Thesis Abstract

Abstract
The field of radiography has seen significant advancements with the integration of artificial intelligence (AI) technologies. This thesis explores the application of AI in radiography image analysis, aiming to enhance diagnostic accuracy, efficiency, and patient care outcomes. The study begins with a comprehensive review of relevant literature on the use of AI in radiography, highlighting key trends, challenges, and opportunities. Subsequently, the research methodology section outlines the approach taken to investigate the impact of AI on radiography image analysis, including data collection, analysis techniques, and evaluation methods. The findings of this study provide valuable insights into the effectiveness of AI algorithms in interpreting radiographic images, identifying abnormalities, and assisting radiographers in making accurate diagnoses. Through a detailed discussion of the results, the thesis underscores the potential benefits of AI integration in radiography, such as improved workflow efficiency, reduced interpretation errors, and enhanced patient outcomes. Moreover, the limitations and challenges associated with AI implementation in radiography are critically examined, offering recommendations for future research and practice. In conclusion, this thesis emphasizes the significance of leveraging AI technologies in radiography image analysis to augment the capabilities of healthcare professionals and optimize patient care delivery. The study contributes to the growing body of knowledge on AI applications in radiography, shedding light on the transformative potential of these technologies in the field. Ultimately, the findings underscore the importance of ongoing research and innovation in leveraging AI to advance radiography practice and improve patient outcomes.

Thesis Overview

The project titled "Application of Artificial Intelligence in Radiography Image Analysis" aims to explore the integration of artificial intelligence (AI) technologies in the field of radiography to enhance image analysis processes. Radiography plays a crucial role in the diagnosis and treatment of various medical conditions by producing detailed images of the internal structures of the human body. However, the interpretation of radiographic images can be complex and time-consuming, requiring a high level of expertise from radiologists and healthcare professionals. The integration of AI in radiography image analysis has the potential to revolutionize the field by providing automated tools for faster and more accurate interpretation of radiographic images. AI algorithms can be trained to recognize patterns and abnormalities in medical images, assisting radiologists in making more precise diagnoses and treatment decisions. This project will focus on developing and evaluating AI-based tools specifically tailored for radiography image analysis, with the goal of improving diagnostic accuracy and efficiency in clinical practice. The research will involve a comprehensive review of existing literature on the application of AI in radiography and medical imaging. This review will provide insights into the current state of AI technologies in radiography, including their strengths, limitations, and potential applications. The project will also include the design and development of AI algorithms for radiography image analysis, utilizing machine learning and deep learning techniques to train models on large datasets of radiographic images. The methodology will involve collecting and preprocessing radiographic images from various sources, annotating the images for training AI models, and evaluating the performance of the developed algorithms using metrics such as sensitivity, specificity, and accuracy. The research will also involve collaboration with healthcare professionals and radiologists to gather feedback on the usability and clinical relevance of the AI-based tools. The findings of this project are expected to demonstrate the efficacy of AI in radiography image analysis, showcasing the potential benefits of using AI technologies to improve diagnostic outcomes in clinical practice. The project outcomes will contribute to advancing the field of radiography and medical imaging by introducing innovative solutions for image interpretation and decision-making. Overall, the research aims to bridge the gap between AI technologies and radiography practice, paving the way for more efficient and accurate diagnostic processes in healthcare settings.