Kim Dong Hyun, Lee Kyong Joon, Choi Dongjun, Lee Jae Ik, Choi Han Gyeol, Lee Yong Seuk
Department of Orthopedic Surgery, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seoul 03080, Korea.
Department of Orthopaedic Surgery, Gwangmyeong 21st Century Hospital, Gyeonggi-do 14100, Korea.
J Clin Med. 2020 Oct 18;9(10):3341. doi: 10.3390/jcm9103341.
The study compares the diagnostic performance of deep learning (DL) with that of the former radiologist reading of the Kellgren-Lawrence (KL) grade and evaluates whether additional patient data can improve the diagnostic performance of DL. From March 2003 to February 2017, 3000 patients with 4366 knee AP radiographs were randomly selected. DL was trained using knee images and clinical information in two stages. In the first stage, DL was trained only with images and then in the second stage, it was trained with image data and clinical information. In the test set of image data, the areas under the receiver operating characteristic curve (AUC)s of the DL algorithm in diagnosing KL 0 to KL 4 were 0.91 (95% confidence interval (CI), 0.88-0.95), 0.80 (95% CI, 0.76-0.84), 0.69 (95% CI, 0.64-0.73), 0.86 (95% CI, 0.83-0.89), and 0.96 (95% CI, 0.94-0.98), respectively. In the test set with image data and additional patient information, the AUCs of the DL algorithm in diagnosing KL 0 to KL 4 were 0.97 (95% confidence interval (CI), 0.71-0.74), 0.85 (95% CI, 0.80-0.86), 0.75 (95% CI, 0.66-0.73), 0.86 (95% CI, 0.79-0.85), and 0.95 (95% CI, 0.91-0.97), respectively. The diagnostic performance of image data with additional patient information showed a statistically significantly higher AUC than image data alone in diagnosing KL 0, 1, and 2 (-values were 0.008, 0.020, and 0.027, respectively).The diagnostic performance of DL was comparable to that of the former radiologist reading of the knee osteoarthritis KL grade. Additional patient information improved DL diagnosis in interpreting early knee osteoarthritis.
该研究比较了深度学习（DL）与之前放射科医生对凯尔格伦-劳伦斯（KL）分级的诊断性能，并评估了额外的患者数据是否能提高DL的诊断性能。2003年3月至2017年2月，随机选取了3000例患者的4366张膝关节前后位X线片。DL分两个阶段使用膝关节图像和临床信息进行训练。在第一阶段，DL仅使用图像进行训练，然后在第二阶段，使用图像数据和临床信息进行训练。在图像数据测试集中，DL算法诊断KL 0至KL 4的受试者操作特征曲线（AUC）下面积分别为0.91（95%置信区间（CI），0.88 - 0.95）、0.80（95%CI，0.76 - 0.84）、0.69（95%CI，0.64 - 0.73）、0.86（95%CI，0.83 - 0.89）和0.96（95%CI，0.94 - 0.98）。在包含图像数据和额外患者信息的测试集中，DL算法诊断KL 0至KL 4的AUC分别为0.97（95%置信区间（CI），0.71 - 0.74）、0.85（95%CI，0.80 - 0.86）、0.75（95%CI，0.66 - 0.73）、0.86（95%CI，0.79 - 0.85）和0.95（95%CI，0.91 - 0.97）。在诊断KL 0、1和2时，包含额外患者信息的图像数据的诊断性能在统计学上显示出比仅图像数据具有显著更高的AUC（-值分别为0.008、0.020和0.027）。DL的诊断性能与之前放射科医生对膝关节骨关节炎KL分级的诊断性能相当。额外的患者信息在解释早期膝关节骨关节炎时改善了DL诊断。
