Abstract
This study aimed to develop and validate a predictive nomogram for diagnosing radicular grooves (RG) in maxillary lateral incisors (MLIs), integrating demographic information, anatomical measurements, and Cone Beam Computed Tomography (CBCT) data to diagnose the RG in MLIs based on the clinical observation before resorting to the CBCT scan. A retrospective cohort of orthodontic patients from the School and Hospital of Stomatology, Wuhan University, was analyzed, including demographic characteristics, photographic anatomical assessments, and CBCT diagnoses. The cohort was divided into development and validation groups. Univariate and multivariate logistic regression analyses identified significant predictors of RG, which informed the development of a nomogram. This nomogram's performance was validated using receiver operating characteristic analysis. The study included 381 patients (64.3% female) and evaluated 760 MLIs, with RG present in 26.25% of MLIs. The nomogram incorporated four significant anatomical predictors of RG presence, demonstrating substantial predictive efficacy with an area under the curve of 0.75 in the development cohort and 0.71 in the validation cohort. A nomogram for the diagnosis of RG in MLIs was successfully developed. This tool offers a practical checklist of anatomical predictors to improve the diagnostic process in clinical practice. The developed nomogram provides a novel, evidence-based tool to enhance the detection and treatment planning of MLIs with RG in diagnostic and therapeutic strategies.
Published Version
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