脊柱骨转移直接影响生活质量，具有骨溶性为主的病变患者高风险出现神经症状和骨折。为了使用常规计算机断层扫描（CT）检测和分类溶性脊柱骨转移，我们开发了一种基于深度学习（DL）的计算机辅助检测（CAD）系统。我们回顾性分析了79名患者的2125个诊断和放射治疗CT图像。标注为肿瘤（阳性）或非肿瘤（阴性）的图像被随机分配到训练（1782张图像）和测试（343张图像）数据集。使用YOLOv5m架构在整个CT扫描上检测椎骨。使用InceptionV3架构和迁移学习技术，将在显示椎骨存在的CT图像中溶性病变的存在/不存在进行分类。通过五倍交叉验证评估DL模型。对于椎骨检测，使用相交比（IoU）估算包围盒准确性。我们评估了接收者操作特征曲线下面积（ROC曲线下面积）对病变进行分类。此外，我们确定了准确性、精确度、召回率和F1分数。我们使用梯度加权类激活映射（Grad-CAM）技术进行视觉解释。计算时间为每张图像0.44秒。测试数据集的预测椎骨的平均IoU值为0.923±0.052（0.684-1.000）。在二元分类任务中，测试数据集的准确性、精确度、召回率、F1分数和AUC值分别为0.872、0.948、0.741、0.832和0.941。使用Grad-CAM技术构建的热图与溶性病变的位置一致。我们的人工智能辅助CAD系统使用两个DL模型可以快速识别整个CT图像中的椎骨，并检测脊柱骨转移的溶性病变，但需要使用更大的样本量进行进一步的诊断准确性评估。© 2023. CARS。

Spinal bone metastases directly affect quality of life, and patients with lytic-dominant lesions are at high risk for neurological symptoms and fractures. To detect and classify lytic spinal bone metastasis using routine computed tomography (CT) scans, we developed a deep learning (DL)-based computer-aided detection (CAD) system.We retrospectively analyzed 2125 diagnostic and radiotherapeutic CT images of 79 patients. Images annotated as tumor (positive) or not (negative) were randomized into training (1782 images) and test (343 images) datasets. YOLOv5m architecture was used to detect vertebra on whole CT scans. InceptionV3 architecture with the transfer-learning technique was used to classify the presence/absence of lytic lesions on CT images showing the presence of vertebra. The DL models were evaluated via fivefold cross-validation. For vertebra detection, bounding box accuracy was estimated using intersection over union (IoU). We evaluated the area under the curve (AUC) of a receiver operating characteristic curve to classify lesions. Moreover, we determined the accuracy, precision, recall, and F1 score. We used the gradient-weighted class activation mapping (Grad-CAM) technique for visual interpretation.The computation time was 0.44 s per image. The average IoU value of the predicted vertebra was 0.923 ± 0.052 (0.684-1.000) for test datasets. In the binary classification task, the accuracy, precision, recall, F1-score, and AUC value for test datasets were 0.872, 0.948, 0.741, 0.832, and 0.941, respectively. Heat maps constructed using the Grad-CAM technique were consistent with the location of lytic lesions.Our artificial intelligence-aided CAD system using two DL models could rapidly identify vertebra bone from whole CT images and detect lytic spinal bone metastasis, although further evaluation of diagnostic accuracy is required with a larger sample size.© 2023. CARS.