International Journal For Multidisciplinary Research

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AI-Assisted Genotype Analysis of Hepatitis Viruses: A Systematic Review on Precision Therapy and Sequencing Innovations

Author(s) Ashish Kumar, Sooraj Kumar, Samesh Kumar, Hamza Ahmed Qureshi, JouvanySarofeem Naguib
Country Pakistan
Abstract Abstract
Background: The Hepatitis B virus, as well as the Hepatitis C virus worldwide, are the leading causes of morbidity and mortality due to chronic liver disease, cirrhosis, and hepatocellular cancer. With the evolution of direct-acting antivirals (DAAs) and nucleoside analogs, appropriate genotyping is critical for the design of individualized treatment approaches. Traditional approaches to genotyping are not fit for purpose since they cannot be scaled up or cope with the problem of emerging resistance. These are some of the objective problems that Artificial Intelligence (AI) with machine and deep learning capabilities has addressed.
Methods:
A systematic review was carried out following the PRISMA 2020 guidelines. The authors searched for relevant studies in PubMed and Google Scholar using structured search strings. A total of 1200 papers were screened, and 30 were included according to the inclusion criteria. The developed data collection form contained information on AIMs, treatment outcome measures, and practice. In this way, the studies were combined to determine the role of AI in hepatitis genotyping and the prospects of personalized medicine.
Results: When it came to genotyping hepatitis viruses for the existing and especially new and rare genotypes like HCV genotype-8, AI-based models could perform better in accuracy and the scalability of the measurement. Machine learning techniques like random forests and support vector machines gave accuracy rates above 90%. But capturing complex genomic imaging like patterns of genome sequences was a deep learning model-based convolutional neural network or long short-term memory network which went beyond imaging. Faster diagnostics, improved detection of resistance-associated mutations and therapy optimization were all enhanced due to AI methods.
Conclusion: There are advances in hepatitis genotyping because of the adoption of AI in this process compared to the classical methods, which come with limitations and cannot provide such accurate, reliable and timely diagnosis. Hence, it helps in planning treatment strategies for patients, helps in real-time application and even supports policies regarding health on a global scale. Nevertheless, factors like patient data protection, relative bias in agglomerative training data, and interpretability remain to be resolved. To tap the full potential of AI, future studies should emphasize multi-omics, federated learning and cost-effective diagnostics.
Keywords Artificial Intelligence (AI), Genotyping, Hepatitis B Virus (HBV), Hepatitis C Virus (HCV), Sequencing Data, Hepatitis Diagnostics, and Therapy Optimization.
Field Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In Volume 6, Issue 6, November-December 2024
Published On 2024-12-10
Cite This AI-Assisted Genotype Analysis of Hepatitis Viruses: A Systematic Review on Precision Therapy and Sequencing Innovations - Ashish Kumar, Sooraj Kumar, Samesh Kumar, Hamza Ahmed Qureshi, JouvanySarofeem Naguib - IJFMR Volume 6, Issue 6, November-December 2024. DOI 10.36948/ijfmr.2024.v06i06.32058
DOI https://doi.org/10.36948/ijfmr.2024.v06i06.32058
Short DOI https://doi.org/g8vgkc
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