Diagnostic imaging as a tool for airway risk stratification in anesthesia

Introduction

Predicting difficult laryngoscopy has long been a central concern in anesthesiology. Although numerous physical and clinical predictors, such as the Mallampati score, thyromental distance, and hyomental ratio, are routinely used, none provide consistently high sensitivity or specificity. Airway difficulty is multifactorial and depends not only on soft-tissue dynamics but also on craniofacial skeletal architecture.

In this context, the study by Sekino et al. (1) introduces an important contribution by demonstrating that the Sassouni cephalometric classification, originally developed for orthodontic evaluation, can predict difficult laryngoscopy. Their finding that Sassouni type 5 morphology (characterized by mandibular retrognathia and deep bite) is strongly associated with increased Cormack-Lehane grade (≥3) highlights how lateral cephalometry, already routine in orthognathic planning, may enhance preoperative airway assessment.

From cephalometry to airway prediction: historical and conceptual links

Sekino et al.’s work (1) aligns with earlier imaging-based investigations that identify mandibular position as a key determinant of airway accessibility. Kohjitani et al. (2) showed that increased mandibular plane-hyoid distance and reduced mandibular length (Ba-Gn) correlate with narrower pharyngeal space and greater laryngoscopic difficulty. Additional studies, including those by Karm et al. (3), Lee et al. (4), and Kim et al. (5), reinforce that variations in mandibular morphology and its relationship to the hyoid bone directly influence upper airway geometry.

Beyond static anatomy, anesthetic physiology also contributes to airway risk. Eastwood et al. (6) demonstrated that propofol increases upper airway collapsibility by suppressing genioglossus muscle activity. In patients with retrognathic features, this physiologic vulnerability combines with pre-existing structural narrowing, creating a compounded risk for difficult laryngoscopy.

Together, these findings indicate that mandibular retrognathia affects airway accessibility not only by shifting skeletal landmarks but also by reducing functional airway stability during anesthesia.

Expanding the radiological toolbox: lessons from recent studies

Recent radiologic studies emphasize that cephalometric analysis can extend conventional airway evaluation and aid in identifying at-risk patients. Kharrat et al. (7) demonstrated that maxillary length and atlanto-occipital distance are independent radiographic predictors of difficult direct laryngoscopy, suggesting that maxillary projection and limited cervical extension also contribute to reduced glottic exposure.

Ito et al. (8) expanded this perspective by evaluating pharyngeal geometry through three cephalometric points (A, B, and C). Their findings showed that smoother posterior pharyngeal curvature (larger ∠ABC) and more posterior alignment of these landmarks predicted greater success in blind nasotracheal intubation. This supports the concept that lateral cephalometry visualizes both skeletal and soft-tissue relationships relevant to airway instrumentation.

These studies collectively underscore that cephalometry provides reproducible anatomical information, low radiation exposure, and practical utility/qualities that strengthen its role as an adjunct to classical clinical predictors.

Integrating morphology, function, and clinical strategy

Taken together, these studies form a cohesive narrative on how radiological anatomy informs airway management:

• Sekino et al. (1): skeletal classification (Sassouni 5) as a predictor of difficult laryngoscopy.
• Kohjitani et al. (2): mandibular and hyoid geometry determining laryngoscopic exposure.
• Ito et al. (8): pharyngeal curvature (∠ABC) predicting success in blind nasotracheal intubation.
• Kharrat et al. (7): maxillary length and atlanto-occipital distance as radiographic risk factors for limited exposure.

What unites these approaches is the standard lateral cephalogram—a low-cost, high-yield imaging tool already ubiquitous in oral and maxillofacial settings. Its application to airway assessment is especially advantageous in preoperative orthodontic and orthognathic contexts, where imaging is already part of patient preparation. Moreover, by quantifying both hard and soft tissue relationships, cephalometry bridges a methodological gap between surface-based predictors (e.g., Mallampati, thyromental distance) and advanced imaging modalities computed tomography (CT) and magnetic resonance imaging (MRI), offering a practical middle ground.

Clinical and research implications

Clinically, adopting cephalometric indices such as the Sassouni classification, pharyngeal curvature (∠ABC), or atlanto-occipital distance may substantially improve preoperative risk stratification in dental and maxillofacial anesthesia. These parameters can be easily integrated into standard imaging workflows and automatically extracted with artificial intelligence (AI)-assisted software, facilitating objective, reproducible, and radiation-minimal airway evaluation.

From a research standpoint, these studies collectively open a pathway toward radiomics-based airway prediction models. Quantitative imaging features extracted from cephalograms or three-dimensional (3D) reconstructions could be incorporated into machine learning frameworks to predict difficult laryngoscopy with greater accuracy. Future multicenter studies should aim to validate cephalometric thresholds across populations, considering ethnic and gender-related craniofacial variability.

In addition to its potential advantages, several important limitations must be acknowledged when considering the clinical applicability of imaging-based airway stratification. Most evidence in this field, including studies using the Sassouni classification, derives from orthodontic or dentofacial populations who undergo cephalometric imaging for dental indications, introducing a selection bias toward patients with craniofacial abnormalities and limiting generalizability to the broader surgical or anesthetic population. Moreover, the Sassouni classification itself, although widely used, is subject to interobserver variability and does not capture the 3D complexity of the airway. The reliance on two-dimensional lateral radiographs also disregards anatomical asymmetries and volumetric relationships that would be visible in CT or MRI-based 3D reconstructions. For these reasons, the extension of cephalometric-based predictors to routine anesthetic practice should be approached with caution, and imaging findings should always be interpreted in conjunction with comprehensive clinical evaluation. A more critical and comparative appraisal of these radiologic predictors, ideally within heterogeneous and non-orthodontic populations, is necessary to establish their true role in airway risk assessment.

Conclusions

The integration of cephalometric radiography into anesthetic airway management represents an elegant convergence of radiology, dentistry, and anesthesiology. The work by Sekino et al. (1), supported by insights from Kohjitani et al. (2), Ito et al. (8), and Kharrat et al. (7), demonstrates that the morphological blueprint of the craniofacial skeleton, readily visible in a simple lateral cephalogram, can provide actionable information for anesthetic safety. In essence, these studies reaffirm a central principle: the key to anticipating airway difficulty lies not only in surface examination but also in understanding the skeletal geometry that defines the airway’s foundation. The combination of classical cephalometric approaches with emerging quantitative and 3D methodologies may establish a hybrid model for airway assessment, one that is more precise, evidence-based, and grounded in anatomical reality. Future research should also clarify the diagnostic performance of cephalometric-based predictors in non-dental and more heterogeneous surgical populations, since current evidence primarily derives from orthodontic cohorts. In addition, studies are needed to evaluate the practical usefulness of these imaging-derived indices in urgent or emergent airway scenarios, as well as in patients who do not undergo routine radiological examinations. Establishing their validity in these settings is essential before broader clinical implementation can be recommended.

Progress in this field will depend on refining imaging methods, assessing how these measurements can be incorporated into everyday anesthetic practice, and integrating radiologic parameters with established clinical predictors. Such combined approaches may ultimately provide more reliable and clinically applicable airway risk tools.

Acknowledgments

None.

Footnote

Provenance and Peer Review: This article was commissioned by the editorial office, Journal of Oral and Maxillofacial Anesthesia. The article has undergone external peer review.

Peer Review File: Available at https://joma.amegroups.com/article/view/10.21037/joma-2025-1-36/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://joma.amegroups.com/article/view/10.21037/joma-2025-1-36/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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