Mobile App for Enhanced Anterior Cruciate Ligament (ACL) Assessment in Conscious Subjects: “Pivot-Shift Meter”
<p>(<b>A</b>) X, Y, and Z axis of movement of gyroscopes integrated in a mobile device. (<b>B</b>) Placement of the mobile device on the anteromedial face of the tibia, two finger widths from the tibial tuberosity and attached with a sports-type cell phone bracelet. (<b>C</b>) X-axis signal can highlight the sequence of the pivot-shift maneuver: (a) flexion movement, (b) extension movement, and (c) joint reduction or “pivot”. It is also noticeable that this pattern repeats, giving a clue to the start of the next maneuver due to the peaks (PI–III).</p> "> Figure 2
<p>Data Recording PSM App and signal processing flowchart.</p> "> Figure 3
<p>(<b>A</b>–<b>F</b>) Signal processing. After normalizing the signal, it was segmented to obtain the important portion of data, which corresponds to the “pivot” (<b>F</b>). (<b>G</b>) Graphic difference between classes: 1, 2, 3, and 4 have a sinusoidal-based waveform, while class 0 has a plateau shape.</p> "> Figure 4
<p>Confusion matrices of AI tested for class 2 (<b>A</b>), class 3 (<b>B</b>), and class 4 (<b>C</b>).</p> ">
Abstract
:1. Introduction
2. Objectives
- Evaluate the efficacy of AI for analyzing and categorizing signals obtained from inertial sensors during pivot-shift tests to identify the most effective approach for integration into mobile software.
- Evaluate the reliability and effectiveness of the developed software in assessing knee joint stability through intraobserver and interobserver analyses.
- Compare the results obtained from the developed application with those from the KT-1000 arthrometer, a gold standard tool for assessing knee laxity.
- Investigate the potential of the developed software to enhance the precision and objectivity of clinical evaluations of ACL injuries and other joint conditions.
3. Materials and Methods
3.1. Population
3.2. Measurements
- Signal capture: The PSM application recorded angular velocity (rad/s) data corresponding to the three axes of movement on the mobile device at a sampling rate of 100 Hz, resulting in 500 data points captured in 5 s—this high-frequency data acquisition aimed to capture precise and detailed measurements of the pivot-shift maneuver. X-axis data was used because it corresponds best to the movement of the leg when executing the maneuver concerning the position of the cell phone (Figure 1A).
- Data storage: The recorded data was securely saved in a database, which was configured to provide a user-friendly experience for the physicians involved in the study. This database facilitated the physician’s ability to review and follow up on each case (Figure 2). During the study, the pivot-shift maneuver was performed three consecutive times on each test subject. The mobile device was placed on the anteromedial aspect of the tibia, approximately two fingers away from the tibial tuberosity. To ensure secure placement, the mobile device was connected to a sports-type cellular armband with the PSM application pre-installed, allowing for accurate recording of the maneuver.
3.3. Data Recording in the PSM Application by the Evaluator
- Patient Data Recording: patient initials, age, gender, height, and weight (Figure 2).
- Placement of the Cell Phone: the cell phone with the PSM application installed is placed on the tibial tuberosity of the patient, securing it approximately two fingers below the patella with an elastic band, ensuring that the device is slightly tilted towards the medial aspect of the tibia (Figure 1B).
- Execution of the Pivot Maneuver: the evaluator holds the ankle on the medial side with the hand corresponding to the patient’s leg, and with the opposite hand, the posterior part of the leg is held at the level of the tibial head, rotating slightly medially. Subsequently, the leg is flexed until reaching a 90° angle.
- Results Recording: the evaluators are instructed to perform two maneuvers and save the results obtained with the PSM application. Additionally, the application allows for adding observations, clinical classification according to IKDC criteria, results of digital arthrometry (KT-1000), results of imaging studies, arthroscopic images, and notes with relevant clinical information (Table 1).
Input Data | Description |
Patient Demographics | Initials, age, gender, height, weight |
Gyroscope Signals | Angular velocity (rad/s) recorded at 100 Hz (500 data points over 5 s) |
Clinical Classification | Results of the IKDC criteria, digital arthrometry (KT-1000), and other relevant clinical information. |
3.4. Statistical Analysis
3.5. Classification Algorithm
4. Results
4.1. Signal Interpretation
4.2. ICC Analysis
4.3. Classification Algorithm
4.4. Highlights
5. Discussion
6. Limitations
7. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations List
ACL | Anterior Cruciate Ligament |
PCL | Posterior Cruciate Ligament |
MCL | Medial Collateral Ligament |
LCL | Lateral Collateral Ligament |
AM | Anteromedial |
PL | Posterolateral |
PS | Pivot-Shift |
PSM | Pivot-Shift Meter |
ICC | Intraclass Correlation Coefficient |
SD | Standard Deviation |
Max | Maximum |
R + | Range plus average |
NV | Normalized Value |
OV | Original Value |
SVM | Support Vector Machine |
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Grade | Difference in mm | Meaning |
---|---|---|
0 | 0–2 | Almost null laxity |
1 | 3 | Low laxity |
2 | 4–5 | Considerable laxity |
3 | >6 | High laxity |
Evaluator A | Evaluator N | |||
---|---|---|---|---|
Peak I | Peak II | Peak III | Peaks I, II, III | |
Subject n − 1 | … | … | ||
Subject 45 | Time of detection (cs) | … | ||
111.00 | 267.00 | 448.00 | ||
Amplitude (rad/s) | ||||
137.54 | 179.86 | 156.83 | ||
Subject n + 1 | … | … |
Grade of Laxity | Number of Subjects |
---|---|
0 | 10 |
1 | 9 |
2 | 9 |
3 | 2 |
Machine Learning Method | Accuracy (%) |
---|---|
Logistic Regression | 40 |
Support Vector Machine | 20 |
Decision Tree | 40 |
Random Forest | 40 |
Machine Learning Method | Accuracy (%) | Recall (%) | Precision (%) | F1-Score |
---|---|---|---|---|
Logistic Regression | 28.57 | 28.57 | 28.57 | 0.2857 |
Support Vector Machine | 100 | 100 | 100 | 1.0 |
Decision Tree | 85.71 | 85.71 | 85.71 | 0.8571 |
Random Forest | 85.71 | 85.71 | 85.71 | 0.8571 |
Class 2 | Accuracy (%) | Recall (%) | Precision (%) | F1-Score |
---|---|---|---|---|
Logistic Regression | 100 | 100 | 100 | 100 |
Support Vector Machine | 33 | 33 | 33 | 33 |
Decision Tree | 100 | 100 | 100 | 100 |
Random Forest | 100 | 100 | 100 | 100 |
Class 3 | Accuracy (%) | Recall (%) | Precision (%) | F1-Score |
Logistic Regression | 0 | 0 | 0 | 0 |
Support Vector Machine | 33 | 33 | 33 | 33 |
Decision Tree | 100 | 100 | 100 | 100 |
Random Forest | 66 | 66 | 66 | 66 |
Class 4 | Accuracy (%) | Recall (%) | Precision (%) | F1-Score |
Logistic Regression | 66 | 66 | 66 | 66 |
Support Vector Machine | 66 | 66 | 66 | 66 |
Decision Tree | 100 | 100 | 100 | 100 |
Random Forest | 100 | 100 | 100 | 100 |
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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. Mobile App for Enhanced Anterior Cruciate Ligament (ACL) Assessment in Conscious Subjects: “Pivot-Shift Meter”. J. Pers. Med. 2024, 14, 651. https://doi.org/10.3390/jpm14060651
Berumen-Nafarrate E, Ramos-Moctezuma IR, Sigala-González LR, Quintana-Trejo FN, Tonche-Ramos JJ, Portillo-Ortiz NK, Cañedo-Figueroa CE, Aguirre-Madrid A. Mobile App for Enhanced Anterior Cruciate Ligament (ACL) Assessment in Conscious Subjects: “Pivot-Shift Meter”. Journal of Personalized Medicine. 2024; 14(6):651. https://doi.org/10.3390/jpm14060651
Chicago/Turabian StyleBerumen-Nafarrate, Edmundo, Ivan Rene Ramos-Moctezuma, Luis Raúl Sigala-González, Fatima Norely Quintana-Trejo, Jesus Javier Tonche-Ramos, Nadia Karina Portillo-Ortiz, Carlos Eduardo Cañedo-Figueroa, and Arturo Aguirre-Madrid. 2024. "Mobile App for Enhanced Anterior Cruciate Ligament (ACL) Assessment in Conscious Subjects: “Pivot-Shift Meter”" Journal of Personalized Medicine 14, no. 6: 651. https://doi.org/10.3390/jpm14060651