Project Abstract

Abstract
The integration of artificial intelligence (AI) technologies in healthcare has revolutionized medical imaging, particularly in radiography. This research project focuses on the utilization of AI for automated detection of pathologies in radiographic imaging, aiming to enhance diagnostic accuracy, efficiency, and patient outcomes. The study addresses the increasing demand for advanced image analysis tools in radiology departments to cope with the growing volume of imaging studies and the need for timely and accurate diagnoses. Chapter one introduces the research topic, provides the background of the study, states the problem statement, outlines the objectives, discusses the limitations and scope of the study, highlights the significance of the research, and defines key terms relevant to the study. The chapter sets the foundation for understanding the importance of leveraging AI in radiographic imaging for pathology detection. Chapter two presents a comprehensive literature review encompassing ten key areas related to AI applications in radiography and automated pathology detection. This section reviews existing research, methodologies, and technologies employed in similar studies, providing a critical analysis of the current state of the field and identifying gaps that the present research seeks to address. Chapter three details the research methodology, including data collection methods, image preprocessing techniques, AI algorithms utilized for pathology detection, validation strategies, and performance evaluation metrics. This chapter outlines the systematic approach taken to train and test the AI model, ensuring robustness, accuracy, and reliability in detecting various pathologies in radiographic images. Chapter four presents a detailed discussion of the research findings, focusing on the performance of the AI model in detecting different types of pathologies, comparing results with ground truth annotations, and analyzing the strengths and limitations of the developed system. This chapter critically evaluates the effectiveness of AI in automating pathology detection tasks, highlighting the potential benefits and challenges in clinical implementation. Chapter five concludes the research project by summarizing the key findings, discussing the implications of the study for healthcare practice, highlighting future research directions, and offering recommendations for integrating AI-based pathology detection systems into routine clinical workflows. The conclusion emphasizes the transformative impact of AI technologies in radiographic imaging and underscores the importance of continuous innovation in enhancing diagnostic capabilities and patient care outcomes. In conclusion, this research project contributes to the growing body of knowledge on leveraging AI for automated detection of pathologies in radiographic imaging. By harnessing the power of AI algorithms, radiology departments can augment their diagnostic capabilities, improve workflow efficiency, and ultimately enhance patient care in the era of precision medicine.

Project Overview