Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs

Thomas Hügle; Elisabeth Rosoux; Guillaume Fahrni; Deborah Markham; Tobias Manigold; Fabio Becce

doi:10.3389/fmed.2024.1431333

Development of a deep learning model for automated detection of calcium pyrophosphate deposition in hand radiographs

Front Med (Lausanne). 2024 Oct 23:11:1431333. doi: 10.3389/fmed.2024.1431333. eCollection 2024.

Authors

Thomas Hügle¹, Elisabeth Rosoux², Guillaume Fahrni², Deborah Markham¹, Tobias Manigold³, Fabio Becce²

Affiliations

¹ Department of Rheumatology, University Hospital Lausanne (CHUV), University of Lausanne, Lausanne, Switzerland.
² Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
³ Department of Rheumatology, Inselspital, University Hospital Bern and University of Bern, Bern, Switzerland.

Abstract

Background: Calcium pyrophosphate deposition (CPPD) disease is a leading cause of arthritis, which can mimic or strongly interfere with other rheumatic diseases such as gout, osteoarthritis (OA) or rheumatoid arthritis (RA). In the recently established ACR/EULAR CPPD classification criteria, calcification and OA features of the wrist and hand joints are substantial features.

Objectives: To develop and test a deep-learning algorithm for automatically and reliably detecting CPPD features in hand radiographs, focusing on calcification of the triangular fibrocartilage complex (TFCC) and metacarpophalangeal (MCP)-2 and -3 joints, in separate or combined models.

Methods: Two radiologists independently labeled a dataset of 926 hand radiographs, yielding 319 CPPD positive and 607 CPPD negative cases across the three sites of interest after adjudicating discrepant cases. CPPD presence was then predicted using a convolutional neural network. We tested seven CPPD models, each with a different combination of sites out of TFCC, MCP-2 and MCP-3. The model performance was assessed using the area under the receiver operating characteristic (AUROC) and area under the precision-recall (AUPR) curves, with heatmaps (Grad-CAM) aiding in case discrimination.

Results: All models trialed gave good class separation, with the combined TFCC, MCP-2 and MCP-3 model showing the most robust performance with a mean AUROC of 0.86, mean AUPR of 0.77, sensitivity of 0.77, specificity of 0.80, and precision of 0.67. The TFCC-alone model had a slightly lower mean AUROC of 0.85 with a mean AUPR of 0.73. The MCP-2-alone and MCP-3-alone models exhibited mean AUROCs of 0.78-0.87, but lower mean AUPRs of 0.29-0.47. Heatmap analysis revealed activation in the regions of interest for positive cases (true and false positives), but unexpected highlights were encountered possibly due to correlated features in different hand regions.

Conclusion: A combined deep-learning model detecting CPPD at the TFCC and MCP-2/3 joints in hand radiographs provides the highest diagnostic performance. The algorithm could be used to screen larger OA or RA databases or electronic medical records for CPPD cases. Future work includes dataset expansion and validation with external datasets.

Keywords: CPPD; automated; chondrocalcinosis; detection; image recoginiton; machine learning; radiograph (X-ray).

Grants and funding

The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. Open access funding by University of Lausanne.