Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization
Colorectal cancer (CRC) is the third most common cancer in the world. Colonoscopy has contributed significantly to reduction of incidence and mortality of CRC. Integration of artificial intelligence (AI) into colonoscopy practice has addressed the various shortcomings of screening colonoscopies. AI-...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Thieme Medical and Scientific Publishers Pvt. Ltd.
2023-12-01
|
| Series: | Journal of Digestive Endoscopy |
| Subjects: | |
| Online Access: | http://www.thieme-connect.de/DOI/DOI?10.1055/s-0043-1777330 |
| _version_ | 1797373159411810304 |
|---|---|
| author | Shivaraj Afzalpurkar Mahesh K. Goenka Rakesh Kochhar |
| author_facet | Shivaraj Afzalpurkar Mahesh K. Goenka Rakesh Kochhar |
| author_sort | Shivaraj Afzalpurkar |
| collection | DOAJ |
| description | Colorectal cancer (CRC) is the third most common cancer in the world. Colonoscopy has contributed significantly to reduction of incidence and mortality of CRC. Integration of artificial intelligence (AI) into colonoscopy practice has addressed the various shortcomings of screening colonoscopies. AI-assisted colonoscopy will help in real-time recognition of type of polyp with probable histology. This will not only save time but will also help to mitigate human errors. Computer-aided detection and computer-aided characterization are two applications of AI, which are being studied extensively with a goal of improvement of polyp and adenoma detection rates. Several studies are being conducted across the globe, which either involve simple decision-making algorithms or complex patterns through neural networks, which imitate the human brain. Most data are collected retrospectively and the research is limited to single-center studies, which might have bias. Therefore, the future research on AI in colonoscopy should aim to develop more sophisticated convolutional neural network and deep learning models that will help to standardize the practice and ensure the same degree of accuracy with all the colonoscopies, irrespective of experience of performing endoscopists. In this review, we will take a closer look at the current state of AI and its integration into the field of colonoscopy. |
| first_indexed | 2024-03-08T18:46:18Z |
| format | Article |
| id | doaj.art-b4a64fc777db455ab0a117916bdfee1a |
| institution | Directory Open Access Journal |
| issn | 0976-5042 0976-5050 |
| language | English |
| last_indexed | 2024-03-08T18:46:18Z |
| publishDate | 2023-12-01 |
| publisher | Thieme Medical and Scientific Publishers Pvt. Ltd. |
| record_format | Article |
| series | Journal of Digestive Endoscopy |
| spelling | doaj.art-b4a64fc777db455ab0a117916bdfee1a2023-12-28T23:34:36ZengThieme Medical and Scientific Publishers Pvt. Ltd.Journal of Digestive Endoscopy0976-50420976-50502023-12-01140422122610.1055/s-0043-1777330Impact of Artificial Intelligence on Colorectal Polyp Detection and CharacterizationShivaraj Afzalpurkar0https://orcid.org/0000-0002-4810-5165Mahesh K. Goenka1Rakesh Kochhar2Department of Gastroenterology, Nanjappa Multi-speciality Hospitals, Davangere, Karnataka, IndiaDirector and Head, Institute of Gastrosciences and Liver, Apollo Multi-speciality Hospitals, Kolkata, West Bengal, IndiaDepartment of Gastroenterology, NIMS University, Jaipur, IndiaColorectal cancer (CRC) is the third most common cancer in the world. Colonoscopy has contributed significantly to reduction of incidence and mortality of CRC. Integration of artificial intelligence (AI) into colonoscopy practice has addressed the various shortcomings of screening colonoscopies. AI-assisted colonoscopy will help in real-time recognition of type of polyp with probable histology. This will not only save time but will also help to mitigate human errors. Computer-aided detection and computer-aided characterization are two applications of AI, which are being studied extensively with a goal of improvement of polyp and adenoma detection rates. Several studies are being conducted across the globe, which either involve simple decision-making algorithms or complex patterns through neural networks, which imitate the human brain. Most data are collected retrospectively and the research is limited to single-center studies, which might have bias. Therefore, the future research on AI in colonoscopy should aim to develop more sophisticated convolutional neural network and deep learning models that will help to standardize the practice and ensure the same degree of accuracy with all the colonoscopies, irrespective of experience of performing endoscopists. In this review, we will take a closer look at the current state of AI and its integration into the field of colonoscopy.http://www.thieme-connect.de/DOI/DOI?10.1055/s-0043-1777330artificial intelligencecolonoscopycomputer-aided detectioncomputer-aided diagnosis |
| spellingShingle | Shivaraj Afzalpurkar Mahesh K. Goenka Rakesh Kochhar Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization Journal of Digestive Endoscopy artificial intelligence colonoscopy computer-aided detection computer-aided diagnosis |
| title | Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization |
| title_full | Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization |
| title_fullStr | Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization |
| title_full_unstemmed | Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization |
| title_short | Impact of Artificial Intelligence on Colorectal Polyp Detection and Characterization |
| title_sort | impact of artificial intelligence on colorectal polyp detection and characterization |
| topic | artificial intelligence colonoscopy computer-aided detection computer-aided diagnosis |
| url | http://www.thieme-connect.de/DOI/DOI?10.1055/s-0043-1777330 |
| work_keys_str_mv | AT shivarajafzalpurkar impactofartificialintelligenceoncolorectalpolypdetectionandcharacterization AT maheshkgoenka impactofartificialintelligenceoncolorectalpolypdetectionandcharacterization AT rakeshkochhar impactofartificialintelligenceoncolorectalpolypdetectionandcharacterization |