骨骼/骨髓摄取（BMU）的分类可能具有挑战性。我们的目标是探索一种基于人工智能的方法（AI），该方法可以突出可疑的局部BMU，以增强不同医院的一组医生对化疗病人（HL）进行分类时的观察者间的一致性。调查了在2017年至2018年间在Sahlgenska大学医院进行[18F]FDG PET/CT分期的48例化疗病人，两次审核他们的局部BMU，隔6个月。在第二次审核期间，10名医生还可以接触到有关中心性BMU的AI建议。将每个医生的分类与所有其他医生所做的分类进行成对比较，因此获得了45个没有和有AI建议的独特的比较对。AI建议可使医生间的协议显著提高，通过在没有AI建议时的平均Kappa值从0.51（范围0.25-0.80）到有AI建议时的平均Kappa值0.61（范围0.19-0.94）（p = 0.005） 。大多数医生都同意在48个案例中40个（83％）基于AI的方法。基于人工智能的方法通过突出HL病人中可疑的局部BMU，显著增加了不同医院工作的医生之间的观察者一致性。 ©作者（们）2022。

Classification of focal skeleton/bone marrow uptake (BMU) can be challenging. The aim is to investigate whether an artificial intelligence-based method (AI), which highlights suspicious focal BMU, increases interobserver agreement among a group of physicians from different hospitals classifying Hodgkin's lymphoma (HL) patients staged with [18F]FDG PET/CT.Forty-eight patients staged with [18F]FDG PET/CT at Sahlgenska University Hospital between 2017 and 2018 were reviewed twice, 6 months apart, regarding focal BMU. During the second time review, the 10 physicians also had access to AI-based advice regarding focal BMU.Each physician's classifications were pairwise compared with the classifications made by all the other physicians, resulting in 45 unique pairs of comparisons both without and with AI advice. The agreement between the physicians increased significantly when AI advice was available, which was measured as an increase in mean Kappa values from 0.51 (range 0.25-0.80) without AI advice to 0.61 (range 0.19-0.94) with AI advice (p = 0.005). The majority of the physicians agreed with the AI-based method in 40 (83%) of the 48 cases.An AI-based method significantly increases interobserver agreement among physicians working at different hospitals by highlighting suspicious focal BMU in HL patients staged with [18F]FDG PET/CT.© The Author(s) 2022.