Development and Evaluation of Artificial Intelligence Algorithms for Automated Detection of Pathologies in Radiographic Images

Thesis Abstract

Abstract
This thesis presents the research findings on the development and evaluation of artificial intelligence (AI) algorithms for automated detection of pathologies in radiographic images. The advancement of AI technology has opened up new possibilities in the field of radiography, offering the potential for more accurate and efficient diagnosis of medical conditions. The primary aim of this study was to investigate the effectiveness of AI algorithms in detecting various pathologies in radiographic images, with a focus on improving diagnostic accuracy and reducing the workload of radiographers and clinicians. The research methodology involved the collection of a diverse dataset of radiographic images encompassing different types of pathologies, including fractures, tumors, pneumonia, and more. This dataset was used to train and validate the AI algorithms, which were developed using deep learning techniques such as convolutional neural networks (CNNs). The study evaluated the performance of the AI algorithms in terms of sensitivity, specificity, accuracy, and speed of detection. The literature review section provided a comprehensive overview of existing research in the field of AI applications in radiography, highlighting the strengths and limitations of previous studies. The research methodology chapter detailed the steps involved in dataset collection, preprocessing, algorithm development, and evaluation metrics. The findings chapter presented the results of the study, including the performance metrics of the AI algorithms on the test dataset and comparisons with manual radiologist readings. The discussion of findings chapter provided an in-depth analysis of the results, discussing the implications of the findings for clinical practice and future research directions. The study highlighted the potential benefits of incorporating AI algorithms in radiography, including improved diagnostic accuracy, reduced human error, and enhanced workflow efficiency. The conclusion chapter summarized the key findings of the study and offered recommendations for further research and practical implementation of AI technologies in radiography. Overall, this thesis contributes to the growing body of knowledge on the application of AI in radiography and provides valuable insights into the potential of AI algorithms for automated detection of pathologies in radiographic images. The study demonstrates the feasibility and effectiveness of using AI technology to enhance diagnostic capabilities in medical imaging, paving the way for future advancements in the field of radiography.