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

作者：Farbod Moghaddam, Javad Sajadi, Ann Clarke, Sasha Bernatsky, Karen H. Costenbader, Murray Urowitz, John Hanly, Caroline Gordon, Sang‐Cheol Bae, Juanita Romero‐Díaz, Jorge Sánchez‐Guerrero, Daniel J. Wallace, David Isenberg, Anisur Rahman, Joan T. Merrill, Paul R. Fortin, D. Gladman, Ian Bruce, Michelle Petri, Ellen M. Ginzler, Mary-Anne Dooley, Rosalind Ramsey‐Goldman, Susan Manzi, Andreas Jönsen, Graciela S. Alarcón, Ronald Van Vollenhoven, Cynthia Aranow, Meggan Mackay, Guillermo Ruiz‐Irastorza, S. Sam Lim, Murat İnanç, Kenneth Kalunian, Søren Jacobsen, Christine Peschken, Diane L Kamen, Anca Askanase, Marvin J. Fritzler, Mina Aminghafari, May Y. Choi · 发表于：The Journal of Rheumatology · 年份：2025 · DOI：10.3899/jrheum.2025-0390.o032 · 被引用次数：3 · 研究领域：Systemic Lupus Erythematosus Research、Monoclonal and Polyclonal Antibodies Research、Chronic Lymphocytic Leukemia Research

O032 / #273 Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes ABSTRACT CONCURRENT SESSION 05: EMERGING INSIGHTS ON THE MANAGEMENT OF LUPUS MANIFESTATIONS AND COMORBIDITIES 23-05-2025 1:40 PM - 2:40 PM 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 p...