Utilizing Artificial Intelligence for Automated Detection of Pathologies in Radiographic Images

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the utilization of Artificial Intelligence (AI) for the automated detection of pathologies in radiographic images. The field of radiography plays a crucial role in the diagnosis and treatment of various medical conditions, and the accurate interpretation of radiographic images is essential for effective healthcare delivery. However, the process of manual interpretation by radiologists can be time-consuming and prone to errors. The integration of AI technologies offers a promising solution to enhance the efficiency and accuracy of pathology detection in radiographic images. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, the problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The significance of this study lies in the potential to revolutionize the field of radiography by leveraging AI algorithms to automate the detection of pathologies, thus improving diagnostic accuracy and reducing the burden on healthcare professionals. Chapter 2 comprises a detailed literature review that explores existing research on AI applications in radiography and pathology detection. The review covers ten key areas, including the evolution of AI in healthcare, the role of AI in radiographic image analysis, challenges and opportunities in automated pathology detection, and current trends in AI-assisted diagnosis. Chapter 3 outlines the research methodology employed in this study, encompassing eight key components such as data collection, preprocessing techniques, AI model selection, training and validation processes, performance evaluation metrics, and ethical considerations. The methodology aims to establish a robust framework for developing and evaluating AI algorithms for automated pathology detection in radiographic images. Chapter 4 presents an in-depth discussion of the findings obtained from the implementation of AI models for pathology detection in radiographic images. The analysis includes the performance metrics of the developed AI algorithms, comparison with traditional manual interpretation methods, identification of strengths and limitations, and insights into the potential clinical implications of the automated detection system. Finally, Chapter 5 offers a comprehensive conclusion and summary of the research thesis. The findings of this study underscore the efficacy of utilizing AI for automated pathology detection in radiographic images, showcasing the potential to enhance diagnostic accuracy, reduce interpretation time, and improve patient outcomes. The thesis concludes with recommendations for further research and practical implementation of AI technologies in radiography to advance healthcare practices. In conclusion, this thesis contributes to the growing body of knowledge on AI applications in radiography and pathology detection, emphasizing the transformative potential of AI technologies in revolutionizing healthcare practices. The integration of AI for automated pathology detection in radiographic images holds great promise for enhancing diagnostic accuracy, optimizing healthcare workflows, and ultimately improving patient care outcomes.