Dataset Information

Importance of Consistent Datasets in Musculoskeletal Modelling: A Study of the Hand and Wrist.

ABSTRACT: Hand musculoskeletal models provide a valuable insight into the loads withstood by the upper limb; however, their development remains challenging because there are few datasets describing both the musculoskeletal geometry and muscle morphology from the elbow to the finger tips. Clinical imaging, optical motion capture and microscopy were used to create a dataset from a single specimen. Subsequently, a musculoskeletal model of the wrist was developed based on these data to estimate muscle tensions and to demonstrate the potential of the provided parameters. Tendon excursions and moment arms predicted by this model were in agreement with previously reported experimental data. When simulating a flexion-extension motion, muscle forces reached 90 N among extensors and a co-contraction of flexors, amounting to 62.6 N, was estimated by the model. Two alternative musculoskeletal models were also created based on anatomical data available in the literature to illustrate the effect of combining incomplete datasets. Compared to the initial model, the intensities and load sharing of the muscles estimated by the two alternative models differed by up to 180% for a single muscle. This confirms the importance of using a single source of anatomical data when developing such models.

SUBMITTER: Goislard De Monsabert B

PROVIDER: S-EPMC5754461 | biostudies-literature | 2018 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Importance of Consistent Datasets in Musculoskeletal Modelling: A Study of the Hand and Wrist.

Goislard De Monsabert Benjamin B Edwards Dafydd D Shah Darshan D Kedgley Angela A

Annals of biomedical engineering 20171002 1

Hand musculoskeletal models provide a valuable insight into the loads withstood by the upper limb; however, their development remains challenging because there are few datasets describing both the musculoskeletal geometry and muscle morphology from the elbow to the finger tips. Clinical imaging, optical motion capture and microscopy were used to create a dataset from a single specimen. Subsequently, a musculoskeletal model of the wrist was developed based on these data to estimate muscle tension ...[more]

PMID: 28971327

Similar Datasets

Project description:ObjectivesTo assess how an artificial intelligence (AI) algorithm performs against five experienced musculoskeletal radiologists in diagnosing scaphoid fractures and whether it aids their diagnosis on conventional multi-view radiographs.MethodsFour datasets of conventional hand, wrist, and scaphoid radiographs were retrospectively acquired at two hospitals (hospitals A and B). Dataset 1 (12,990 radiographs from 3353 patients, hospital A) and dataset 2 (1117 radiographs from 394 patients, hospital B) were used for training and testing a scaphoid localization and laterality classification component. Dataset 3 (4316 radiographs from 840 patients, hospital A) and dataset 4 (688 radiographs from 209 patients, hospital B) were used for training and testing the fracture detector. The algorithm was compared with the radiologists in an observer study. Evaluation metrics included sensitivity, specificity, positive predictive value (PPV), area under the characteristic operating curve (AUC), Cohen's kappa coefficient (κ), fracture localization precision, and reading time.ResultsThe algorithm detected scaphoid fractures with a sensitivity of 72%, specificity of 93%, PPV of 81%, and AUC of 0.88. The AUC of the algorithm did not differ from each radiologist (0.87 [radiologists' mean], p ≥ .05). AI assistance improved five out of ten pairs of inter-observer Cohen's κ agreements (p < .05) and reduced reading time in four radiologists (p < .001), but did not improve other metrics in the majority of radiologists (p ≥ .05).ConclusionsThe AI algorithm detects scaphoid fractures on conventional multi-view radiographs at the level of five experienced musculoskeletal radiologists and could significantly shorten their reading time.Key points• An artificial intelligence algorithm automatically detects scaphoid fractures on conventional multi-view radiographs at the same level of five experienced musculoskeletal radiologists. • There is preliminary evidence that automated scaphoid fracture detection can significantly shorten the reading time of musculoskeletal radiologists.

Project description:IntroductionWrist and hand represent the third most common body part in work-related injuries, being associated with long-term absenteeism. Telerehabilitation can promote access to treatment, patient adherence, and engagement, while reducing health care-related costs.ObjectiveReport the results of a fully remote digital care program (DCP) for wrist and hand pain (WP).MethodsA single-arm interventional study was conducted on individuals with WP applying for a DCP. Primary outcome was the mean change in the Numerical Pain Rating Scale after 8 weeks (considering a minimum clinically important change of 30%). Secondary outcomes were: disability (Quick Disabilities of the Arm, Shoulder, and Hand questionnaire), analgesic intake, surgery intention, mental health (patient health questionnaire [PHQ-9] and generalized anxiety disorder [GAD-7]), fear-avoidance beliefs (FABQ-PA), work productivity and activity impairment, and engagement.ResultsFrom 189 individuals starting the DCP, 149 (78.8%) completed the intervention. A significant pain improvement was observed (51.3% reduction (2.26, 95% CI 1.73; 2.78)) and 70.4% of participants surpassing minimum clinically important change. This change correlated with improvements in disability (52.1%), FABQ-PA (32.2%), and activities impairment recovery (65.4%). Improvements were also observed in other domains: surgery intent (76.1%), mental health (67.0% in anxiety and 72.7% in depression), and overall productivity losses (68.2%). Analgesic intake decreased from 22.5% to 7.1%. Mean patient satisfaction score was 8.5/10.0 (SD 1.8).ConclusionsThese findings support the feasibility and utility of a fully remote DCP for patients with WP. Clinically significant improvements were observed in all health-related and productivity-related outcomes, alongside very high patient adherence rates and satisfaction. This study strengthens that management of WP is possible through a remote DCP, decreasing access barriers and potentially easing health care expenditure.

Project description:BackgroundFew prospective cohort studies of distal upper extremity musculoskeletal disorders have been performed. Past studies have provided somewhat conflicting evidence for occupational risk factors and have largely reported data without adjustments for many personal and psychosocial factors.Methods/designA multi-center prospective cohort study was incepted to quantify risk factors for distal upper extremity musculoskeletal disorders and potentially develop improved methods for analyzing jobs. Disorders to analyze included carpal tunnel syndrome, lateral epicondylalgia, medial epicondylalgia, trigger digit, deQuervain's stenosing tenosynovitis and other tendinoses. Workers have thus far been enrolled from 17 different employment settings in 3 diverse US states and performed widely varying work. At baseline, workers undergo laptop administered questionnaires, structured interviews, two standardized physical examinations and nerve conduction studies to ascertain demographic, medical history, psychosocial factors and current musculoskeletal disorders. All workers' jobs are individually measured for physical factors and are videotaped. Workers are followed monthly for the development of musculoskeletal disorders. Repeat nerve conduction studies are performed for those with symptoms of tingling and numbness in the prior six months. Changes in jobs necessitate re-measure and re-videotaping of job physical factors. Case definitions have been established. Point prevalence of carpal tunnel syndrome is a combination of paraesthesias in at least two median nerve-served digits plus an abnormal nerve conduction study at baseline. The lifetime cumulative incidence of carpal tunnel syndrome will also include those with a past history of carpal tunnel syndrome. Incident cases will exclude those with either a past history or prevalent cases at baseline. Statistical methods planned include survival analyses and logistic regression.DiscussionA prospective cohort study of distal upper extremity musculoskeletal disorders is underway and has successfully enrolled over 1,000 workers to date.

Dataset Information

Importance of Consistent Datasets in Musculoskeletal Modelling: A Study of the Hand and Wrist.

Publications

Importance of Consistent Datasets in Musculoskeletal Modelling: A Study of the Hand and Wrist.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets