骨转移性疾病(MBD)是最常见的转移形式，大多数来自前列腺癌。MBD通过骨显像检查(BS)进行筛查，该检查对于MBD的诊断具有高敏感性但低特异性，通常需要进一步的调查。深度学习(DL)——一种旨在模仿人类神经元相互作用的机器学习技术——在医学影像分析领域中显示出了潜力，包括病变的分割和分类。在本研究中，我们旨在开发一种DL算法，可对骨显像扫描中增强的区域进行分类。我们收集了三个欧洲医疗中心的2365个BS。该模型在1203个和164个BS扫描中进行了训练和验证。此外，我们还评估了其在由998个BS扫描组成的外部测试集上的性能。我们进一步旨在通过使用激活图来增强我们开发的算法的可解释性。我们将我们的算法性能与6名核医学医师的表现进行了比较。开发的基于DL的算法能够在BS上检测MBD，具有较高的特异性和敏感性(分别在外部测试集上为0.80和0.82)，而与核医学医师(2.5分钟的AI和30分钟的核医学医师对134个BS进行分类)相比，能够更短的时间内完成任务。在该算法能用于临床之前，还需要进一步的前瞻性验证。©2023.作者。

Metastatic bone disease (MBD) is the most common form of metastases, most frequently deriving from prostate cancer. MBD is screened with bone scintigraphy (BS), which have high sensitivity but low specificity for the diagnosis of MBD, often requiring further investigations. Deep learning (DL) - a machine learning technique designed to mimic human neuronal interactions- has shown promise in the field of medical imaging analysis for different purposes, including segmentation and classification of lesions. In this study, we aim to develop a DL algorithm that can classify areas of increased uptake on bone scintigraphy scans.We collected 2365 BS from three European medical centres. The model was trained and validated on 1203 and 164 BS scans respectively. Furthermore we evaluated its performance on an external testing set composed of 998 BS scans. We further aimed to enhance the explainability of our developed algorithm, using activation maps. We compared the performance of our algorithm to that of 6 nuclear medicine physicians.The developed DL based algorithm is able to detect MBD on BSs, with high specificity and sensitivity (0.80 and 0.82 respectively on the external test set), in a shorter time compared to the nuclear medicine physicians (2.5 min for AI and 30 min for nuclear medicine physicians to classify 134 BSs). Further prospective validation is required before the algorithm can be used in the clinic.© 2023. The Author(s).