Use of Artificial Intelligence in Radiography Image Analysis

Thesis Abstract

Abstract
The field of radiography has seen significant advancements in recent years, with the integration of artificial intelligence (AI) playing a crucial role in enhancing image analysis processes. This thesis explores the use of AI in radiography image analysis, focusing on its applications, benefits, challenges, and future implications. The study begins with an introduction highlighting the increasing importance of AI in healthcare and the specific relevance of AI in radiography. The background of the study provides a comprehensive overview of the evolution of radiography and the emergence of AI technologies in this field. The problem statement identifies the existing limitations and inefficiencies in traditional radiography image analysis methods, underscoring the need for AI-driven solutions. The objectives of the study are outlined to investigate how AI can improve the accuracy, efficiency, and speed of radiography image analysis. The study also addresses the limitations inherent in the implementation of AI in radiography, such as data privacy concerns and ethical considerations. The scope of the study delves into the specific areas within radiography where AI can be applied, including image interpretation, diagnosis assistance, and treatment planning. The significance of the study lies in its potential to revolutionize radiography practices, leading to enhanced patient care and outcomes. The thesis structure is detailed to provide a roadmap for the reader, outlining the chapters and their respective contents. The literature review in Chapter Two explores existing research, studies, and technological developments related to AI in radiography image analysis. It covers topics such as machine learning algorithms, deep learning techniques, image segmentation, and feature extraction methods. The review also discusses the challenges and opportunities associated with integrating AI into radiography practices. Chapter Three focuses on the research methodology, detailing the data collection methods, AI models used, and evaluation criteria employed in the study. The chapter includes information on the dataset utilized, model training procedures, and performance metrics measured to assess the effectiveness of AI in radiography image analysis. Chapter Four presents a detailed discussion of the findings obtained from the study, analyzing the impact of AI on radiography image analysis in terms of accuracy, efficiency, and clinical outcomes. The chapter also addresses the challenges encountered during the implementation of AI and proposes recommendations for future research and practical applications. The conclusion and summary in Chapter Five encapsulate the key findings, implications, and contributions of the study. It highlights the potential of AI to transform radiography practices, improve diagnostic accuracy, and optimize patient care. The thesis concludes with suggestions for further research directions and practical considerations for integrating AI into radiography image analysis workflows. In conclusion, this thesis provides a comprehensive analysis of the use of artificial intelligence in radiography image analysis, offering insights into its applications, benefits, challenges, and future prospects. The findings of this study contribute to the ongoing dialogue on leveraging AI technologies to enhance healthcare practices and improve patient outcomes in the field of radiography.