Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies
Accuracy of a machine learning muscle MRI-based tool for the diagnosis of muscular dystrophies
复制标题
基于机器学习肌肉 MRI 的肌肉营养不良诊断工具的准确性
作者：
J. Verdu;J. Alonso;C. Nuñez;G. Tasca;J. Vissing;V. Straub;R. Fernandez;J. Llauger;I. Illa;J. Díaz
目的肌营养不良症（MD）的遗传学诊断通常由临床表现、肌肉活检和肌肉MRI资料指导。肌肉MRI建议根据肌肉脂肪替代模式进行诊断。然而，不同疾病之间的模式重叠，有关疾病特异性模式的知识有限。我们的目标是开发一种基于软件的工具，可以识别肌肉MRI模式，从而帮助诊断MD。方法收集10个不同MD的976例骨盆和下肢肌肉T1加权MRI。使用Mercuri评分量化脂肪替代，并生成包含数字数据的文件。随机森林监督机器学习被应用于开发一个用于识别正确诊断的模型。生成了2000个不同的模型，并选择了准确度最高的模型。使用一组新的20个MRI来测试模型的准确性，并将结果与该领域4位专家提出的诊断进行比较。结果共使用10个不同MD的976条下肢MRI。获得的最佳模型具有95.7%的准确性，92.1%的灵敏度和99.4%的特异性。与该领域的专家相比，在一组新的20个MRI中，所生成模型的诊断准确性显著更高。结论机器学习可以通过分析肌肉MRI中肌肉脂肪替代的模式来帮助医生诊断肌肉营养不良。该工具可以在日常诊所和下一代测序测试结果的解释中有所帮助。证据分类本研究提供了II类证据，证明基于肌肉MRI的人工智能工具可以准确诊断肌营养不良症。
Objective Genetic diagnosis of muscular dystrophies (MDs) has classically been guided by clinical presentation, muscle biopsy, and muscle MRI data. Muscle MRI suggests diagnosis based on the pattern of muscle fatty replacement. However, patterns overlap between different disorders and knowledge about disease-specific patterns is limited. Our aim was to develop a software-based tool that can recognize muscle MRI patterns and thus aid diagnosis of MDs. Methods We collected 976 pelvic and lower limbs T1-weighted muscle MRIs from 10 different MDs. Fatty replacement was quantified using Mercuri score and files containing the numeric data were generated. Random forest supervised machine learning was applied to develop a model useful to identify the correct diagnosis. Two thousand different models were generated and the one with highest accuracy was selected. A new set of 20 MRIs was used to test the accuracy of the model, and the results were compared with diagnoses proposed by 4 specialists in the field. Results A total of 976 lower limbs MRIs from 10 different MDs were used. The best model obtained had 95.7% accuracy, with 92.1% sensitivity and 99.4% specificity. When compared with experts on the field, the diagnostic accuracy of the model generated was significantly higher in a new set of 20 MRIs. Conclusion Machine learning can help doctors in the diagnosis of muscle dystrophies by analyzing patterns of muscle fatty replacement in muscle MRI. This tool can be helpful in daily clinics and in the interpretation of the results of next-generation sequencing tests. Classification of evidence This study provides Class II evidence that a muscle MRI-based artificial intelligence tool accurately diagnoses muscular dystrophies.
利用全基因组测序诊断肢腰肌营养不良症的结果和经验教训
影响因子：
29
作者：
Ghaoui, Roula;Cooper, Sandra T.;Clarke, Nigel F.
通讯作者：
Clarke, Nigel F.
DOI：
10.1056/nejmp1705348
发表时间：
2017-09-28
期刊：
The New England journal of medicine
影响因子：
--
作者：
Obermeyer Z;Lee TH
通讯作者：
Lee TH