Mobile App for Enhanced Anterior Cruciate Ligament (ACL) Assessment in Conscious Subjects: “Pivot-Shift Meter”

Author:

Berumen-Nafarrate Edmundo12,Ramos-Moctezuma Ivan Rene2,Sigala-González Luis Raúl2,Quintana-Trejo Fatima Norely2,Tonche-Ramos Jesus Javier2,Portillo-Ortiz Nadia Karina2ORCID,Cañedo-Figueroa Carlos Eduardo2ORCID,Aguirre-Madrid Arturo3

Affiliation:

1. Star Medica Chihuahua Hospital, Perif. de la Juventud 6103, Fracc. El Saucito, Chihuahua 31110, Mexico

2. Faculty of Medicine and Biomedical Sciences, University Autonomous of Chihuahua (UACH), Chihuahua 31110, Mexico

3. Department of Orthopedic Surgery, Star Medica Chihuahua Hospital, Perif. de la Juventud 6103, Fracc. El Saucito, Chihuahua 31110, Mexico

Abstract

Anterior cruciate ligament (ACL) instability poses a considerable challenge in traumatology and orthopedic medicine, demanding precise diagnostics for optimal treatment. The pivot-shift test, a pivotal assessment tool, relies on subjective interpretation, emphasizing the need for supplementary imaging. This study addresses this limitation by introducing a machine learning classification algorithm integrated into a mobile application, leveraging smartphones’ built-in inertial sensors for dynamic rotational stability assessment during knee examinations. Orthopedic specialists conducted knee evaluations on a cohort of 52 subjects, yielding valuable insights. Quantitative analyses, employing the Intraclass Correlation Coefficient (ICC), demonstrated robust agreement in both intraobserver and interobserver assessments. Specifically, ICC values of 0.94 reflected strong concordance in the timing between maneuvers, while signal amplitude exhibited consistency, with the ICC ranging from 0.71 to 0.66. The introduced machine learning algorithms proved effective, accurately classifying 90% of cases exhibiting joint hypermobility. These quantifiable results underscore the algorithm’s reliability in assessing knee stability. This study emphasizes the practicality and effectiveness of implementing machine learning algorithms within a mobile application, showcasing its potential as a valuable tool for categorizing signals captured by smartphone inertial sensors during the pivot-shift test.

Publisher

MDPI AG

Reference47 articles.

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3. Latarjet, M., and Liard, A.R. (2023, January 10). Human Anatomy, 5th ed.; Panamericana. Available online: https://www.medicapanamericana.com/mx/libro/coleccion-latarjet-anatomia-humana-incluye-version-digital-2-tomos.

4. Healthy Gait: Review of Anatomy and Physiology of Knee Joint;Kumar;Int. J. Curr. Res. Rev.,2020

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