Berumen Nafarrate, E.; Ramos Moctezuma, I.R.; Sigala González, L.R.; Quintana Trejo, F.N.; Tonche Ramos, J.J.; Portillo Ortiz, N.K.; Cañedo Figueroa, C.E.; Aguirre Madrid, A. Integrating Bayesian Classification into a Mobile App for Enhanced ACL Assessment: “Pivot-Shift Meter App”. Preprints2024, 2024030152. https://doi.org/10.20944/preprints202403.0152.v1
APA Style
Berumen Nafarrate, E., Ramos Moctezuma, I.R., Sigala González, L.R., Quintana Trejo, F.N., Tonche Ramos, J.J., Portillo Ortiz, N.K., Cañedo Figueroa, C.E., & Aguirre Madrid, A. (2024). Integrating Bayesian Classification into a Mobile App for Enhanced ACL Assessment: “Pivot-Shift Meter App”. Preprints. https://doi.org/10.20944/preprints202403.0152.v1
Chicago/Turabian Style
Berumen Nafarrate, E., Carlos Eduardo Cañedo Figueroa and Arturo Aguirre Madrid. 2024 "Integrating Bayesian Classification into a Mobile App for Enhanced ACL Assessment: “Pivot-Shift Meter App”" Preprints. https://doi.org/10.20944/preprints202403.0152.v1
Abstract
ACL instability poses a significant challenge in traumatology and orthopedic medicine, often requiring accurate diagnosis for appropriate treatment. While the pivot-shift test offers a crucial means of assessment, its reliance on subjective interpretation underscores the need for supplementary imaging studies. This study aims to address this limitation by developing a Bayesian classification algorithm tailored for integration into a mobile application. Using the built-in inertial sensors of smartphones, this new approach aims to dynamically evaluate rotational stability during knee examinations. Orthopedic specialists conducted knee evaluations on 52 subjects, with subsequent analysis revealing interesting insights. Intraobserver and interobserver analyses, as measured by ICC, demonstrated strong agreement both in terms of timing between maneuvers (ICC = 0.94) and signal amplitude (ICC = 0.71-0.66). Notably, the Bayesian algorithm successfully classified 95% of joint hypermobility cases, with an additional 7 cases of hyperlaxity identified by the Pivot-Shift Meter (PSM). These findings highlight the practicality and effectiveness of implementing a Bayesian classification algorithm within a mobile application for assessing and categorizing signals captured by smartphone inertial sensors during the pivot-shift test.
Keywords
ACL; Bayesian classifier; Mobile application; Pivot-Shift; Rotational stability
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
