scholarly journals Intelligent localization and quantitative evaluation of anterior talofibular ligament injury using magnetic resonance imaging of ankle

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wen Yan ◽  
Xianghong Meng ◽  
Jinglai Sun ◽  
Hui Yu ◽  
Zhi Wang
Keyword(s):  
Ankle Injuries ◽  
Ligament Injury ◽  
Lateral Ligament ◽  
Support Vector ◽  
Anterior Talofibular Ligament ◽  
Good Prediction ◽  
Feature Points ◽  
Characteristic Analysis ◽  
Prediction Ability ◽  
Mean Values

Abstract Background There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability. Methods According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury. Results Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set. Conclusion Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury.

scholarly journals Intelligent Localization and Quantitative Evaluation of Anterior Talofibular Ligament Injury using Magnetic Resonance Imaging of Ankle

2021 ◽  
Author(s):  
Wen Yan ◽  
Xianghong Meng ◽  
Jinglai Sun ◽  
Hui Yu ◽  
Zhi Wang
Keyword(s):  
Ankle Injuries ◽  
Ligament Injury ◽  
Lateral Ligament ◽  
Support Vector ◽  
Anterior Talofibular Ligament ◽  
Good Prediction ◽  
Feature Points ◽  
Characteristic Analysis ◽  
Prediction Ability ◽  
Mean Values

Abstract Background: There is a high incidence of injury to the lateral ligament of the ankle in daily living and sports activities. The anterior talofibular ligament (ATFL) is the most frequent types of ankle injuries. It is of great clinical significance to achieve intelligent localization and injury evaluation of ATFL due to its vulnerability.Methods: According to the specific characteristics of bones in different slices, the key slice was extracted by image segmentation and characteristic analysis. Then, the talus and fibula in the key slice were segmented by distance regularized level set evolution (DRLSE), and the curvature of their contour pixels was calculated to find useful feature points including the neck of talus, the inner edge of fibula, and the outer edge of fibula. ATFL area can be located using these feature points so as to quantify its first-order gray features and second-order texture features. Support vector machine (SVM) was performed for evaluation of ATFL injury.Results: Data were collected retrospectively from 158 patients who underwent MRI, and were divided into normal (68) and tear (90) group. The positioning accuracy and Dice coefficient were used to measure the performance of ATFL localization, and the mean values are 87.7% and 77.1%, respectively, which is helpful for the following feature extraction. SVM gave a good prediction ability with accuracy of 93.8%, sensitivity of 88.9%, specificity of 100%, precision of 100%, and F1 score of 94.2% in the test set.Conclusion: Experimental results indicate that the proposed method is reliable in diagnosing ATFL injury. This study may provide a potentially viable method for aided clinical diagnoses of some ligament injury.

The Role of Calcaneofibular Ligament Injury in Ankle Instability: Implications for Surgical Management

2018 ◽  
Vol 47 (2) ◽  
pp. 431-437 ◽  
Author(s):  
Kenneth J. Hunt ◽  
Helder Pereira ◽  
Judas Kelley ◽  
Nicholas Anderson ◽  
Richard Fuld ◽  
...  
Keyword(s):  
Contact Mechanics ◽  
Ankle Joint ◽  
Contact Area ◽  
Ankle Instability ◽  
Peak Torque ◽  
Ligament Injury ◽  
Lateral Ligament ◽  
Anterior Talofibular Ligament ◽  
Ankle Sprains ◽  
Calcaneofibular Ligament

Background: Acute inversion ankle sprains are among the most common musculoskeletal injuries. Higher grade sprains, including anterior talofibular ligament (ATFL) and calcaneofibular ligament (CFL) injury, can be particularly challenging. The precise effect of CFL injury on ankle instability is unclear. Hypothesis: CFL injury will result in decreased stiffness, decreased peak torque, and increased talar and calcaneal motion and will alter ankle contact mechanics when compared with the uninjured ankle and the ATFL-only injured ankle in a cadaveric model. Study Design: Descriptive laboratory study. Methods: Ten matched pairs of cadaver specimens with a pressure sensor in the ankle joint and motion trackers on the fibula, talus, and calcaneus were mounted on a material testing system with 20° of ankle plantarflexion and 15° of internal rotation. Intact specimens were axially loaded to body weight and then underwent inversion along the anatomic axis of the ankle from 0° to 20°. The ATFL and CFL were sequentially sectioned and underwent inversion testing for each condition. Linear mixed models were used to determine significance for stiffness, peak torque, peak pressure, contact area, and inversion angles of the talus and calcaneus relative to the fibula across the 3 conditions. Results: Stiffness and peak torque did not significantly decrease after sectioning of the ATFL but decreased significantly after sectioning of the CFL. Peak pressures in the tibiotalar joint decreased and mean contact area increased significantly after CFL release. Significantly more inversion of the talus and calcaneus as well as calcaneal medial displacement was seen with weightbearing inversion after sectioning of the CFL. Conclusion: The CFL contributes considerably to lateral ankle instability. Higher grade sprains that include CFL injury result in significant decreases in rotation stiffness and peak torque, substantial alteration of contact mechanics at the ankle joint, increased inversion of the talus and calcaneus, and increased medial displacement of the calcaneus. Clinical Relevance: Repair of an injured CFL should be considered during lateral ligament reconstruction, and there may be a role for early repair in high-grade injuries to avoid intermediate and long-term consequences of a loose or incompetent CFL.

Anterior Drawer Test for Acute Anterior Talofibular Ligament Injuries of the Ankle: How Much Load Should be Applied during the Test?

2003 ◽  
Vol 31 (2) ◽  
pp. 226-232 ◽  
Author(s):  
Harukazu Tohyama ◽  
Kazunori Yasuda ◽  
Yasumitu Ohkoshi ◽  
Bruce D. Beynnon ◽  
Per A. Renstrom
Keyword(s):  
Cadaver Study ◽  
Ankle Injuries ◽  
Ligament Injury ◽  
Normal Displacement ◽  
Anterior Talofibular Ligament ◽  
Anterior Drawer ◽  
Anterior Drawer Test ◽  
Ankle Ligament ◽  
Anterior Posterior

Background: There is a lack of consensus regarding the magnitude of load for performing the anterior drawer test in evaluating acute ankle injuries. Purpose: To determine how much load should be applied during the anterior drawer test to detect the integrity of the anterior talofibular ligament. Methods: First, the anterior-posterior load-displacement response of nine cadaveric ankles was measured. Second, anterior displacement of the ankle was measured at 30 and 60 N of anterior load in 14 patients with acute tears of the anterior talofibular ligament. Results: In the cadaver study, the increased displacement by sectioning of the ligament measured at 10, 20, 30, and 40 N of anterior load were significantly greater than those measured at 60 N. In vivo examination of the subjects without anesthesia demonstrated that the injured-to-normal displacement value at 30 N of anterior load was significantly greater than the value at 60 N. Conclusions: This study suggests that a large magnitude of anterior load is not necessary to detect the integrity of the ligament during the anterior drawer test. Clinical Relevance: When evaluating the integrity of the anterior talofibular ligament in cases of acute ankle ligament injury, a relatively low-magnitude load should be applied.

scholarly journals Weber B Fracture of the Lateral Malleolus with Concomitant Anterior Talofibular Ligament Injury following an Ankle Supination Injury

2016 ◽  
Vol 2016 ◽  
pp. 1-5
Author(s):  
Mohammed Khalid Faqi ◽  
Abdulla AlJawder ◽  
Fahad Alkhalifa ◽  
Ali H. Almajed
Keyword(s):  
Clinical Practice ◽  
Critical Review ◽  
Ankle Instability ◽  
Lateral Malleolus ◽  
Ankle Injuries ◽  
Ligament Injury ◽  
Anterior Talofibular Ligament ◽  
Prediction System ◽  
Mechanism Of Injury ◽  
Ligament Tear

The Lauge-Hansen (LH) classification attempts to predict patterns of ankle injuries based upon the preceding mechanism of injury. Although it is widely used in clinical practice, it has been criticized mainly due to numerous reports of cases conflicting the prediction system. Here, we report a case of a 32-year-old male who sustained a Weber B fracture of the lateral malleolus following a supination ankle injury, which was treated conservatively, following which the patient presented with ankle instability and was found to have concurrent anterior talofibular ligament tear. Critical review of the LH classification along with its shortcomings is discussed.

scholarly journals Predict Arrival Time by Using Machine Learning Algorithm to Promote Utilization of Urban Smart Bus

2020 ◽  
Author(s):  
Rafidah Md Noor ◽  
Ng Seong Yik ◽  
Raenu Kolandaisamy ◽  
Ismail Ahmedy ◽  
Mohammad Asif Hossain ◽  
...  
Keyword(s):  
Arrival Time ◽  
Research Work ◽  
Experimental Result ◽  
Weather Data ◽  
Support Vector ◽  
Good Prediction ◽  
Prediction Ability ◽  
Urban City ◽  
Original Dataset ◽  
The Impact

The impact of the accurate estimated time of arrival (ETA) is often overlooked by bus operators. By providing accurate ETA to riders, it gives them the impression of bus services is efficient and reliable and this promotes higher ridership in the long run. This research project aims to predict bus arrival time by using the Support Vector Regression (SVR) model which is based on the same theory as the Support Vector Machine (SVM). Urban City Bus data covering part of the Petaling Jaya area (route name PJ03) is used in this research work. Features related to traffic such as travel duration, a distance of the road, weather and operation at peak or non-peak hour have been used as input in the training of the SVR model. By using kernel trick and specifying optimum parameters, all the features in higher dimensions are efficiently calculated and the SVR model achieves convergence. The model is evaluated with the test set of data split from the original dataset. The experimental result indicates the SVR model displays good prediction ability with its low average error on the prediction result. However, weather data has not been influential to the prediction model as the results of the model trained with and without weather data show a negligible difference.

scholarly journals Assessment of Hellwig Method for Predictors’ Selection in Groundwater Level Time Series Forecasting

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 778
Author(s):  
Joanna Kajewska-Szkudlarek ◽  
Wojciech Łyczko
Keyword(s):  
Time Series ◽  
Groundwater Level ◽  
Mean Squared Error ◽  
Measurement Data ◽  
Treatment Plant ◽  
Water Volume ◽  
Support Vector ◽  
Complex Nature ◽  
Good Prediction ◽  
Prediction Ability

Effective groundwater planning and management should be based on the prediction of available water volume. The complex nature of groundwater systems makes this complicated and requires the use of complex methods. Data-driven models using computational intelligence are becoming increasingly popular in that field. The key issue in predictive modelling is the selection of input variables. Wrocław-Osobowice irrigation fields were a wastewater treatment plant until 2013. The monitoring of groundwater levels is being continued to assess the water relations in that area after the end of their exploitation. The aim of the study was to assess the Hellwig method for predictors’ selection in groundwater level forecasting with support vector regression models. Data covered the daily time series of groundwater level in the period 2015–2019. Obtained models with a root mean squared error (RMSE) of 0.024–0.292 m and r2 of 0.7–0.9 were considered as high quality. Moreover, they showed good prediction ability for high as well as low groundwater values. Additionally, the proposed method is simple, and its implementation only requires access to groundwater level measurement data. It may be useful in groundwater management and planning in terms of actual climate change and threat of water deficits.

Intraoperative Findings of Lateral Ligament Avulsion Fractures and Outcome After Refixation to the Fibula

2018 ◽  
Vol 39 (6) ◽  
pp. 669-673 ◽  
Author(s):  
Jasmin Diallo ◽  
Joe Wagener ◽  
Christine Schweizer ◽  
Tamara Horn Lang ◽  
Roxa Ruiz ◽  
...  
Keyword(s):  
Case Series ◽  
Ligament Injury ◽  
Lateral Ligament ◽  
Rotational Instability ◽  
Anterior Talofibular Ligament ◽  
Calcaneofibular Ligament ◽  
Level Of Evidence ◽  
Avulsion Fractures ◽  
Bony Avulsion ◽  
Lateral Ankle Ligaments

Background: An acute ankle sprain can result in a bony avulsion of the lateral ankle ligaments. The extent of concomitant lesions and subsequent instability patterns are not clearly understood. The high incidence of old avulsion fractures found in symptomatic chronic ankle instability may indicate the need for primary fixation. Methods: Ten patients (5 male, 5 female; mean age, 34 years; range, 15–64 years) with bony avulsion fractures of the fibula after acute ankle sprains were included. All patients were treated after a mean of 7.7 days (range, 2–17 days) with screw fixation of the fragment to the fibula. Clinical and radiographic assessments were performed, and the extent of ligament injury was documented. Results: The anterior talofibular ligament and calcaneofibular ligament were found to be attached to the avulsion fragment. The average size was 6.3 mm (range, 4–9 mm) in width from anterior to posterior and 5.2 mm (range, 4–7 mm) in length from superior to inferior. The displacement of the fragment increased under varus stress. Two patients had cartilage lesions in the lateral talus. After a mean follow-up period of 2.4 years (range, 2–4 years), all were clinically and radiographically stable, and patients were satisfied. Conclusions: It was observed arthroscopically that the anterior talofibular ligament and calcaneofibular ligament were attached to the fragment. This is critical because motion between the fragment and the fibula may prevent spontaneous healing. Primary fixation of the fragment prevented rotational instability and was efficient to restore function and stability. Level of Evidence: Level IV, retrospective case series.

A comprehensive study on statistical prediction and reduction of tire/road noise

2021 ◽  
pp. 107754632110131
Author(s):  
Somaye Mohammadi ◽  
Abdolreza Ohadi ◽  
Mostafa Irannejad-Parizi
Keyword(s):  
Neural Network ◽  
Road Transport ◽  
Relevance Vector Machine ◽  
Statistical Investigation ◽  
Statistical Prediction ◽  
Support Vector ◽  
Prediction Ability ◽  
Vector Method ◽  
Tire Noise ◽  
Rolling Noise

Promoting safe tires with low external rolling noise increases the environmental efficiency of road transport. Although tire builders have been striving to reduce emitted noise, the issue’s sophisticated nature has made it difficult. This article aims to make the problem straightforward, relying on recent significant improvements in statistical science. In this regard, the prediction ability of new methods in this field, including support vector machine, relevance vector machine, and convolutional neural network, along with the new architecture of the neural network is compared. Tire noise is measured under the coast-by condition. Two training strategies are proposed: extracting features from a tread pattern image and directly importing an image to the model. The relevance vector method, which is trained using the first strategy, has provided the most accurate results with an error of 0.62 dB(A) in predicting the total noise level. This precise model is used instead of experimentation to analyze the sensitivity of tire noise to its parameters using a small central composite design. The parametric study reveals striking tips for reducing noise, especially in terms of interactions between parameters that have not previously been shown. Finally, a novel two-stage approach for reducing noise by tread pattern optimization is proposed, inspired by two regression models derived from statistical investigation and variance analysis. Changes in tread pattern specifications of two case studies and their randomization have resulted in a reduction of 3.2 dB(A) for a high-noise tire and 0.4 dB(A) decrement for a quieter tire.

scholarly journals Study on Quality Prediction of 2219 Aluminum Alloy Friction Stir Welding Based on Real-Time Temperature Signal

Materials ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3496
Author(s):  
Haijun Wang ◽  
Diqiu He ◽  
Mingjian Liao ◽  
Peng Liu ◽  
Ruilin Lai
Keyword(s):  
Aluminum Alloy ◽  
Friction Stir Welding ◽  
Prediction Model ◽  
Real Time ◽  
Support Vector ◽  
Weld Quality ◽  
Prediction Ability ◽  
Friction Stir ◽  
Temperature Signal ◽  
2219 Aluminum Alloy

The online prediction of friction stir welding quality is an important part of intelligent welding. In this paper, a new method for the online evaluation of weld quality is proposed, which takes the real-time temperature signal as the main research variable. We conducted a welding experiment with 2219 aluminum alloy of 6 mm thickness. The temperature signal is decomposed into components of different frequency bands by wavelet packet method and the energy of component signals is used as the characteristic parameter to evaluate the weld quality. A prediction model of weld performance based on least squares support vector machine and genetic algorithm was established. The experimental results showed that, when welding defects are caused by a sudden perturbation during welding, the amplitude of the temperature signal near the tool rotation frequency will change significantly. When improper process parameters are used, the frequency band component of the temperature signal in the range of 0~11 Hz increases significantly, and the statistical mean value of the temperature signal will also be different. The accuracy of the prediction model reached 90.6%, and the AUC value was 0.939, which reflects the good prediction ability of the model.

Reverse Anterolateral Drawer Test schneidet am besten ab

2021 ◽  
Vol 25 (01) ◽  
pp. 5-5
Author(s):  
Arne Vielitz
Keyword(s):  
Ligament Injury ◽  
Anterior Talofibular Ligament

Li Q, Tu Y, Chen J et al. Reverse Anterolateral Drawer Test Is More Sensitive and Accurate for Diagnosing Chronic Anterior Talofibular Ligament Injury. Knee Surg Sports Traumatol Arthrosc 2020; 28: 55–62. doi: 10.1007/s00167-019-05705-x

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