ArticleJAMA facial plastic surgery2019
Categorization and Analysis of Nasal Base Shapes Using a Parametric Model.
Article in JAMA facial plastic surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed, 8 citations in OpenAlex.
- Qualitative Terms Used In Rhinoplasty: A Scoping Review -Toward Establishing a Bridge Between Qualitative Terminology and Quantifiability.Aesthetic plastic surgery · 2025Article
- Automatic Apparent Nasal Index from Single Facial Photographs Using a Lightweight Deep Learning Pipeline: A Pilot Study.Medicina (Kaunas, Lithuania) · 2025Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
importanceNasal base view is important for rhinoplasty analysis. Although some descriptors of nasal base shape exist, they are largely subjective and qualitative.
objectiveTo evaluate a parametric model of nasal base shape and compare it with categorization by surgeons to create an objective classification system for clinical evaluation and communication. DESIGN, SETTING, AND
participantsRetrospective cohort review of deidentified photographs of 420 patients evaluated for possible facial plastic surgery at a tertiary care academic medical center between January 2013 and June 2017. The nasal bases were classified into 6 shape categories (equilateral, boxy, cloverleaf, flat, round, and narrow) via visual inspection. The contour of each nasal base was traced using MATLAB software (MathWorks Inc). The software then performed a curve fit to the parametric model with output of values for 5 parameters: projection-to-width ratio, the anterior-posterior positioning of the tip bulk, symmetry, degree of lateral recurvature of the nasal base, and size. The differences among shape categories for each parameter were analyzed using 1-way analysis of variance. Pairwise comparisons were then performed to ascertain how the various shapes differed. Finally, a multinomial logistic regression model was used to predict nasal base shape using parameter values. Data were analyzed between April 2017 and January 2018. MAIN OUTCOMES AND MEASURES: An algorithm that categorized nasal base shapes into 6 categories.
resultsThe 420 nasal base photographs of patients evaluated for possible plastic surgery were categorized into 1 of 6 categories; 305 photographs were readily classified, and the remaining 115 were termed unclassified and were categorized. For both the classified and unclassified nasal base groups, there were statistically significant differences between projection-to-width ratio (classified, F5,299 = 21.51; unclassified, F4,100 = 10.59; P < .001), the anterior-posterior positioning of the tip bulk (classified, F5,299 = 3.76; P = .003; unclassified, F4,110 = 4.54; P = .002), and degree of lateral recurvature of the nasal base (classified, F5,299 = 24.14; unclassified, F4,100 = 7.21; P < .001). A multinomial logistic regression model categorization was concordant with surgeon categorization in 201 of 305 (65.9%) cases of classified nasal bases and 38 of 115 (33.0%) unclassified nasal bases. CONCLUSIONS AND RELEVANCE: The parametric model may provide an objective and numerical approach to analyzing nasal base shape. LEVEL OF EVIDENCE: NA.
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