End-to-End AI Solutions for Capsule Endoscopy: Enhancing Efficiency and Accuracy in Gastrointestinal Diagnostics
[eng] Artificial Intelligence (AI) models are fundamentally transforming the way clinicians carry out their daily tasks. By streamlining various processes, AI offers a more robust and consistent method for reviewing medical procedures. This thesis is dedicated to the development of AI applications f...
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| Tipo de recurso: | tesis doctoral |
| Estado: | Versión publicada |
| Fecha de publicación: | 2025 |
| País: | España |
| Institución: | CBUC, CESCA |
| Repositorio: | TDR. Tesis Doctorales en Red |
| OAI Identifier: | oai:www.tdx.cat:10803/694089 |
| Acceso en línea: | http://hdl.handle.net/10803/694089 |
| Access Level: | acceso abierto |
| Palabra clave: | Càpsula endoscòpica Cápsula endoscópica Capsule endoscopy Intel·ligència artificial Inteligencia artificial Artificial intelligence Aprenentatge profund Aprendizaje profundo Deep learning (Machine learning) Informàtica mèdica Informática médica Medical informatics Ciències Experimentals i Matemàtiques 004 |
| Sumario: | [eng] Artificial Intelligence (AI) models are fundamentally transforming the way clinicians carry out their daily tasks. By streamlining various processes, AI offers a more robust and consistent method for reviewing medical procedures. This thesis is dedicated to the development of AI applications for Capsule Endoscopy (CE), a small device that patients swallow, which is equipped with both a light and a camera to traverse the digestive system, capturing detailed images of internal organs. Once these images are captured, physicians are tasked with meticulously reviewing an extensive number of frames to identify potential pathologies, a process that is both time-consuming and tedious. In this thesis, we aim to enhance the entire review pipeline from end to end, providing support to physicians at multiple stages of the process. These stages include data collection, data labeling, assessing the usability of the videos (particularly in determining whether intestinal residues may hinder the process), identifying the entry and exit points of the small and large intestines, and most crucially, detecting polyps as early indicators of Colorectal Cancer (CRC). By employing advanced techniques such as Active Learning (AL) for data labeling and Vision Transformer (ViT) for polyp detection, we significantly improve upon existing systems in the literature, achieving state-of-the-art results. Additionally, the integration of AI into CE holds the promise of not only improving diagnostic accuracy but also reducing the workload for clinicians, allowing them to focus on more complex cases. This technological advancement has the potential to revolutionize gastrointestinal diagnostics, leading to earlier detection of diseases and, ultimately, better patient outcomes. Furthermore, this thesis led to the initiation of two clinical studies. The first was a controlled study that evaluated the performance of the polyp detection application. The second is a larger study involving over 600 patients, testing an enhanced version of the application, which is currently under development. |
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