ArticleFrontiers in surgery2025
Development of a prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty: an automated machine learning approach.
Article in Frontiers in surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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5 authors.
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Abstract
Objective: To develop an automated machine learning (AutoML)-based prognostic prediction model and visualization system for autologous costal cartilage rhinoplasty (ACCR), addressing the clinical challenges of postoperative complications and satisfaction disparity. Methods: A retrospective cohort of 447 ACCR patients (2019-2024) was analyzed, integrating 20+ parameters spanning biological, surgical, and behavioral domains. We proposed an improved metaheuristic algorithm (INPDOA) for AutoML optimization, validated against 12 CEC2022 benchmark functions. Bidirectional feature engineering identified critical predictors, and SHAP values quantified variable contributions. A MATLAB-based clinical decision support system (CDSS) was developed for real-time prognosis visualization. Results: The INPDOA-enhanced AutoML model outperformed traditional algorithms, achieving a test-set AUC of 0.867 for 1-month complications and Conclusion: This study establishes the first AutoML-driven prognostic framework for ACCR, effectively bridging the gap between surgical precision and patient-reported outcomes. Its integration of dynamic risk prediction and explainable AI offers a paradigm for aesthetic surgical decision-making.
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