The Course of Nasolabial Angle Loss After Rhinoplasty and Prediction of Late Outcome Using Machine Learning: A Proof-of-Concept Study

Background/Objectives: The primary objective of this study was to predict the absolute nasolabial angle (NLA) at postoperative month 12 using early postoperative measurements. Secondary objectives were to characterize longitudinal NLA changes through month 12 and quantify residual angle loss. Methods: This single-center retrospective cohort study included 78 patients who underwent primary open rhinoplasty and had complete standardized lateral photographs from the preoperative assessment and postoperative months 1, 3, 6, and 12. The absolute NLA at month 12 was designated as the primary outcome and prediction target. Longitudinal NLA changes and residual loss from months 1, 3, and 6 to month 12 were secondary outcomes. NLA was measured using a Python (version 3.12.13)/OpenCV-based semi-automatic image-analysis system. Intraobserver and interobserver reliability were evaluated in a stratified random sample of 40 images measured twice by two physicians using intraclass correlation coefficients and Bland–Altman analysis. Linear and machine-learning models were evaluated using repeated nested cross-validation. Three simple benchmarks were also assessed using identical patient-level outer splits: the training-fold mean, persistence (NLA12 = NLA6), and linear regression using NLA6 alone. Results: NLA was highest at 1 month and decreased significantly thereafter, while remaining above the preoperative level at month 12. Measurement reliability was excellent, although measurement uncertainty was comparable to the mean late change. Elastic Net predicted absolute NLA12 with an MAE of 3.47°. It outperformed the training-mean and persistence benchmarks but did not outperform the one-predictor linear regression model (Mean Absolute Error (MAE) = 3.35°; paired MAE difference, 0.12°; 95% CI, −0.06 to 0.30). Patient-specific loss from months 6 to 12 could not be predicted reliably. Conclusions: The month-6 NLA provided moderate predictive information for the absolute 12-month angle. More complex multivariable algorithms did not provide additional predictive value over a simple regression based on NLA6 alone, and individualized late rotation loss remained unpredictable with the available variables.