Objective To evaluate the efficacy and safety of mesenchymal stem cells (MSCs) therapy in patients with tendon disorders enrolled in prospective clinical studies.Methods We systematically searched prospective clinical studies that investigated the effects of MSC administration on human tendon disorders with at least a 6-month follow-up period in the PubMed-MEDLINE, EMBASE, and Cochrane Library databases. The primary outcome of interest was the change in pain on motion related to tendon disorders. Meta-regression analyses were performed to assess the relationship between MSC dose and pooled effect sizes in each cell dose.Results Four prospective clinical trials that investigated the effect of MSCs on tendon disorders were retrieved. MSCs showed a significant pooled effect size (overall Hedges’ g pooled standardized mean difference=1.868; 95% confidence interval, 1.274–2.462; p<0.001). The treatment with MSCs improved all the aspects analyzed, namely pain, functional scores, radiological parameters (magnetic resonance image or ultrasonography), and arthroscopic findings. In the meta-regression analysis, a significant cell dose-dependent response in pain relief (Q=9.06, p=0.029) was observed.Conclusion Our meta-analysis revealed that MSC therapy may improve pain, function, radiological, and arthroscopic parameters in patients with tendon disorders. A strong need for large-scale randomized controlled trials has emerged to confirm the long-term functional improvement and adverse effects of MSC therapies in tendon disorders.
Electromyography is a valuable diagnostic procedure for diagnosing patients with neuromuscular diseases; however, it has some drawbacks. First, diagnosis using electromyography is subjective, and in some cases, there is the potential for inter-individual discrepancies. Second, it is a time- and effort-intensive process that requires expertise to yield accurate results. Recently, a deep learning algorithm shows effectiveness for the analysis of waveform data such as electrocardiography. To overcome limitations of electromyography, we developed a deep learning-based electromyography classification system and compared the performance of our deep learning model with that of six physicians. This study included 58 subjects who underwent electromyography and were finally confirmed as having myopathy or neuropathy, or to be in a normal state between June 2015 and July 2020 at Seoul National University Hospital. We developed a one-dimensional convolutional neural network algorithm and divide-and-vote system for diagnosing subjects. Diagnosis results with our deep learning model were compared with those of six physicians with experience in performing and interpreting electromyography. The accuracy, sensitivity, specificity, and positive predictive value of the deep learning model for diagnosis as to whether subjects have myopathy or neuropathy or normal were 0.875, 0.820, 0.904, and 0.820, respectively, whereas those for the physicians were 0.694, 0.537, 0.773, and 0.524, respectively. The area under the receiver operating characteristic curves of the deep learning model for predicting myopathy, neuropathy, and normal states was better than the averaged results of six physicians. Our study showed that deep learning could play a key role in reading electromyography and diagnosing patients with neuromuscular diseases. In the future, large prospective cohort studies incorporating diverse neuromuscular diseases can enable deep learning-based electrodiagnosis on behalf of physicians.
Electromyography is a valuable diagnostic tool for diagnosing patients with neuromuscular diseases; however, it has possible drawbacks including diagnostic accuracy and a time- and effort-intensive process. To overcome these limitations, we developed a deep learning-based electromyography diagnosis system and compared its performance with that of six physicians. This study included 58 participants who underwent electromyography and were subsequently confirmed to have myopathy or neuropathy or to be in a normal state at single tertiary centre. We developed a one-dimensional convolutional neural network and Divide-and-Vote algorithms for diagnosing patients. Diagnostic results from our deep learning model were compared with those of six physicians with experience in performing and interpreting electromyography. The accuracy, sensitivity, specificity, and positive predictive value of the deep learning model were 0.875, 0.820, 0.904, and 0.820, respectively, whereas those of the physicians were 0.694, 0.537, 0.773, and 0.524, respectively. The area under the receiver operating characteristic curves of the deep learning model was also better than those of the averaged results of the six physicians. Thus, deep learning could play a key role in diagnosing patients with neuromuscular diseases.
Purpose Although several studies with animals have reported the effects of mesenchymal stem cells (MSCs) for tendon regeneration, little is known about the efficacy and safety of MSCs in human tendon disorders. We performed this meta-analysis to evaluate the efficacy and safety of MSC therapy in patients with tendon disorders enrolled in prospective clinical studies. Methods We systematically searched prospective clinical studies investigating the effects of MSCs administration on human tendon disorders with at least a 6-month follow-up period on PubMed-Medline, Embase, and Cochrane Library databases. The primary outcome of interest was the change in pain on motion related to tendon disorders. We performed a pairwise meta-analysis using the fixed-effects model to assess treatment response, which was calculated by the standardized mean difference. Meta-regression analyses were performed to assess the relationship between MSCs dose and pooled effect sizes in each cell dose. Results Four prospective clinical trials investigating the effect of MSCs on tendon disorders were retrieved. MSCs showed significant pooled effect size (overall Hedge’s g pooled standardized mean difference (SMD) = 1.868; 95% confidence interval [CI], 1.274–2.462; P < 0.001). The treatment with MSCs improved all the aspects analyzed, i.e. pain, functional scores, radiologic parameters (magnetic resonance image or ultrasonography), and arthroscopic findings. In the meta-regression analysis, there was a significant cell dose-dependent response in pain relief (Q = 9.06, P = 0.029). While three studies reported mild adverse events after MSCs injection, these were not severe and relieved spontaneously. Conclusions Our meta-analysis revealed that MSC therapy may improve pain, function, radiologic, and arthroscopic parameters in patients with tendon disorders. Due to the small number of studies in this meta-analysis and considering the increasing MSCs applications, there is a strong need for large-scale randomized controlled trials to confirm the long-term functional improvement as well as the adverse effects of MSC therapies in tendon disorders.
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