Development and Validation of a Python-Based HMAR Assessment Tool: A Cross-Sectional Diagnostic Study

AbstractBackground: The Handicapping Malocclusion Assessment Record (HMAR) is aclinical index tool used to evaluate the severity of malocclusion and dentofacialdeformities for triage and public health reporting. This study describes applicationdevelopment and validation of a Python-based graphical user interface (GUI)application for standardized HMAR scoring.Methods: The application was developed using Python Tkinter, with weightedscoring based on established orthodontic indices. Validation was performed usingsimulated cases (n=50) and clinical cases (n=30) assessed by three orthodontists.Intraclass correlation (ICC) and Bland-Altman analysis were used to evaluateagreement.Results: The application demonstrated excellent reliability (ICC = 0.98, 95% CI:0.96-0.99) and strong correlation with manual scoring (r = 0.97, p < 0.001). Meanscoring time was reduced from 5.2 ± 1.1 minutes (manual) to 0.8 ± 0.3 minutes(automated).Conclusion: The HMAR Python based app provides a fast, reliable, andstandardized method for malocclusion assessment, with potential applications inclinical practice and public health reporting.