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MACHINE LEARNING CAN IDENTIFY AN ANTINUCLEAR ANTIBODY PATTERN THAT MAY RULE OUT SYSTEMIC AUTOIMMUNE RHEUMATIC DISEASES
Abstract   Peer reviewed

MACHINE LEARNING CAN IDENTIFY AN ANTINUCLEAR ANTIBODY PATTERN THAT MAY RULE OUT SYSTEMIC AUTOIMMUNE RHEUMATIC DISEASES

Farbod Moghaddam, Javad Sajadi, Ann Clarke, Sasha Bernatsky, Karen Costenbader, Murray Urowitz, John Hanly, Caroline Gordon, Sang-Cheol Bae, Juanita Romero-Diaz, …
Journal of rheumatology, v 52(Suppl 1), pp 30-30
21 May 2025
DOI: https://doi.org/10.3899/jrheum.2025-0390.O032
url
https://doi.org/10.3899/jrheum.2025-0390.O032View
Published, Version of Record (VoR) Open

Abstract

Background/Purpose Antinuclear antibody (ANA) testing is used to screen for systemic autoimmune rheumatic diseases (SARD) like systemic lupus erythematosus. It is well established that a nuclear dense fine-speckled (DFS) ANA pattern (AC-2), being rare among SARD patients, decreases the likelihood of these conditions. However, the AC-2 pattern is challenging for lab technologists to accurately identify due to similarities with other patterns, ie, AC-4 (speckled) and AC-30 (nuclear speckled with mitotic plate staining), which are associated with SARDs. We determined if machine learning could accurately differentiate between AC-2 and SARD-related AC-4/AC-30 patterns. Methods 13,671 ANA images from SLE patients enrolled in the Systemic Lupus International Collaborating Clinics Inception Cohort (SLICC, n=2,825 images), non-SLE subjects enrolled in the Ontario Health Study (OHS, n=10,639 images), and the International Consensus on ANA Patterns (ICAP, n=207 images) were analyzed. All SLICC and OHS ANA were performed in one central laboratory using IFA on HEp-2 cells (NovaLite, Werfen, SD) and read on a digital IFA microscope (NovaView, Werfen, SD). A lab technologist (HH) with >30 years of experience identified AC-2, AC-4, and AC-30 images. Images were resized to 224x224 pixels. Three machine learning models (ANA Reader©) using a convolutional neural network (CNN) and an image feature extractor were developed to differentiate AC-2 from the other patterns. We also merged the outputs of all 3 CNNs to create a combined ANA Reader© model. 80% of the images were used for training and 20% for validation. We compared the performance of the 4 machine learning models (lab technologist as the reference standard) to determine the best prediction model. Results The lab technologist identified 308 AC-2, 957 AC-4, and 379 AC-30 images. All 4 models performed similarly with high area-under-the-curve (AUC) scores ranging from 96.5%-97.1% (Table 1). When comparing other performance metrics, the combined ANA Reader© model performed the best with the highest accuracy (93.0%), precision (92.7%), specificity (93.2%), and F1 score (92.7%). It was tied with another CNN model (Model 2) for the second most sensitive model (92.7%).

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