Cross-validation of a machine learning algorithm that determines anterior cruciate ligament rehabilitation status and evaluation of its ability to predict future injury

2021 ◽  
pp. 1-11
Author(s):  
Chris Richter ◽  
Erich Petushek ◽  
Hege Grindem ◽  
Andrew Franklyn-Miller ◽  
Roald Bahr ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anterior Cruciate Ligament ◽  
Cross Validation ◽  
Learning Algorithm ◽  
Cruciate Ligament ◽  
Machine Learning Algorithm ◽  
Anterior Cruciate ◽  
Rehabilitation Status

scholarly journals Machine learning algorithm to predict anterior cruciate ligament revision demonstrates external validity

2022 ◽  
Author(s):  
R. Kyle Martin ◽  
Solvejg Wastvedt ◽  
Ayoosh Pareek ◽  
Andreas Persson ◽  
Håvard Visnes ◽  
...  
Keyword(s):  
Machine Learning ◽  
Acl Reconstruction ◽  
External Validity ◽  
Learning Algorithm ◽  
Cruciate Ligament ◽  
External Validation ◽  
Model Performance ◽  
Knee Ligament ◽  
Graft Choice ◽  
Anterior Cruciate

Abstract Purpose External validation of machine learning predictive models is achieved through evaluation of model performance on different groups of patients than were used for algorithm development. This important step is uncommonly performed, inhibiting clinical translation of newly developed models. Machine learning analysis of the Norwegian Knee Ligament Register (NKLR) recently led to the development of a tool capable of estimating the risk of anterior cruciate ligament (ACL) revision (https://swastvedt.shinyapps.io/calculator_rev/). The purpose of this study was to determine the external validity of the NKLR model by assessing algorithm performance when applied to patients from the Danish Knee Ligament Registry (DKLR). Methods The primary outcome measure of the NKLR model was probability of revision ACL reconstruction within 1, 2, and/or 5 years. For external validation, all DKLR patients with complete data for the five variables required for NKLR prediction were included. The five variables included graft choice, femur fixation device, KOOS QOL score at surgery, years from injury to surgery, and age at surgery. Predicted revision probabilities were calculated for all DKLR patients. The model performance was assessed using the same metrics as the NKLR study: concordance and calibration. Results In total, 10,922 DKLR patients were included for analysis. Average follow-up time or time-to-revision was 8.4 (± 4.3) years and overall revision rate was 6.9%. Surgical technique trends (i.e., graft choice and fixation devices) and injury characteristics (i.e., concomitant meniscus and cartilage pathology) were dissimilar between registries. The model produced similar concordance when applied to the DKLR population compared to the original NKLR test data (DKLR: 0.68; NKLR: 0.68–0.69). Calibration was poorer for the DKLR population at one and five years post primary surgery but similar to the NKLR at two years. Conclusion The NKLR machine learning algorithm demonstrated similar performance when applied to patients from the DKLR, suggesting that it is valid for application outside of the initial patient population. This represents the first machine learning model for predicting revision ACL reconstruction that has been externally validated. Clinicians can use this in-clinic calculator to estimate revision risk at a patient specific level when discussing outcome expectations pre-operatively. While encouraging, it should be noted that the performance of the model on patients undergoing ACL reconstruction outside of Scandinavia remains unknown. Level of evidence III.

scholarly journals Motion Sensors-Based Machine Learning Approach for the Identification of Anterior Cruciate Ligament Gait Patterns in On-the-Field Activities in Rugby Players

Sensors ◽  
2020 ◽  
Vol 20 (11) ◽  
pp. 3029
Author(s):  
Salvatore Tedesco ◽  
Colum Crowe ◽  
Andrew Ryan ◽  
Marco Sica ◽  
Sebastian Scheurer ◽  
...  
Keyword(s):  
Machine Learning ◽  
Anterior Cruciate Ligament ◽  
Cruciate Ligament ◽  
Gradient Boosting ◽  
Support Vector ◽  
Motion Sensors ◽  
Multi Layer Perceptron ◽  
Gait Patterns ◽  
Anterior Cruciate ◽  
Rugby Players

Anterior cruciate ligament (ACL) injuries are common among athletes. Despite a successful return to sport (RTS) for most of the injured athletes, a significant proportion do not return to competitive levels, and thus RTS post ACL reconstruction still represents a challenge for clinicians. Wearable sensors, owing to their small size and low cost, can represent an opportunity for the management of athletes on-the-field after RTS by providing guidance to associated clinicians. In particular, this study aims to investigate the ability of a set of inertial sensors worn on the lower-limbs by rugby players involved in a change-of-direction (COD) activity to differentiate between healthy and post-ACL groups via the use of machine learning. Twelve male participants (six healthy and six post-ACL athletes who were deemed to have successfully returned to competitive rugby and tested in the 5–10 year period following the injury) were recruited for the study. Time- and frequency-domain features were extracted from the raw inertial data collected. Several machine learning models were tested, such as k-nearest neighbors, naïve Bayes, support vector machine, gradient boosting tree, multi-layer perceptron, and stacking. Feature selection was implemented in the learning model, and leave-one-subject-out cross-validation (LOSO-CV) was adopted to estimate training and test errors. Results obtained show that it is possible to correctly discriminate between healthy and post-ACL injury subjects with an accuracy of 73.07% (multi-layer perceptron) and sensitivity of 81.8% (gradient boosting). The results of this study demonstrate the feasibility of using body-worn motion sensors and machine learning approaches for the identification of post-ACL gait patterns in athletes performing sport tasks on-the-field even a number of years after the injury occurred.

scholarly journals Anterior Cruciate Ligament Knee Injury Detection of Sports Persons using Machine Learning Techniques

2020 ◽  
Vol 9 (4) ◽  
pp. 2189-2193
Keyword(s):  
Machine Learning ◽  
Anterior Cruciate Ligament ◽  
Expert System ◽  
Knee Injury ◽  
Cruciate Ligament ◽  
Weight Bearing ◽  
Machine Learning Techniques ◽  
Treatment Needs ◽  
Learning Techniques ◽  
Anterior Cruciate

ACL is one of most common knee injury in sports person which increases as the participation in sports increases. People participating in basketball, football, hockey and athletics generally faces more weight bearing on hip, knee and ankle which is directly breaking of muscular tissues of ACL. After this it is mandatory to rehabilitate and back to the field for sport person at same pre-injury stage. But it requires mental and physical strength to recover from ACL tear and proper treatment needs health management. In a growing age of Information Technology the use of computer and its applications are extensively used in almost all areas. The main objective of this paper is to check the use of expert system or the use of any computerized equipment while diagnosis Anterior Cruciate Ligament injury. There are many techniques and machines to predict the knee injury grade like MRI but to check the accuracy level computer expert system Machine learning techniques are best for the results.

scholarly journals A Prediction Model for Primary Anterior Cruciate Ligament Injury Using Artificial Intelligence

2021 ◽  
Vol 9 (9) ◽  
pp. 232596712110275
Author(s):  
Iskandar Tamimi ◽  
Joaquin Ballesteros ◽  
Almudena Perez Lara ◽  
Jimmy Tat ◽  
Motaz Alaqueel ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Anterior Cruciate Ligament ◽  
Prediction Model ◽  
Acl Reconstruction ◽  
Cruciate Ligament ◽  
Acl Injury ◽  
Learning Techniques ◽  
Testing Accuracy ◽  
Anterior Cruciate

Background: Supervised machine learning models in artificial intelligence (AI) have been increasingly used to predict different types of events. However, their use in orthopaedic surgery has been limited. Hypothesis: It was hypothesized that supervised learning techniques could be used to build a mathematical model to predict primary anterior cruciate ligament (ACL) injuries using a set of morphological features of the knee. Study Design: Cross-sectional study; Level of evidence, 3. Methods: Included were 50 adults who had undergone primary ACL reconstruction between 2008 and 2015. All patients were between 18 and 40 years of age at the time of surgery. Patients with a previous ACL injury, multiligament knee injury, previous ACL reconstruction, history of ACL revision surgery, complete meniscectomy, infection, missing data, and associated fracture were excluded. We also identified 50 sex-matched controls who had not sustained an ACL injury. For all participants, we used the preoperative magnetic resonance images to measure the anteroposterior lengths of the medial and lateral tibial plateaus as well as the lateral and medial bone slope (LBS and MBS), lateral and medial meniscal height (LMH and MMH), and lateral and medial meniscal slope (LMS and MMS). The AI predictor was created using Matlab R2019b. A Gaussian naïve Bayes model was selected to create the predictor. Results: Patients in the ACL injury group had a significantly increased posterior LBS (7.0° ± 4.7° vs 3.9° ± 5.4°; P = .008) and LMS (–1.7° ± 4.8° vs –4.0° ± 4.2°; P = .002) and a lower MMH (5.5 ± 0.1 vs 6.1 ± 0.1 mm; P = .006) and LMH (6.9 ± 0.1 vs 7.6 ± 0.1 mm; P = .001). The AI model selected LBS and MBS as the best possible predictive combination, achieving 70% validation accuracy and 92% testing accuracy. Conclusion: A prediction model for primary ACL injury, created using machine learning techniques, achieved a >90% testing accuracy. Compared with patients who did not sustain an ACL injury, patients with torn ACLs had an increased posterior LBS and LMS and a lower MMH and LMH.

scholarly journals Radiomics Features Predict Telomerase Reverse Transcriptase Promoter Mutations in World Health Organization Grade II Gliomas via a Machine-Learning Approach

2021 ◽  
Vol 10 ◽  
Author(s):  
Shengyu Fang ◽  
Ziwen Fan ◽  
Zhiyan Sun ◽  
Yiming Li ◽  
Xing Liu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cross Validation ◽  
Learning Algorithm ◽  
World Health ◽  
Machine Learning Algorithm ◽  
Who Grade ◽  
Grade Ii ◽  
Health Organization ◽  
Who Grade Ii Gliomas ◽  
Fold Cross Validation

The detection of mutations in telomerase reverse transcriptase promoter (pTERT) is important since preoperative diagnosis of pTERT status helps with evaluating prognosis and determining the surgical strategy. Here, we aimed to establish a radiomics-based machine-learning algorithm and evaluated its performance with regard to the prediction of mutations in pTERT in patients with World Health Organization (WHO) grade II gliomas. In total, 164 patients with WHO grade II gliomas were enrolled in this retrospective study. We extracted a total of 1,293 radiomics features from multi-parametric magnetic resonance imaging scans. Elastic net (used for feature selection) and support vector machine with linear kernel were applied in nested 10-fold cross-validation loops. The predictive model was evaluated by receiver operating characteristic and precision-recall analyses. We performed an unpaired t-test to compare the posterior predictive probabilities among patients with differing pTERT statuses. We selected 12 valuable radiomics features using nested 10-fold cross-validation loops. The area under the curve (AUC) was 0.8446 (95% confidence interval [CI], 0.7735–0.9065) with an optimal summed value of sensitivity of 0.9355 (95% CI, 0.8802–0.9788) and specificity of 0.6197 (95% CI, 0.5071–0.7371). The overall accuracy was 0.7988 (95% CI, 0.7378–0.8598). The F1-score was 0.8406 (95% CI, 0.7684–0.902) with an optimal precision of 0.7632 (95% CI, 0.6818–0.8364) and recall of 0.9355 (95% CI, 0.8802–0.9788). Posterior probabilities of pTERT mutations were significantly different between patients with wild-type and mutant TERT promoters. Our findings suggest that a radiomics analysis with a machine-learning algorithm can be useful for predicting pTERT status in patients with WHO grade II glioma and may aid in glioma management.

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