Artificial Intelligence Increases the Agreement among Physicians Classifying Focal Skeleton/Bone Marrow Uptake in Hodgkin's Lymphoma Patients Staged with [F-18]FDG PET/CT-a Retrospective Study
Journal article, 2023

Purpose 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 [F-18]FDG PET/CT. Methods Forty-eight patients staged with [F-18]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. Results 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. Conclusion An AI-based method significantly increases interobserver agreement among physicians working at different hospitals by highlighting suspicious focal BMU in HL patients staged with [F-18]FDG PET/CT.

Hodgkin disease

Bone marrow

Artificial intelligence

Observer variation

Fluorodeoxyglucose F18


May Sadik

University of Gothenburg

Jesus Lopez-Urdaneta

University of Gothenburg

Johannes Ulen

Eigenvision AB

Olof Enqvist

Imaging and Image Analysis

Eigenvision AB

Per-Ola Andersson

University of Gothenburg

Rajender Kumar

Post Graduate Institute of Medical Education and Research

Elin Tragardh

Lund University

Skåne University Hospital

Nuclear Medicine and Molecular Imaging

1869-3474 (ISSN) 1869-3482 (eISSN)

Vol. 57 2 110-116

Subject Categories

Medical Ethics


Radiology, Nuclear Medicine and Medical Imaging

Cancer and Oncology



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7/6/2023 7