our approach in preoperative airway evaluation. The integration of these modalities with artificial intelligence (AI) is futuristic and helps characterise anatomy, predict difficulty, guide interventions and even personalise airway management. This is an exciting transition we are witnessing, with direct implications for patient safety and training in the near future.3 The authors effectively argue that the upper airway – particularly the pharyngo-laryngo-tracheal region – represents an anatomical ‘blind spot’ where clinical examination offers limited insight. Incorporating modalities such as nasal endoscopy and radiological imaging provides vital information about anatomy for better airway planning. Plain radiographs are easily available and non-invasive, and these help estimate the extent of traumatic injuries or tracheal deviations due to any pathology, such as goitre or tumours. These do indicate obvious tracheal narrowings; however, they may not provide details about the nature or exact location of the Central Airway Obstruction. Advanced imaging modalities such as computed tomography (CT) scans or magnetic resonance imaging (MRI) are often required for a more comprehensive airway evaluation and diagnosis. The high-resolution structural details allow clinicians to appreciate distortions, compressions, secondary tissue involvement and the true extent of the underlying pathology. CT/MRI images in complex airway lesions such as head-and-neck malignancies, deep neck infections, airway trauma or congenital abnormalities can significantly improve the airway planning and preparedness. Conical-beam CT is sometimes used in such situations, as it provides excellent information about the hard tissues, for example teeth and bones, and enables 3D reconstruction of the relevant airway spaces and volumes. The risk of radiation-induced harm remains a significant barrier to the widespread use of radiation-based modalities in the general population. Point-of-care ultrasound (POCUS) in airway management has been a practice-changing development in recent years. Its utility in locating the cricothyroid membrane, confirming correct endotracheal tube placement, predicting difficult laryngoscopy (e.g. using the difficult airway evaluation with sonography DARES protocol) and guiding airway rescue strategies makes it an increasingly valuable component of modern airway practice. There is evidence to show that POCUS may improve the first-pass success for procedures such as percutaneous tracheostomy too. The key limitations remain operator dependence and variability in measurement thresholds across populations. Flexible nasal endoscopy is a simple procedure often performed in the outpatient or emergency department settings by an Ear, Nose and Throat specialist. It may be of vital importance to an anaesthesiologist as these often reveal dynamic abnormalities and offer real-time assessment of airway patency and function, which is particularly valuable in patients with vocal cord abnormalities or other such obstructive lesions. Virtual bronchoscopy and 3D airway reconstructions (Virtual Endoscopy VE) allow clinicians to ‘fly through’ narrowed segments, facilitate preemptive mapping, plan airway device selection and even plan front-of-neck access.4 In a study, the provision of VE findings and CT images to select high-risk patients led to significant modifications to the initial airway plans.5 The review appropriately emphasises the transformative potential of AI in airway assessment. The integration of imaging data into predictive models may allow clinicians to move beyond subjective bedside scoring systems towards an objective, data-driven risk stratification. AI-based tools could assist in recognising subtle anatomical patterns, quantifying airway dimensions6 and supporting airway planning decisions even in a remote location via telemedicine. Despite the promise of these advances, important challenges remain. All assessment methods need robust validation before they can be integrated into any institution’s clinical workflow. Radiation exposure, costs, resource availability and training needs are important considerations. Evidence associating imaging modalities with improved airway outcomes remains evolving, as many studies on the topic have been observational studies or single-centre trials. Future research must focus on defining which patients benefit most, establishing training frameworks and seamlessly integrating imaging and AI into airway algorithms without complexities. The views presented in the article ‘Diagnostic Imaging in Airway Assessment: Current Perspectives and Advances’2 serve as an important reminder that airway evaluation is entering a new era – one in which visualisation, digital reconstruction and predictive intelligence may complement traditional bedside assessment. Training and updating one’s skills is a need of the hour. As an anaesthesiologist continues to prioritise safety, a comprehensive knowledge of the diagnostic imaging modalities available enhances his ability to ‘see beyond the bedside’, which is pivotal in reducing unexpected airway events, while delivering precise, patient-specific airway care. Consent statement Not applicable Authors contribution Dr Rashmi N R - Review of literature and main manuscript drafting. Dr Nishanth Sahay - Editing, proofreading and final approval. Disclosure of use of artificial intelligence (AI)-assistive or generative tools ChatGpt was used for editing and grammar correction of the manuscript. Financial support and sponsorship Nil. Conflict of interest Nil.
Rajappa et al. (Thu,) studied this question.