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Si us plau utilitzeu sempre aquest identificador per citar o enllaçar aquest document: https://hdl.handle.net/2445/219970
End-to-End AI Solutions for Capsule Endoscopy: Enhancing Efficiency and Accuracy in Gastrointestinal Diagnostics
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[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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GILABERT ROCA, Pere. End-to-End AI Solutions for Capsule Endoscopy: Enhancing Efficiency and Accuracy in Gastrointestinal Diagnostics. [consulta: 30 de novembre de 2025]. [Disponible a: https://hdl.handle.net/2445/219970]