Dei, Neri Niccolò;
Mazomenos, Evangelos B;
Zhang, Shuai;
Bano, Sophia;
Montiel, Josè MM;
Stoyanov, Danail;
Ciuti, Gastone;
(2025)
Adjunct Tools for Colonoscopy Enhancement: a Comprehensive Review.
IEEE Transactions on Medical Robotics and Bionics, Proceedings
10.1109/TMRB.2025.3573024.
(In press).
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Abstract
Colonoscopy is considered the gold standard for detecting and diagnosing colorectal cancer (CRC), which is the second most common cause of cancer-related deaths worldwide. While colonoscopy is generally safe and effective at reducing CRC mortality, lesions can be missed during procedures, with adverse impacts on the patient. Latest innovations in hardware and software led to the development of adjunct tools for complementing standard colonoscopy to ensure optimal outcomes. Such tools aim to enhance the detection of lesions, standardize procedures, enhance safety, and minimize discomfort. Ultimately, they contribute to reducing the morbidity and mortality rates associated with CRC. This survey comprehensively explores both clinically tested and emerging advanced hardware and software adjunct tools, categorizing them based on their role in targeting three clinical challenges: mucosal visualization, lesion detection and classification, and navigation and procedure assessment. Moreover, this analysis allows exploring synergistic strategies for the future of the practice, with a focus on the promising role of AI-embedded robotic technologies.
| Type: | Article |
|---|---|
| Title: | Adjunct Tools for Colonoscopy Enhancement: a Comprehensive Review |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/TMRB.2025.3573024 |
| Publisher version: | https://doi.org/10.1109/TMRB.2025.3573024 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Colonoscopy, Hardware, Lesions, Endoscopes, Visualization, Software, Navigation, Cameras, Biomedical optical imaging, Surveys |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10209529 |
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