Droppelmann Guillermo, Rodríguez Constanza, Smague Dali, Jorquera Carlos, Feijoo Felipe
Research Center on Medicine, Exercise, Sport and Health, MEDS Clinic, Santiago, RM, Chile.
Health Sciences PhD Program, Universidad Católica de Murcia UCAM, Murcia, Spain.
EFORT Open Rev. 2024 Oct 3;9(10):941-952. doi: 10.1530/EOR-24-0016.
Different deep-learning models have been employed to aid in the diagnosis of musculoskeletal pathologies. The diagnosis of tendon pathologies could particularly benefit from applying these technologies. The objective of this study is to assess the performance of deep learning models in diagnosing tendon pathologies using various imaging modalities.
A meta-analysis was conducted, with searches performed on MEDLINE/PubMed, SCOPUS, Cochrane Library, Lilacs, and SciELO. The QUADAS-2 tool was employed to assess the quality of the studies. Diagnostic measures, such as sensitivity, specificity, diagnostic odds ratio, positive and negative likelihood ratios, area under the curve, and summary receiver operating characteristic, were included using a random-effects model. Heterogeneity and subgroup analyses were also conducted. All statistical analyses and plots were generated using the R software package. The PROSPERO ID is CRD42024506491.
Eleven deep-learning models from six articles were analyzed. In the random effects models, the sensitivity and specificity of the algorithms for detecting tendon conditions were 0.910 (95% CI: 0.865; 0.940) and 0.954 (0.909; 0.977). The PLR, NLR, lnDOR, and AUC estimates were found to be 37.075 (95%CI: 4.654; 69.496), 0.114 (95%CI: 0.056; 0.171), 5.160 (95% CI: 4.070; 6.250) with a (P < 0.001), and 96%, respectively.
The deep-learning algorithms demonstrated a high level of accuracy level in detecting tendon anomalies. The overall robust performance suggests their potential application as a valuable complementary tool in diagnosing medical images.
不同的深度学习模型已被用于辅助肌肉骨骼疾病的诊断。肌腱疾病的诊断尤其可以从应用这些技术中受益。本研究的目的是评估深度学习模型在使用各种成像模态诊断肌腱疾病方面的性能。
进行了一项荟萃分析，在MEDLINE/PubMed、SCOPUS、Cochrane图书馆、Lilacs和SciELO上进行检索。采用QUADAS - 2工具评估研究质量。使用随机效应模型纳入诊断指标，如敏感性、特异性、诊断比值比、阳性和阴性似然比、曲线下面积和汇总接受者操作特征。还进行了异质性和亚组分析。所有统计分析和图表均使用R软件包生成。PROSPERO编号为CRD42024506491。
分析了来自六篇文章的11个深度学习模型。在随机效应模型中，检测肌腱疾病算法的敏感性和特异性分别为0.910（95%CI：0.865；0.940）和0.954（0.909；0.977）。发现阳性似然比、阴性似然比、对数诊断比值比和曲线下面积估计值分别为37.075（95%CI：4.654；69.496）、0.114（95%CI：0.056；0.171）、5.160（95%CI：4.070；6.250）（P < 0.001）和96%。
深度学习算法在检测肌腱异常方面表现出较高的准确性。整体稳健的性能表明它们作为诊断医学图像的有价值的辅助工具具有潜在应用价值。
