The Role Of Digital Pathology And Artificial Intelligence In Radiological Techniques And Various Parasitic Disease In Grading Of Tumor Histopathology

Abstract

Introduction:

AI tools can alleviate radiologists' workload by automating mundane tasks, reducing burnout and enhancing care delivery in underserved areas. At the same time, they offer radiologists opportunities to focus on building stronger connections with patients and colleagues plays a crucial role in parasitology, assisting in diagnostics, drug discovery, and epidemiological analysis. It helps automate and improve the accuracy of parasite identification from microscopic images, analyzes large datasets to understand transmission patterns, and accelerates the development of new anti-parasitic therapies by identifying drug targets and predicting their efficacy Accurate grading of tumor histopathology is critical for determining patient prognosis [1]and guiding treatment decisions. Traditional pathology methods, while effective, are limited by interobserver variability and time constraints. Digital pathology (DP) combined with artificial intelligence (AI) offers innovative solutions for overcoming these challenges, providing faster and more consistent tumor grading. Objective: To investigate the role of digital pathology and AI in enhancing the accuracy and efficiency of tumor histopathology grading in a cohort of 85 cancer patients from Saudi Arabia. Methodology: Tumor samples from 85 patients were digitized and analyzed using AI-driven algorithms. Key parameters, including histopathological features and findings, were compared between traditional and AI-assisted methods. Results: AI-assisted grading demonstrated higher accuracy and consistency compared to manual grading, with an agreement rate of 92% with expert consensus. AI models significantly reduced diagnostic time by 40%, highlighting their potential to streamline pathology workflows. Conclusion: The integration of digital pathology and AI into clinical practice holds transformative potential for tumor histopathology grading in parasitic origion , ensuring higher diagnostic accuracy, reduced variability, and improved efficiency.