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Information from dynamic length changes improves reliability of static ultrasound fascicle length measurements.

ABSTRACT: Various strategies for improving reliability of fascicle identification on ultrasound images are used in practice, yet these strategies are untested for effectiveness. Studies suggest that the largest part of differences between fascicle lengths on one image are attributed to the error on the initial image. In this study, we compared reliability results between different strategies.Static single-image recordings and image sequence recordings during passive ankle rotations of the medial gastrocnemius were collected. Images were tracked by three different raters. We compared results from uninformed fascicle identification (UFI) and results with information from dynamic length changes, or data-informed tracking (DIT). A second test compared tracking of image sequences of either fascicle shortening (initial-long condition) or fascicle lengthening (initial-short condition).Intra-class correlations (ICC) were higher for the DIT compared to the UFI, yet yielded similar standard error of measurement (SEM) values. Between the initial-long and initial-short conditions, similar ICC values, coefficients of multiple determination, mean squared errors, offset-corrected mean squared errors and fascicle length change values were found for the DIT, yet with higher SEM values and greater absolute fascicle length differences between raters on the first image in the initial-long condition and on the final image in the initial-short condition.DIT improves reliability of fascicle length measurements, without lower SEM values. Fascicle length on the initial image has no effect on subsequent tracking results. Fascicles on ultrasound images should be identified by a single rater and care should be taken when comparing absolute fascicle lengths between studies.

SUBMITTER: Aeles J

PROVIDER: S-EPMC5733898 | biostudies-other | 2017

REPOSITORIES: biostudies-other

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Information from dynamic length changes improves reliability of static ultrasound fascicle length measurements.

Aeles Jeroen J Lichtwark Glen A GA Lenchant Sietske S Vanlommel Liesbeth L Delabastita Tijs T Vanwanseele Benedicte B

PeerJ 20171215

<h4>Purpose</h4>Various strategies for improving reliability of fascicle identification on ultrasound images are used in practice, yet these strategies are untested for effectiveness. Studies suggest that the largest part of differences between fascicle lengths on one image are attributed to the error on the initial image. In this study, we compared reliability results between different strategies.<h4>Methods</h4>Static single-image recordings and image sequence recordings during passive ankle r ...[more]

PMID: 29259845

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Project description:Background and objectiveDynamic muscle fascicle length measurements through B-mode ultrasound have become popular for the non-invasive physiological insights they provide regarding musculoskeletal structure-function. However, current practices typically require time consuming post-processing to track muscle length changes from B-mode images. A real-time measurement tool would not only save processing time but would also help pave the way toward closed-loop applications based on feedback signals driven by in vivo muscle length change patterns. In this paper, we benchmark an approach that combines traditional machine learning (ML) models with B-mode ultrasound recordings to obtain muscle fascicle length changes in real-time. To gauge the utility of this framework for 'in-the-loop' applications, we evaluate accuracy of the extracted muscle length change signals against time-series' derived from a standard, post-hoc automated tracking algorithm.MethodsWe collected B-mode ultrasound data from the soleus muscle of six participants performing five defined ankle motion tasks: (a) seated, constrained ankle plantarflexion, (b) seated, free ankle dorsi/plantarflexion, (c) weight-bearing, calf raises (d) walking, and then a (e) mix. We trained machine learning (ML) models by pairing muscle fascicle lengths obtained from standardized automated tracking software (UltraTrack) with the respective B-mode ultrasound image input to the tracker, frame-by-frame. Then we conducted hyperparameter optimizations for five different ML models using a grid search to find the best performing parameters for a combination of high correlation and low RMSE between ML and UltraTrack processed muscle fascicle length trajectories. Finally, using the global best model/hyperparameter settings, we comprehensively evaluated training-testing outcomes within subject (i.e., train and test on same subject), cross subject (i.e., train on one subject, test on another) and within/direct cross task (i.e., train and test on same subject, but different task).ResultsSupport vector machine (SVM) was the best performing model with an average r = 0.70 ±0.34 and average RMSE = 2.86 ±2.55 mm across all direct training conditions and average r = 0.65 ±0.35 and average RMSE = 3.28 ±2.64 mm when optimized for all cross-participant conditions. Comparisons between ML vs. UltraTrack (i.e., ground truth) tracked muscle fascicle length versus time data indicated that ML tracked images reliably capture the salient qualitative features in ground truth length change data, even when correlation values are on the lower end. Furthermore, in the direct training, calf raises condition, which is most comparable to previous studies validating automated tracking performance during isolated contractions on a dynamometer, our ML approach yielded 0.90 average correlation, in line with other accepted tracking methods in the field.ConclusionsBy combining B-mode ultrasound and classical ML models, we demonstrate it is possible to achieve real-time tracking of human soleus muscle fascicles across a number of functionally relevant contractile conditions. This novel sensing modality paves the way for muscle physiology in-the-loop applications that could be used to modify gait via biofeedback or unlock novel wearable device control techniques that could enable restored or augmented locomotion performance.

Project description:BackgroundBedside monitors in the ICU routinely measure and collect patients' physiologic data in real time to continuously assess the health status of patients who are critically ill. With the advent of increased computational power and the ability to store and rapidly process big data sets in recent years, these physiologic data show promise in identifying specific outcomes and/or events during patients' ICU hospitalization.MethodsWe introduced a methodology designed to automatically extract information from continuous-in-time vital sign data collected from bedside monitors to predict if a patient will experience a prolonged stay (length of stay) on mechanical ventilation, defined as >4 d, in a pediatric ICU.ResultsContinuous-in-time vital signs information and clinical history data were retrospectively collected for 284 ICU subjects from their first 24 h on mechanical ventilation from a medical-surgical pediatric ICU at Boston Children's Hospital. Multiple machine learning models were trained on multiple subsets of these subjects to predict the likelihood that each of these subjects would experience a long stay. We evaluated the predictive power of our models strictly on unseen hold-out validation sets of subjects. Our methodology achieved model performance of >83% (area under the curve) by using only vital sign information as input, and performances of 90% (area under the curve) by combining vital sign information with subjects' static clinical data readily available in electronic health records. We implemented this approach on 300 independently trained experiments with different choices of training and hold-out validation sets to ensure the consistency and robustness of our results in our study sample. The predictive power of our approach outperformed recent efforts that used deep learning to predict a similar task.ConclusionsOur proposed workflow may prove useful in the design of scalable approaches for real-time predictive systems in ICU environments, exploiting real-time vital sign information from bedside monitors. (ClinicalTrials.gov registration NCT02184208.).

Project description:BackgroundRehabilitation is the key to management of patients with subacromial impingement syndrome to prevent disability and loss of function. While point-of-care musculoskeletal ultrasound aids clinical diagnosis of subacromial impingement syndrome, many patients do not demonstrate the classic findings of dynamic supraspinatus tendon impingement beneath the acromion on ultrasound. The objective of this study was to establish the most reliable shoulder ultrasound measurements for subacromial impingement, by evaluating the intra-rater and inter-rater reliability of measurements in asymptomatic participants.MethodsEighteen participants (9 women, 9 men, mean ± standard deviation: 34.6 ± 7.9 years of age) underwent bilateral shoulder ultrasound evaluations with measurements for subacromial impingement (acromiohumeral distance, acromion-greater tuberosity distance, supraspinatus tendon, subacromial-subdeltoid bursa, and subacromial-subdeltoid bursal fluid thickness) performed by two sports medicine physicians. Intra-class coefficients were calculated to determine the intra- and inter-rater reliability of shoulder ultrasound images and measurements.ResultsIntra-rater reliability for acromiohumeral distance (0.76-0.79), supraspinatus tendon (0.91-0.95), subacromial-subdeltoid bursa (0.76-0.84), and subacromial-subdeltoid bursal fluid thickness (0.75-0.81) was found to be good to excellent, whereas inter-rater reliability ranged from poor to moderate.ConclusionsAcromiohumeral distance in neutral position and short axis ultrasound measurements of supraspinatus tendon, subacromial-subdeltoid bursa, and subacromial-subdeltoid bursal fluid thickness in the modified Crass position were the most reliable for subacromial impingement in asymptomatic participants. We recommend validation of these measurements in a symptomatic population to aid diagnosis and direct rehabilitation of patients with suspected subacromial impingement, and to increase point-of-care ultrasound uptake, availability, and training among rehabilitation professionals across health systems.

Project description:BackgroundAge-related changes in muscle properties affect daily functioning, therefore a reliable assessment of such properties is required. We examined the effects of age on reliability, muscle quality and interrelation among muscle architecture (MA) parameters of the gastrocnemius medialis (GM), tibialis anterior (TA), and vastus lateralis (VL) muscles.MethodsThree raters scored ultrasound (US) scans of 12 healthy younger and older adults, on fascicle length (FL), pennation angle (PA) and muscle thickness (MT). Intra- and inter-rater reliability of MA measures in rest and contraction was assessed by intraclass correlation coefficients (ICC) and standard error of measurements (SEM, SEM%). The relationship between MA parameters was examined using Pearson correlation coefficients. Muscle quality (MQ) was examined using mean pixel intensity.ResultsReliability was moderate to excellent for TA in both groups (ICCs: 0.64-0.99, SEM% = 1.6-14.8%), and for VL in the younger group (ICCs: 0.67-0.98, SEM% = 2.0-18.3%). VL reliability was poor to excellent in older adults (ICCs: 0.22-0.99, SEM% = 2.7-36.0%). For GM, ICCs were good to excellent (ICCs: 0.76-0.99) in both groups, but GM SEM% were higher in older adults (SEM%Younger = 1.5-10.7%, SEM%Older = 1.6-28.1%). Muscle quality was on average 19.0% lower in older vs. younger adults. In both groups, moderate to strong correlations were found for VL FL and MT (r ≥ 0.54), and TA PA and MT (r ≥ 0.72), while TA FL correlated with MT (r ≥ 0.67) in younger adults only.ConclusionsIn conclusion, age- and muscle-specificities were present in the relationships between MT and PA, and MT and FL at rest. Furthermore, the reliability of MA parameters assessed with 2D panoramic US is acceptable. However, the level of reliability varies with age, muscle and MA measure. In older adults notably, the lowest reliability was observed in the VL muscle. Among the MA parameters, MT appears to be the simplest and most easily reproducible parameter in all muscles and age groups.

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Information from dynamic length changes improves reliability of static ultrasound fascicle length measurements.

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Information from dynamic length changes improves reliability of static ultrasound fascicle length measurements.

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