Chamberlin J H, Smith C, Schoepf U J, Nance S, Elojeimy S, O'Doherty J, Baruah D, Burt J R, Varga-Szemes A, Kabakus I M
Division of Thoracic Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
Division of Cardiovascular Imaging, Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA.
Clin Radiol. 2023 May;78(5):e368-e376. doi: 10.1016/j.crad.2023.01.014. Epub 2023 Feb 16.
To evaluate primary and secondary pathologies of interest using an artificial intelligence (AI) platform, AI-Rad Companion, on low-dose computed tomography (CT) series from integrated positron-emission tomography (PET)/CT to detect CT findings that might be overlooked.
One hundred and eighty-nine sequential patients who had undergone PET/CT were included. Images were evaluated using an ensemble of convolutional neural networks (AI-Rad Companion, Siemens Healthineers, Erlangen, Germany). The primary outcome was detection of pulmonary nodules for which the accuracy, identity, and intra-rater reliability was calculated. For secondary outcomes (binary detection of coronary artery calcium, aortic ectasia, vertebral height loss), accuracy and diagnostic performance were calculated.
The overall per-nodule accuracy for detection of lung nodules was 0.847. The overall sensitivity and specificity for detection of lung nodules was 0.915 and 0.781. The overall per-patient accuracy for AI detection of coronary artery calcium, aortic ectasia, and vertebral height loss was 0.979, 0.966, and 0.840, respectively. The sensitivity and specificity for coronary artery calcium was 0.989 and 0.969. The sensitivity and specificity for aortic ectasia was 0.806 and 1.
The neural network ensemble accurately assessed the number of pulmonary nodules and presence of coronary artery calcium and aortic ectasia on low-dose CT series of PET/CT. The neural network was highly specific for the diagnosis of vertebral height loss, but not sensitive. The use of the AI ensemble can help radiologists and nuclear medicine physicians to catch CT findings that might be overlooked.
使用人工智能（AI）平台AI-Rad Companion对整合正电子发射断层显像（PET）/CT的低剂量计算机断层扫描（CT）系列进行评估，以检测可能被忽视的CT表现，评估感兴趣的原发性和继发性病变。
纳入189例连续接受PET/CT检查的患者。使用卷积神经网络集合（AI-Rad Companion，西门子医疗，德国埃尔朗根）对图像进行评估。主要结局是检测肺结节，并计算其准确性、一致性及评估者内部可靠性。对于次要结局（冠状动脉钙化、主动脉扩张、椎体高度丢失的二元检测），计算准确性和诊断性能。
肺结节检测的总体结节准确率为0.847。肺结节检测的总体敏感性和特异性分别为0.915和0.781。AI检测冠状动脉钙化、主动脉扩张和椎体高度丢失的总体患者准确率分别为0.979、0.966和0.840。冠状动脉钙化的敏感性和特异性分别为0.989和0.969。主动脉扩张的敏感性和特异性分别为0.806和1。
神经网络集合准确评估了PET/CT低剂量CT系列上的肺结节数量以及冠状动脉钙化和主动脉扩张的存在情况。神经网络对椎体高度丢失的诊断具有高度特异性，但不敏感。使用AI集合可帮助放射科医生和核医学医生发现可能被忽视的CT表现。
