Quantitative Analysis and Classification of the Nasal Base Using a Parametric Model

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Abstract

Importance

The nasal base view is often overlooked in rhinoplasty analysis and, unlike lateral and frontal views, lacks detailed quantitative analysis and descriptors. While shape-category analysis of the nasal base is well established, these descriptive methods remain subjective and do not facilitate quantitative analysis.

Objective

To establish a simple and quantitative classification scheme using a multiple-parameter numerical model for analyzing and describing the shape of the nasal base.

Design, Setting, and Participants

Deidentified photographs of the nasal base view were analyzed without knowledge of patients’ pathology or medical history. Each nose was classified into 1 of 6 categories derived from literature (equilateral, narrow, flat, cloverleaf, boxy, and round). Finite parametric modeling was performed on each nose, and the correlations between the resulting parameters and the 6 categories were analyzed. Photographs for this study were acquired from the practice of a single facial plastic surgeon (B.J.F.W.) at a tertiary care academic medical center. One hundred twenty-one consecutive patients who had nasal base view photographs taken were included in the study.

Main Outcomes and Measures

All of the 121 images were classified into 1 of the 6 categories by 1 reviewer (C.H.B.). The contour of each nasal base was curve fit to a 5-parameter numerical model. The 5 parameters controlled base size, deviation from the midline, projection-to-width ratio, degree of nasal alar recurvature, and anterior-posterior positioning of nasal base bulk. A numerical value for each nasal base shape type was predicted by the parametric model.

Results

In 121 patient photographs, the parametric model generated shapes that accurately matched the tracing of the actual nasal base contours with an average correlation coefficient of greater than 0.98. This finding indicates close approximation of the nasal base shape with the curve fit constructed by the PM. Parameters b (projection-to-width ratio) and e (roundedness) were shown to have significant differences among the groups (F statistic, 8.88; P < .001 and F statistic, 13.05; P < .001, respectively). These two curve-fit parameters alone could be used to classify nasal shape into 1 of the 6 clinically determined base geometries.

Conclusions and Relevance

A numerical approach to classify nasal base shape was developed using a 5-parameter model and tested against subjective analysis. This model may aid in the advancement of algorithm-driven objective nasal analysis techniques, preoperative modeling, intraoperative guidance, and surgical outcome measures beyond using Likert scales.

Level of Evidence

NA.

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