Innovations In Non-Invasive Liver Disease Diagnostics (E.G., Fatty Liver, Cirrhosis)
DOI:
https://doi.org/10.63682/jns.v14i32S.8039Keywords:
Non-invasive diagnostics, liver disease, fatty liver, cirrhosis, Fibro Scan, usage of technology, Cronbach, Alpha, correlation Pearson, regression analysisAbstract
Background: Fatty liver disease (FLD) and cirrhosis are some of the major diseases of the liver and a major cause of morbidity. Liver biopsy, despite being a useful method of diagnosing the disease, is an invasive technique, expensive, and accompanied by risk and pain. Non-invasive diagnostic methods that are under development today, like FibroScan, Shear Wave Elastography (SWE), Magnetic Resonance Elastography (MRE), and diagnostics with the use of biomarkers, are good options. Nonetheless, they are not evenly adopted among different stakeholders and are assumed to be successful.
Objective: Due to this purpose, this research study will determine the level of awareness, images, and usage of non-invasive technologies for liver disease diagnosis among medical workers, investigators, and patients. It also explores the correlation between the major determinant measures, which include trust in technology, perceived effectiveness, cost concern, and availability.
Methods: A Quantitative cross-sectional survey consisting of 250 respondents spread across five provinces of Pakistan was used. Data on demographic factors, awareness, perceived effectiveness, practicing behaviour, and factors were obtained with the help of a structured questionnaire. Some of the statistical analyses used included normality testing, reliability testing (Cronbach’s Alpha), construct validity through Principal component analysis (PCA), Pearson correlation, and multiple linear regression.
Results: The findings according to the data indicated non-normality in the data of age and normality in the data of years of experience. The internal consistency was good, and Cronbach's Alpha was 0.871, which demonstrated good reliability of the survey instrument. PCA advocated construct validity, which had a cumulative variance above 70 percent. Pearson's correlation revealed a positive correlation among all the important variables. The predictors of adoption of diagnostic tools were found to be cost concern, trust in technology, and awareness by multiple regression analysis. The positive relationship.
between R and adopting behaviour was 61.4 percent (R 2 = 0.614).
Conclusion: Liver diseases are being tested in terms of non-invasive diagnoses and have begun to achieve popularity. The widespread spread of innovations is highly dependent on aspects that include cost, accessibility, trust in the technology, and awareness. They can be largely improved by offering better training, reducing the cost of implementation, and exposure to technology. The paper presents utilitarian knowledge in healthcare policy formulation, clinical decision making, and diagnostic innovation research..
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Alqahtani, S. A., & Schattenberg, J. M. (2021). Nonalcoholic fatty liver disease: use of diagnostic biomarkers and modalities in clinical practice. Expert Review of Molecular Diagnostics, 21(10), 1065-1078.
Anstee, Q. M., Castera, L., & Loomba, R. (2022). Impact of non-invasive biomarkers on hepatology practice: Past, present, and future. Journal of hepatology, 76(6), 1362-1378.
Anty, R., Morvan, M., Le Corvec, M., Canivet, C. M., Patouraux, S., Gugenheim, J., Bonnafous, S., Bailly-Maitre, B., Sire, O., & Tariel, H. (2019). The mid-infrared spectroscopy: A novel non-invasive diagnostic tool for NASH diagnosis in severe obesity. JHEP Reports, 1(5), 361-368.
Aravind, A., Bahirvani, A. G., Quiambao, R., & Gonzalo, T. (2020). Machine learning technology for evaluation of liver fibrosis, inflammation activity, and steatosis (LIVERFAStTM). Journal of Intelligent Learning Systems and Applications, 12(2), 31-49.
Bennett, L., Purssell, H., Street, O., Piper Hanley, K., Morling, J. R., Hanley, N. A., Athwal, V., & Guha, I. N. (2022). Health technology adoption in liver disease: Innovative use of data science solutions for early disease detection. Frontiers in digital health, 4, 737729.
Boon-Yasidhi, P., & Karnsakul, W. (2024). Non-Invasive Biomarkers and Breath Tests for Diagnosis and Monitoring of Chronic Liver Diseases. Diagnostics, 15(1), 68.
Boursier, J., Roux, M., Costentin, C., Chaigneau, J., Fournier-Poizat, C., Trylesinski, A., Canivet, C. M., Michalak, S., Le Bail, B., & Paradis, V. (2023). Practical diagnosis of cirrhosis in non-alcoholic fatty liver disease using currently available non-invasive fibrosis tests. Nature Communications, 14(1), 5219.
Cholankeril, G., Kramer, J. R., Chu, J., Yu, X., Balakrishnan, M., Li, L., El-Serag, H. B., & Kanwal, F. (2023). Longitudinal changes in fibrosis markers are associated with the risk of cirrhosis and hepatocellular carcinoma in non-alcoholic fatty liver disease. Journal of hepatology, 78(3), 493-500.
Di Sessa, A., Cirillo, G., Guarino, S., Marzuillo, P., & Miraglia del Giudice, E. (2019). Pediatric non-alcoholic fatty liver disease: Current perspectives on diagnosis and management. Pediatric Health, Medicine and Therapeutics, 89-97.
Grander, C., Grabherr, F., & Tilg, H. (2023). Non-alcoholic fatty liver disease: pathophysiological concepts and treatment options. Cardiovascular research, 119(9), 1787-1798.
Han, A., Byra, M., Heba, E., Andre, M. P., Erdman Jr, J. W., Loomba, R., Sirlin, C. B., & O’Brien Jr, W. D. (2020). Noninvasive diagnosis of nonalcoholic fatty liver disease and quantification of liver fat with radiofrequency ultrasound data using one-dimensional convolutional neural networks. Radiology, 295(2), 342-350.
Heyens, L. J., Busschots, D., Koek, G. H., Robaeys, G., & Francque, S. (2021). Liver fibrosis in non-alcoholic fatty liver disease: from liver biopsy to non-invasive biomarkers in diagnosis and treatment. Frontiers in medicine, 8, 615978.
Hu, H., Han, Y., Cao, C., & He, Y. (2022). The triglyceride glucose-body mass index: a non-invasive index that identifies non-alcoholic fatty liver disease in the general Japanese population. Journal of Translational Medicine, 20(1), 398.
Huang, W., Peng, Y., & Kang, L. (2024). Advancements of non‐invasive imaging technologies for the diagnosis and staging of liver fibrosis: Present and future. View, 5(4), 20240010.
Kupčová, V., Fedelešová, M., Bulas, J., Kozmonová, P., & Turecký, L. (2019). Overview of the pathogenesis, genetic, and non-invasive clinical, biochemical, and scoring methods in the assessment of NAFLD. International journal of environmental research and public health, 16(19), 3570.
Lonardo, A. (2023). Principles of risk stratification in nonalcoholic fatty liver disease. A narrative review emphasizing non-invasive strategies. Exploration of Digestive Diseases, 2(4), 188-201.
Long, M. T., Gandhi, S., & Loomba, R. (2020). Advances in non-invasive biomarkers for the diagnosis and monitoring of non-alcoholic fatty liver disease. Metabolism, 111, 154259.
Miele, L., Zocco, M. A., Pizzolante, F., De Matthaeis, N., Ainora, M. E., Liguori, A., Gasbarrini, A., Grieco, A., & Rapaccini, G. (2020). Use of imaging techniques for non-invasive assessment in the diagnosis and staging of non-alcoholic fatty liver disease. Metabolism, 112, 154355.
Pennisi, G., Enea, M., Falco, V., Aithal, G. P., Palaniyappan, N., Yilmaz, Y., Boursier, J., Cassinotto, C., De Lédinghen, V., & Chan, W. K. (2023). Noninvasive assessment of liver disease severity in patients with nonalcoholic fatty liver disease (NAFLD) and type 2 diabetes. Hepatology, 78(1), 195-211.
Piciotti, R., Longo, M., Agresta, A., Paolini, E., Cespiati, A., Meroni, M., & Dongiovanni, P. (2022). Old-fashioned and newly discovered biomarkers: the future of NAFLD-related HCC screening and monitoring. Hepatoma Res, 8(37), 10.20517.
Pirmoazen, A. M., Khurana, A., El Kaffas, A., & Kamaya, A. (2020). Quantitative ultrasound approaches for diagnosis and monitoring hepatic steatosis in nonalcoholic fatty liver disease. Theranostics, 10(9), 4277.
Rónaszéki, A. D. (2024). NEW QUANTITATIVE ULTRASOUND BIOMARKERS FOR THE DIAGNOSIS OF CHRONIC HEPATITIS AND FATTY LIVER DISEASE
Srivastava, A., Gailer, R., Tanwar, S., Trembling, P., Parkes, J., Rodger, A., Suri, D., Thorburn, D., Sennett, K., & Morgan, S. (2019). Prospective evaluation of a primary care referral pathway for patients with non-alcoholic fatty liver disease. Journal of hepatology, 71(2), 371-378.
Wazir, H., Abid, M., Essani, B., Saeed, H., Khan, M. A., Nasrullah, F., Qadeer, U., Khalid, A., Varrassi, G., & Muzammil, M. A. (2023). Diagnosis and treatment of liver disease: current trends and future directions. Cureus, 15(12).
Zhou, Y., Yang, X., Wei, X., Zhang, S.-S., & Yan, M. (2024). Recent progress in small-molecule fluorescent probes for imaging and diagnosis of non-alcoholic fatty liver disease. Coordination Chemistry Reviews, 513, 215864.
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