PDF(Pubmed)
Abstract：
BACKGROUND: Anterior cruciate ligament (ACL) injuries are common in sports and are critical knee injuries that require prompt diagnosis. Magnetic resonance imaging (MRI) is a strong, noninvasive tool for detecting ACL tears, which requires training to read accurately. Clinicians with different experiences in reading MR images require different information for the diagnosis of ACL tears. Artificial intelligence (AI) image processing could be a promising approach in the diagnosis of ACL tears.
OBJECTIVE: This study sought to use AI to (1) diagnose ACL tears from complete MR images, (2) identify torn-ACL images from complete MR images with a diagnosis of ACL tears, and (3) differentiate intact-ACL and torn-ACL MR images from the selected MR images.
METHODS: The sagittal MR images of torn ACL (n=1205) and intact ACL (n=1018) from 800 cases and the complete knee MR images of 200 cases (100 torn ACL and 100 intact ACL) from patients aged 20-40 years were retrospectively collected. An AI approach using a convolutional neural network was applied to build models for the objective. The MR images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. The MR images of 40 randomly selected cases from the test set were used to compare the reading accuracy of ACL tears between the trained model and clinicians with different levels of experience.
RESULTS: The first model differentiated between torn-ACL, intact-ACL, and other images from complete MR images with an accuracy of 0.9946, and the sensitivity, specificity, precision, and F1-score were 0.9344, 0.9743, 0.8659, and 0.8980, respectively. The final accuracy for ACL-tear diagnosis was 0.96. The model showed a significantly higher reading accuracy than less experienced clinicians. The second model identified torn-ACL images from complete MR images with a diagnosis of ACL tear with an accuracy of 0.9943, and the sensitivity, specificity, precision, and F1-score were 0.9154, 0.9660, 0.8167, and 0.8632, respectively. The third model differentiated torn- and intact-ACL images with an accuracy of 0.9691, and the sensitivity, specificity, precision, and F1-score were 0.9827, 0.9519, 0.9632, and 0.9728, respectively.
CONCLUSIONS: This study demonstrates the feasibility of using an AI approach to provide information to clinicians who need different information from MRI to diagnose ACL tears.
OBJECTIVE: This study sought to use AI to (1) diagnose ACL tears from complete MR images, (2) identify torn-ACL images from complete MR images with a diagnosis of ACL tears, and (3) differentiate intact-ACL and torn-ACL MR images from the selected MR images.
METHODS: The sagittal MR images of torn ACL (n=1205) and intact ACL (n=1018) from 800 cases and the complete knee MR images of 200 cases (100 torn ACL and 100 intact ACL) from patients aged 20-40 years were retrospectively collected. An AI approach using a convolutional neural network was applied to build models for the objective. The MR images of 200 independent cases (100 torn ACL and 100 intact ACL) were used as the test set for the models. The MR images of 40 randomly selected cases from the test set were used to compare the reading accuracy of ACL tears between the trained model and clinicians with different levels of experience.
RESULTS: The first model differentiated between torn-ACL, intact-ACL, and other images from complete MR images with an accuracy of 0.9946, and the sensitivity, specificity, precision, and F1-score were 0.9344, 0.9743, 0.8659, and 0.8980, respectively. The final accuracy for ACL-tear diagnosis was 0.96. The model showed a significantly higher reading accuracy than less experienced clinicians. The second model identified torn-ACL images from complete MR images with a diagnosis of ACL tear with an accuracy of 0.9943, and the sensitivity, specificity, precision, and F1-score were 0.9154, 0.9660, 0.8167, and 0.8632, respectively. The third model differentiated torn- and intact-ACL images with an accuracy of 0.9691, and the sensitivity, specificity, precision, and F1-score were 0.9827, 0.9519, 0.9632, and 0.9728, respectively.
CONCLUSIONS: This study demonstrates the feasibility of using an AI approach to provide information to clinicians who need different information from MRI to diagnose ACL tears.
摘要：
背景：前交叉韧带（ACL）损伤在运动中很常见，是严重的膝关节损伤，需要及时诊断。磁共振成像(MRI)是一种很强的,用于检测ACL撕裂的非侵入性工具，这需要训练才能准确阅读。在阅读MR图像方面具有不同经验的临床医生需要不同的信息来诊断ACL撕裂。人工智能（AI）图像处理可能是诊断ACL撕裂的一种有前途的方法。
目的：这项研究试图使用AI来（1）从完整的MR图像中诊断ACL撕裂，（2）从完整的MR图像中识别撕裂的ACL图像，并诊断为ACL撕裂，和(3)将完整ACL和撕裂ACLMR图像与所选择的MR图像区分开。
方法：回顾性收集了800例撕裂的ACL（n=1205）和完整的ACL（n=1018）的矢状MR图像以及200例（100例撕裂的ACL和100例完整的ACL）20-40岁患者的完整膝关节MR图像。使用卷积神经网络的AI方法被应用于为目标构建模型。使用200个独立病例的MR图像（100个撕裂的ACL和100个完整的ACL）作为模型的测试集。从测试集中随机选择的40例的MR图像用于比较训练模型与具有不同经验水平的临床医生之间的ACL眼泪的读取准确性。
结果：第一个区分撕裂ACL的模型，完整的ACL,以及来自完整MR图像的其他图像，精度为0.9946，灵敏度，特异性，精度,F1评分分别为0.9344、0.9743、0.8659和0.8980。ACL撕裂诊断的最终准确性为0.96。该模型显示出比经验不足的临床医生明显更高的阅读准确性。第二个模型从完整的MR图像中识别出撕裂的ACL图像，诊断ACL撕裂的准确度为0.9943，灵敏度为，特异性，精度,F1评分分别为0.9154、0.9660、0.8167和0.8632。第三个模型区分撕裂和完整的ACL图像，精度为0.9691，灵敏度，特异性，精度,F1评分分别为0.9827、0.9519、0.9632和0.9728。
结论：这项研究证明了使用AI方法为需要MRI诊断ACL撕裂的不同信息的临床医生提供信息的可行性。
目的：这项研究试图使用AI来（1）从完整的MR图像中诊断ACL撕裂，（2）从完整的MR图像中识别撕裂的ACL图像，并诊断为ACL撕裂，和(3)将完整ACL和撕裂ACLMR图像与所选择的MR图像区分开。
方法：回顾性收集了800例撕裂的ACL（n=1205）和完整的ACL（n=1018）的矢状MR图像以及200例（100例撕裂的ACL和100例完整的ACL）20-40岁患者的完整膝关节MR图像。使用卷积神经网络的AI方法被应用于为目标构建模型。使用200个独立病例的MR图像（100个撕裂的ACL和100个完整的ACL）作为模型的测试集。从测试集中随机选择的40例的MR图像用于比较训练模型与具有不同经验水平的临床医生之间的ACL眼泪的读取准确性。
结果：第一个区分撕裂ACL的模型，完整的ACL,以及来自完整MR图像的其他图像，精度为0.9946，灵敏度，特异性，精度,F1评分分别为0.9344、0.9743、0.8659和0.8980。ACL撕裂诊断的最终准确性为0.96。该模型显示出比经验不足的临床医生明显更高的阅读准确性。第二个模型从完整的MR图像中识别出撕裂的ACL图像，诊断ACL撕裂的准确度为0.9943，灵敏度为，特异性，精度,F1评分分别为0.9154、0.9660、0.8167和0.8632。第三个模型区分撕裂和完整的ACL图像，精度为0.9691，灵敏度，特异性，精度,F1评分分别为0.9827、0.9519、0.9632和0.9728。
结论：这项研究证明了使用AI方法为需要MRI诊断ACL撕裂的不同信息的临床医生提供信息的可行性。
