Journal
FRONTIERS IN MEDICINE
Volume 9, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fmed.2022.850552
Keywords
artificial intelligence; arthritis; joint; bone; deep learning
Categories
Funding
- German Research Council [CRC1181, CRC1483, FOR2438]
- PANDORA FOR2886
- German Ministry of Science and Education
- European Union (project ERC-Syn 4DNanoscope)
- Innovative Medicine Initiative (RT-Cure and Hippocrates)
- University Erlangen-Nuernberg
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In this study, a neural network was used to differentiate between rheumatoid arthritis, psoriatic arthritis, and healthy controls based on the shape of joints. Disease-specific regions in joints were identified using heat maps. The trained network was also able to classify patients with undifferentiated arthritis based on joint shape.
Objective:We investigated whether a neural network based on the shape of joints can differentiate between rheumatoid arthritis (RA), psoriatic arthritis (PsA), and healthy controls (HC), which class patients with undifferentiated arthritis (UA) are assigned to, and whether this neural network is able to identify disease-specific regions in joints. MethodsWe trained a novel neural network on 3D articular bone shapes of hand joints of RA and PsA patients as well as HC. Bone shapes were created from high-resolution peripheral-computed-tomography (HR-pQCT) data of the second metacarpal bone head. Heat maps of critical spots were generated using GradCAM. After training, we fed shape patterns of UA into the neural network to classify them into RA, PsA, or HC. ResultsHand bone shapes from 932 HR-pQCT scans of 617 patients were available. The network could differentiate the classes with an area-under-receiver-operator-curve of 82% for HC, 75% for RA, and 68% for PsA. Heat maps identified anatomical regions such as bare area or ligament attachments prone to erosions and bony spurs. When feeding UA data into the neural network, 86% were classified as RA, 11% as PsA, and 3% as HC based on the joint shape. ConclusionWe investigated neural networks to differentiate the shape of joints of RA, PsA, and HC and extracted disease-specific characteristics as heat maps on 3D joint shapes that can be utilized in clinical routine examination using ultrasound. Finally, unspecific diseases such as UA could be grouped using the trained network based on joint shape.
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