Abstract
Background Measuring the exact quantitative values of lordotic curves is a vital factor in clinical settings to prevent musculoskeletal deformities in the future. Existing lordotic assessment methods are very diverse, expensive, inaccurate, and not handy, and their availability cannot be maintained in every clinic setup. Aim The purpose of this research was to study the reliability of a mobile app as a feasible method to measure lumbar lordosis angle using a lateral view radiograph. Methodology A lateral view low back region radiograph of 58 participants was taken based on the criteria, and the experienced physiotherapists uploaded the X-ray to the mobile app and measured the lordotic angles with the support of machine learning algorithms. Descriptive statistics were used to calculate the average and dispersion of the data of the lumbar lordosis angle measured using the mobile app method (Statistical Package for the Social Sciences (IBM SPSS Statistics for Windows, IBM Corp., Version 23, Armonk, NY)). Results Associations between and within raters were assessed using the Karl Pearson coefficient of correlation (1.000). Inter-rater and intra-rater reliability were determined by using Cronbach's alpha (.966) and the split-half method. The internal consistency of the mobile app was found to be good. Conclusions Based on our findings, we conclude that the mobile app method is reliable and useful in measuring lumbar lordosis objectively with less effort. Since the app is handy on smartphones, physiotherapists can conduct an objective lumbar lordosis assessment in clinical settings.