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  1. Fulltext Exploring the predictive power of antinuclear antibodies and Rheumatoid factor correlations in anticipating therapeutic outcomes for female patients with coexisting Sjögren's syndrome and Rheumatoid arthritis
    Krishnan Pandarathodiyil A, Shree K H, Ramani P, Sivapathasundharam B, Ramadoss R
    J Oral Biol Craniofac Res, 2025;15(2):288-296.
    PMID: 40027855 DOI: 10.1016/j.jobcr.2025.01.012
    BACKGROUND: Sjögren's Syndrome (SS) and Rheumatoid Arthritis (RA) are autoimmune conditions that often coexist in female patients. Biomarkers such as antinuclear antibodies (ANA) and rheumatoid factor (RF) are used for diagnosis, but their predictive power for treatment outcomes remains unclear. This study aims to investigate the correlation between age, ANA, RF, and treatment response in female patients with both SS and RA.

    OBJECTIVE: To evaluate the relationships between age, ANA, RF levels, RA (disease present), and treatment response using Pearson correlation analysis and a neural network model, to predict treatment outcomes in patients with coexisting SS and RA.

    METHODS: A cohort of 56 female patients aged 30-73 was analyzed. Descriptive statistics provided an overview of key variables, followed by Pearson correlation analysis to assess relationships between age, ANA, RF, RA, and treatment response. A neural network model was developed to predict treatment response based on age, ANA, and RF levels, using a training-to-testing split of 81.3 % and 18.8 %, respectively.

    RESULTS: The Pearson correlation analysis revealed a significant positive correlation between age and ANA levels (r = .541, p = 0.031), though no significant correlations were found between age, RF, RA, and treatment response. The neural network model achieved an accuracy of 92.3 % during training and 100 % accuracy during testing for most treatment categories. However, the model struggled to accurately distinguish between certain classes, particularly treatment categories 1 and 3.

    CONCLUSION: Age showed a significant correlation with ANA levels, indicating that older patients may have elevated ANA. The neural network model demonstrated strong predictive power for treatment response, although further refinement is needed to improve its ability to distinguish between all response categories. These findings suggest that machine learning models could enhance personalized treatment strategies for patients with SS and RA, but additional validation with larger datasets is required.

  2. Fulltext Healthcare Professional in the Loop (HPIL): Classification of Standard and Oral Cancer-Causing Anomalous Regions of Oral Cavity Using Textural Analysis Technique in Autofluorescence Imaging
    Awais M, Ghayvat H, Krishnan Pandarathodiyil A, Nabillah Ghani WM, Ramanathan A, Pandya S, et al.
    Sensors (Basel), 2020 Oct 12;20(20).
    PMID: 33053886 DOI: 10.3390/s20205780
    Oral mucosal lesions (OML) and oral potentially malignant disorders (OPMDs) have been identified as having the potential to transform into oral squamous cell carcinoma (OSCC). This research focuses on the human-in-the-loop-system named Healthcare Professionals in the Loop (HPIL) to support diagnosis through an advanced machine learning procedure. HPIL is a novel system approach based on the textural pattern of OML and OPMDs (anomalous regions) to differentiate them from standard regions of the oral cavity by using autofluorescence imaging. An innovative method based on pre-processing, e.g., the Deriche-Canny edge detector and circular Hough transform (CHT); a post-processing textural analysis approach using the gray-level co-occurrence matrix (GLCM); and a feature selection algorithm (linear discriminant analysis (LDA)), followed by k-nearest neighbor (KNN) to classify OPMDs and the standard region, is proposed in this paper. The accuracy, sensitivity, and specificity in differentiating between standard and anomalous regions of the oral cavity are 83%, 85%, and 84%, respectively. The performance evaluation was plotted through the receiver operating characteristics of periodontist diagnosis with the HPIL system and without the system. This method of classifying OML and OPMD areas may help the dental specialist to identify anomalous regions for performing their biopsies more efficiently to predict the histological diagnosis of epithelial dysplasia.
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