■在医学成像中通过传统的测量和观察来诊断Graves\'眼病(GO)的效果并不理想。本研究旨在开发和验证可应用于基于磁共振成像(MRI)的GO诊断的深度学习(DL)模型,并将其与放射科医生的传统测量和判断进行比较。
■回顾性招募了199名临床证实的连续GO患者和145名接受MRI检查的正常对照,其中240人被随机分配到训练组,104人被随机分配到验证组。优越的地区,劣等,中间,分别计算冠状面上的外侧直肌和所有直肌。建立基于眼外肌面积的Logistic回归模型来诊断GO。使用名为ResNet101和SwinTransformer的无对比T1加权MRI作为输入的DL模型来诊断GO,并将结果与仅依靠MRIT1加权扫描的放射科医生的诊断进行比较。
■GO组各肌肉冠状面上的面积明显大于正常组。在验证组中,逻辑回归模型的曲线下面积(AUC),劣等,中间,外侧直肌和所有肌肉均为0.897[95%置信区间(CI):0.833-0.949],0.705(95%CI:0.598-0.804),0.799(95%CI:0.712-0.876),0.681(95%CI:0.567-0.776),和0.905(95%CI:0.843-0.955)。ResNet101和Swin变压器的AUC分别为0.986(95%CI:0.977-0.994)和0.936(95%CI:0.912-0.957),分别。准确性,灵敏度,ResNet101的特异性分别为0.933、0.979和0.869。准确性,灵敏度,Swin变压器的特异性分别为0.851、0.817和0.898。ResNet101模型的AUC高于所有肌肉和放射科医生的模型(0.986vs.0.905,0.818;P<0.001)。
■基于MRIT1加权扫描的DL模型可以准确诊断GO,DL系统在MRI中的应用可能会提高放射科医师在GO诊断和早期检测方面的表现。
UNASSIGNED: The effect of diagnosing Graves\' ophthalmopathy (GO) through traditional measurement and observation in medical imaging is not ideal. This study aimed to develop and validate deep learning (DL) models that could be applied to the diagnosis of GO based on magnetic resonance imaging (MRI) and compare them to traditional measurement and judgment of radiologists.
UNASSIGNED: A total of 199 clinically verified consecutive GO patients and 145 normal controls undergoing MRI were retrospectively recruited, of whom 240 were randomly assigned to the training group and 104 to the validation group. Areas of superior, inferior, medial, and lateral rectus muscles and all rectus muscles on coronal planes were calculated respectively. Logistic regression models based on areas of extraocular muscles were built to diagnose GO. The DL models named ResNet101 and Swin Transformer with T1-weighted MRI without contrast as input were used to diagnose GO and the results were compared to the radiologist\'s diagnosis only relying on MRI T1-weighted scans.
UNASSIGNED: Areas on the coronal plane of each muscle in the GO group were significantly greater than those in the normal group. In the validation group, the areas under the curve (AUCs) of logistic regression models by superior, inferior, medial, and lateral rectus muscles and all muscles were 0.897 [95% confidence interval (CI): 0.833-0.949], 0.705 (95% CI: 0.598-0.804), 0.799 (95% CI: 0.712-0.876), 0.681 (95% CI: 0.567-0.776), and 0.905 (95% CI: 0.843-0.955). ResNet101 and Swin Transformer achieved AUCs of 0.986 (95% CI: 0.977-0.994) and 0.936 (95% CI: 0.912-0.957), respectively. The accuracy, sensitivity, and specificity of ResNet101 were 0.933, 0.979, and 0.869, respectively. The accuracy, sensitivity, and specificity of Swin Transformer were 0.851, 0.817, and 0.898, respectively. The ResNet101 model yielded higher AUC than models of all muscles and radiologists (0.986 vs. 0.905, 0.818; P<0.001).
UNASSIGNED: The DL models based on MRI T1-weighted scans could accurately diagnose GO, and the application of DL systems in MRI may improve radiologists\' performance in diagnosing GO and early detection.