Radiomics and anaesthetic planning: Quantitative imaging as a new frontier in preoperative risk assessment

Table 6 Comparison of traditional and artificial intelligence/radiomics-based airway assessment approaches

Assessment method	Sensitivity (%)	Specificity (%)	AUC	Advantages	Limitations
Mallampati score	20-62	82-97	0.55-0.65	Simple; bedside; no equipment needed	Low sensitivity; subjective; operator-dependent
Thyromental distance	25-50	80-95	0.60-0.70	Quick measurement; objective distance	Poor positive predictive value; single parameter
CT-based airway analysis	71-91	85-95	0.80-0.91	Objective 3D assessment; quantitative measurements	Requires CT; radiation exposure; not routine
Facial image deep learning	80-82	84-90	0.81-0.86	Non-invasive; rapid; smartphone-compatible	Requires validation across ethnicities; lighting dependency
Combined ML models	80-90	90-100	> 0.80	Integrates multiple parameters; outperforms individual tests	Limited emergency applicability; dataset-dependent

Reported performance ranges are pooled across heterogeneous studies and do not represent single prospectively validated estimates. CT: Computed tomography; AUC: Area under the curve; ML: Machine learning; 3D: Three-dimensional.