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. 2024 Mar 4:11:1297088.
doi: 10.3389/fmed.2024.1297088. eCollection 2024.

Artificial intelligence model for segmentation and severity scoring of osteophytes in hand osteoarthritis on ultrasound images

Benjamin Schultz Overgaard  1 , Anders Bossel Holst Christensen  2 , Lene Terslev  3 , Thiusius Rajeeth Savarimuthu  4 , Søren Andreas Just  1
Affiliations

Artificial intelligence model for segmentation and severity scoring of osteophytes in hand osteoarthritis on ultrasound images

Benjamin Schultz Overgaard et al. Front Med (Lausanne). .

Abstract

Objective: To develop an artificial intelligence (AI) model able to perform both segmentation of hand joint ultrasound images for osteophytes, bone, and synovium and perform osteophyte severity scoring following the EULAR-OMERACT grading system (EOGS) for hand osteoarthritis (OA).

Methods: One hundred sixty patients with pain or reduced function of the hands were included. Ultrasound images of the metacarpophalangeal (MCP), proximal interphalangeal (PIP), distal interphalangeal (DIP), and first carpometacarpal (CMC1) joints were then manually segmented for bone, synovium and osteophytes and scored from 0 to 3 according to the EOGS for OA. Data was divided into a training, validation, and test set. The AI model was trained on the training data to perform bone, synovium, and osteophyte identification on the images. Based on the manually performed image segmentation, an AI was trained to classify the severity of osteophytes according to EOGS from 0 to 3. Percent Exact Agreement (PEA) and Percent Close Agreement (PCA) were assessed on individual joints and overall. PCA allows a difference of one EOGS grade between doctor assessment and AI.

Results: A total of 4615 ultrasound images were used for AI development and testing. The developed AI model scored on the test set for the MCP joints a PEA of 76% and PCA of 97%; for PIP, a PEA of 70% and PCA of 97%; for DIP, a PEA of 59% and PCA of 94%, and CMC a PEA of 50% and PCA of 82%. Combining all joints, we found a PEA between AI and doctor assessments of 68% and a PCA of 95%.

Conclusion: The developed AI model can perform joint ultrasound image segmentation and severity scoring of osteophytes, according to the EOGS. As proof of concept, this first version of the AI model is successful, as the agreement performance is slightly higher than previously found agreements between experts when assessing osteophytes on hand OA ultrasound images. The segmentation of the image makes the AI explainable to the doctor, who can immediately see why the AI applies a given score. Future validation in hand OA cohorts is necessary though.

Keywords: artificial intelligence; automated ultrasound scanning; hand osteoarthritis; neural networks; osteoarthritis; osteophyte; robotics.

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Conflict of interest statement

A cooperation agreement between Odense University Hospital and Ropca Aps was signed by both parties before the trial started. SJ and TS are cofounders of Ropca Aps, developing AI and producing the automated ultrasound scanning system called ARTHUR. ARTHUR’s AI cannot currently assess osteophyte or OA severity. AC is a full-time employee of Ropca Aps. The remaining authors declare that the research was conducted without any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Ground truth and AI segmentation on an example image from each EOGS score.
See this image and copyright information in PMC

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