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Author: search.filters.author.Jamrasnarodom, JirakornSubject: search.filters.subject.Artificial intelligence

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    Quantitative Ultrasound Assessment of Liver Fat Using Deep Learning and Clinical Data Integration
    (2025-12-01)
    Jamrasnarodom, Jirakorn
    ;
    Apiparakoon, Terapap
    ;
    Marukatat, Sanparith
    ;
    Chaichuen, Oracha
    ;
    Sukchareon, Sasima
    Purpose: The increasing prevalence of Metabolic Dysfunction-Associated Steatosis Liver Disease (MASLD) highlights the need for effective assessment tools, particularly for the quantitative evaluation of hepatic steatosis. Given the limited availability of transient elastography (TE), especially in resource-limited settings, we aimed to develop an Artificial Intelligence (AI) model to quantify hepatic steatosis using conventional ultrasound, which is widely available in most healthcare facilities. Methods: Liver ultrasonographic images and Controlled Attenuation Parameter (CAP) scores obtained from TE were collected from patients between 2017 and 2023. A predictive model was developed by integrating YOLOv8 for image classification with Principal Component Analysis and Lasso regression to estimate CAP scores from the ultrasonographic images. The dataset was randomly divided into training (80%), validation (10%), and test (10%) sets. Baseline patient characteristics and laboratory data were also incorporated to enhance model performance. The model’s predictive ability was evaluated using the coefficient of determination (R²) and mean squared error (MSE). Results: A total of 1065 images from 352 patients were included. The initial model achieved an R² of 0.55 and an MSE of 1004.07. Subgroup analysis revealed that the right intercostal view yielded the best performance (R²=0.74, MSE = 637.99). After incorporating patient characteristics and laboratory data, the model’s performance improved significantly (R²=0.90, MSE = 245.79). Conclusion: The AI-assisted model showed promise for accessible and non-invasive assessment of hepatic steatosis, particularly when using the right intercostal view and supplemental clinical data. Further validation is warranted to improve its accuracy and generalizability.
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    Optimizing colorectal polyp detection and localization: Impact of RGB color adjustment on CNN performance
    (2025-06-01)
    Jamrasnarodom, Jirakorn
    ;
    Rajborirug, Pharuj
    ;
    Pisespongsa, Pises
    ;
    Pasupa, Kitsuchart
    Colorectal cancer, arising from adenomatous polyps, is a leading cause of cancer-related mortality, making early detection and removal crucial for preventing cancer progression. Machine learning is increasingly used to enhance polyp detection during colonoscopy, the gold standard for colorectal cancer screening, despite its operator-dependent miss rates. This study explores the impact of RGB color adjustment on Convolutional Neural Network (CNN) models for improving polyp detection and localization in colonoscopic images. Using datasets from Harvard Dataverse for training and internal validation, and LDPolypVideo-Benchmark for external validation, RGB color adjustments were applied, and YOLOv8s was used to develop models. Bayesian optimization identified the best RGB adjustments, with performance assessed using mean average precision (mAP) and F<inf>1</inf>-scores. Results showed that RGB adjustment with 1.0 R-1.0 G-0.8 B improved polyp detection, achieving an mAP of 0.777 and an F<inf>1</inf>-score of 0.720 on internal test sets, and localization performance with an F<inf>1</inf>-score of 0.883 on adjusted images. External validation showed improvement but with a lower F<inf>1</inf>-score of 0.556. While RGB adjustments improved performance in our study, their generalizability to diverse datasets and clinical settings has yet to be validated. Thus, although RGB color adjustment enhances CNN model performance for detecting and localizing colorectal polyps, further research is needed to verify these improvements across diverse datasets and clinical settings. • RGB Color Adjustment: Applied RGB color adjustments to colonoscopic images to enhance the performance of Convolutional Neural Network (CNN) models. • Model Development: Used YOLOv8s for polyp detection and localization, with Bayesian optimization to identify the best RGB adjustments. • Performance Evaluation: Assessed model performance using mAP and F<inf>1</inf>-scores on both internal and external validation datasets.