RHEUMATOLOGY DIAGNOSTICS UTILIZING ARTIFICIAL INTELLIGENCE FOR ANA PATTERN IDENTIFICATION AND TITRE QUANTIFICATION
Notice bibliographique
Résumé
PT012 / #274 Topic: AS23 - SLE-Diagnosis, Manifestations, & Outcomes POSTER TOUR 03: RECENT ADVANCEMENTS IN SLE CLINICAL OUTCOMES AND THERAPY 23-05-2025 10:00 AM - 10:40 AM Background/Purpose Antinuclear antibody (ANA) immunofluorescence (IFA) patterns and titres are a key part of rheumatology diagnostics, however, there is considerable intra- and interlaboratory variability with manual interpretation. Replacing manual interpretation with a standardized and automated approach could help reduce variability, increasing laboratory accuracy and efficiency. We developed machine learning (ML) models (ANA Reader©) to aid laboratories in ANA pattern and titre interpretation, including a model for the nuclear dense fine-speckled (DFS) ANA pattern (AC-2), a rare pattern among systemic autoimmune rheumatic disease (SARD) patients that decreases the likelihood of these conditions. 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 1 central laboratory using IFA on HEp-2 cells (NovaLite, Werfen, SD) and read on a digital IFA microscope (NovaView, Werfen, SD). As the reference standard, 1 laboratory technologist (HH) with >30 years of experience in ANA studies interpreted 13 ANA patterns and titre for each image. We developed and compared the performance of 8 ML models for ANA pattern recognition. To evaluate ANA titre, we used an ML technique for imaging processing that identified individual HEp-2 cells in the ANA images and then calculated the cell illuminance and cut-offs corresponding to each titre (1:80-1:5120). Fifty images were randomly selected to compare the titre classification based on image processing with the lab technologist as the reference standard. Results 6,307 images containing at least 1 ANA pattern (≥1:80) from SLICC (n=2,806 images), OHS (n=3339 images), and ICAP (n=162 images) were included. We identified 1 ML model (ANA Reader©) with the best performance for ANA pattern identification compared to the reference with a high area under the curve (AUC) score of 83.4%, modest weighted accuracy of 68.4%, precision of 67.1%, sensitivity of 70.1%, and F1 score of 67.2%. The AUC for individual ANA patterns ranged from 0.71 to 0.97 (Figure 1). There was a strong correlation between titres reported by the ANA Reader© and the technologist’s interpretation (Spearman rank 0.93, p <0.0001), where the titres reported were identical or differed by only 1 dilution in most cases (96.0%). The ANA patterns with the best performance were centromere (AUC 0.97) and pleomorphic patterns (AUC 0.97). On average, there were 5 images per patient sample for SLICC, 3 images per patient sample for OHS, and 1 image per patient from ICAP. 80% of the images were used for model training and the remaining 20% for validation. In total, there were 512 patients in the SLICC cohort, 3,559 individuals in the OHS cohort, and 207 patients from ICAP who were included in the study. Figure 1. Area under the curve (AUC) scores for the 13 antinuclear antibody (ANA) patterns using the ANA Reader© model, which had the best performance compared to 7 other machine learning techniques. Conclusions ML has the potential to become a highly effective and efficient approach to evaluating ANA patterns and titres. The performance of our ANA Reader© is expected to improve as we continue to train our models with more ANA images. Future external validation studies and the development of other ML models to predict more complex and multiple ANA patterns and titres are also underway.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».