Interpregnancy management for women with sickle cell disease: A systematic review.

Sjögren’s disease (SjD), a chronic inflammatory autoimmune disorder affecting multiple exocrine glands, is a global condition with cases reported worldwide across diverse regions, ethnicities, and populations. Primarily targeting salivary and lacrimal glands, its hallmark symptoms include persistent dry mouth and eyes. In China, the prevalence ranges from 0.3% to 0.7% (), predominantly affecting women aged 40–60 with a male-to-female ratio of 1:9 (). Beyond these core symptoms, patients often experience fatigue and joint pain, severely impairing daily activities like eating and vision, which drastically reduces quality of life. Severe cases may develop complications such as pulmonary fibrosis and lymphoma, requiring prolonged multidisciplinary care and consuming substantial medical resources. This not only causes significant physical and emotional distress but also imposes substantial burdens on personal health, family finances, and the healthcare system. Currently, lip gland biopsy remains the gold standard pathological criterion for diagnosing Sjögren’s disease (). The diagnosis is established by evaluating the extent of focal lymphocytic infiltration within the gland (focal score ≥1/4 mm²), which offers advantages such as high objectivity and reproducibility. Both the 2002 International Classification and the 2016 American College of Rheumatology/European Union of Rheumatology (ACR/EULAR) classification standards list pathological examination results as core diagnostic criteria (). However, early diagnosis of Sjögren’s disease faces multiple challenges: On one hand, the disease presents with complex and varied symptoms, with some patients only showing mild discomfort that can be easily confused with other common causes of dry mouth and eyes. On the other hand, traditional imaging examinations exhibit strong subjectivity, with limitations such as high operator dependence and significant subjective interpretation biases, making it difficult for clinicians to detect subtle glandular structural changes early, thereby exacerbating diagnostic delays. In recent years, the introduction of artificial intelligence (AI) technology has opened new pathways to overcome the aforementioned bottlenecks. The heterogeneity in clinical manifestations and pathophysiology of Sjögren’s disease—ranging from varying symptom severity and significant differences in glandular damage to multi-pathway immune-inflammatory mechanisms—poses challenges not only for early diagnosis but also for developing effective therapies. Deep learning-based imaging omics technology, however, can automatically extract thousands of quantitative features from routine medical imaging and combine machine learning (ML) algorithms (such as convolutional neural networks and random forests) to build predictive models, enabling objective quantification of glandular damage. For instance, integrating salivary gland ultrasound (SGUS) imaging omics features with serum autoantibody levels (anti-SSA/Ro antibodies, anti-SSB/La antibodies) allows AI to improve early glandular lesion detection accuracy by over 30%, providing technical support for identifying disease diagnosis windows (). This multimodal data fusion strategy not only compensates for the subjectivity of traditional imaging examinations but also reveals potential correlations between imaging features, pathological biopsies, and molecular markers, offering precise decision-making foundations for personalized Sjögren’s disease diagnosis and treatment. The approach demonstrates significant clinical translational value.