<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers’ focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, different computational models as well as their benchmarks expressed through measurements such as positive prediction value and accuracy of detection, and the classification of colorectal polyps. Introducing computer-aided diagnostic tools can help clinicians obtain results with a high degree of confidence when performing colonoscopies. The growing field of machine learning in medicine will have a big impact on patient management in the future.</p></abstract>

Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening method that has a positive impact on the survival of patients with colorectal cancer. The association of colonoscopy with computer-aided diagnostic tools is currently under researchers’ focus, as various methods have already been proposed and show great potential for a better management of this disease. We performed a review of the literature and present a series of aspects, such as the basics of machine learning algorithms, different computational models as well as their benchmarks expressed through measurements such as positive prediction value and accuracy of detection, and the classification of colorectal polyps. Introducing computer-aided diagnostic tools can help clinicians obtain results with a high degree of confidence when performing colonoscopies. The growing field of machine learning in medicine will have a big impact on patient management in the future.

Computer-Aided Diagnosis in Colorectal Cancer: Current Concepts and Future Prospects

Department of Clinical Science-Internal Medicine

Department of Gastroenterology, County Emergency Clinical Hospital

Journal of Interdisciplinary Medicine

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Colorectal cancer is an important health issue, both in terms of the number of people affected and the associated costs. Colonoscopy is an important screening...

Computer-Aided Diagnosis in Colorectal Cancer: Current Concepts and Future Prospects

Online veröffentlicht: 08. Nov. 2017

Seitenbereich: 245 - 249

Eingereicht: 14. Juni 2017

Akzeptiert: 03. Juli 2017

DOI: https://doi.org/10.1515/jim-2017-0057

Schlüsselwörter
colorectal polyps, machine learning, computer-aided diagnosis, colonoscopy

© 2017 Andrei-Constantin Ioanovici et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Computer-Aided Diagnosis in Colorectal Cancer: Current Concepts and Future Prospects

Online veröffentlicht: 08. Nov. 2017

Seitenbereich: 245 - 249

Eingereicht: 14. Juni 2017

Akzeptiert: 03. Juli 2017

DOI: https://doi.org/10.1515/jim-2017-0057

Schlüsselwörtercolorectal polyps, machine learning, computer-aided diagnosis, colonoscopy

© 2017 Andrei-Constantin Ioanovici et al., published by De Gruyter Open

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
colorectal polyps, machine learning, computer-aided diagnosis, colonoscopy