Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps
Objectives: Colonoscopy is the gold standard for screening cancer and precancerous lesions in the large intestine. Recently, remarkable advances in artificial intelligence (AI) have led to the development of various computer-aided detection (CADe) systems for colonoscopy. This study aimed to evaluat...
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
The Japan Society of Coloproctology
2025-01-01
|
| Series: | Journal of the Anus, Rectum and Colon |
| Subjects: | |
| Online Access: | https://www.jstage.jst.go.jp/article/jarc/9/1/9_2024-057/_pdf/-char/en |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832584961888092160 |
|---|---|
| author | Yuya Hiratsuka Takashi Hisabe Kensei Ohtsu Tatsuhisa Yasaka Kazuhiro Takeda Masaki Miyaoka Yoichiro Ono Takao Kanemitsu Kentaro Imamura Teruyuki Takeda Satoshi Nimura Kenshi Yao |
| author_facet | Yuya Hiratsuka Takashi Hisabe Kensei Ohtsu Tatsuhisa Yasaka Kazuhiro Takeda Masaki Miyaoka Yoichiro Ono Takao Kanemitsu Kentaro Imamura Teruyuki Takeda Satoshi Nimura Kenshi Yao |
| author_sort | Yuya Hiratsuka |
| collection | DOAJ |
| description | Objectives: Colonoscopy is the gold standard for screening cancer and precancerous lesions in the large intestine. Recently, remarkable advances in artificial intelligence (AI) have led to the development of various computer-aided detection (CADe) systems for colonoscopy. This study aimed to evaluate the usefulness of AI for colonoscopy using CAD-EYEⓇ (Fujifilm, Tokyo, Japan) to calculate the adenoma miss rate (AMR).
Methods: This randomized, open-label, single-center, tandem study was conducted at Fukuoka University Chikushi Hospital from February 2022 to November 2022. Patients were randomly assigned to the CADe or non-CADe group. Immediately after the completion of the first endoscopy by an endoscopist, a new endoscopist was assigned to perform the second endoscopy. As a result, different endoscopists performed the examinations in a tandem fashion. A missed lesion was defined as a newly detected colorectal polyp by the second endoscopy. Finally, the AMR was compared between the two groups.
Results: The study population comprised 48 patients in the CADe group and 46 patients in the non-CADe group. The AMR was 17.4% in the CADe group and 30.3% in the non-CADe group. Therefore, the AMR in the CADe group was statistically significantly lower than that in the non-CADe group (P=0.009).
Conclusions: The application of CAD-EYEⓇ to colonoscopy reduced the AMR. Overall, CAD-EYEⓇ might be useful for reducing missed colorectal adenomas. |
| format | Article |
| id | doaj-art-df3c0afc9be24b81b9f4ef293204c483 |
| institution | Kabale University |
| issn | 2432-3853 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | The Japan Society of Coloproctology |
| record_format | Article |
| series | Journal of the Anus, Rectum and Colon |
| spelling | doaj-art-df3c0afc9be24b81b9f4ef293204c4832025-01-27T10:02:40ZengThe Japan Society of ColoproctologyJournal of the Anus, Rectum and Colon2432-38532025-01-0191798710.23922/jarc.2024-0572024-057Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal PolypsYuya Hiratsuka0Takashi Hisabe1Kensei Ohtsu2Tatsuhisa Yasaka3Kazuhiro Takeda4Masaki Miyaoka5Yoichiro Ono6Takao Kanemitsu7Kentaro Imamura8Teruyuki Takeda9Satoshi Nimura10Kenshi Yao11Department of Endoscopy, Fukuoka University Chikushi HospitalDepartment of Gastroenterology, Fukuoka University Chikushi HospitalDepartment of Gastroenterology, Fukuoka University Chikushi HospitalDepartment of Gastroenterology, Fukuoka University Chikushi HospitalDepartment of Endoscopy, Fukuoka University Chikushi HospitalDepartment of Endoscopy, Fukuoka University Chikushi HospitalDepartment of Gastroenterology, Fukuoka University Chikushi HospitalDepartment of Endoscopy, Fukuoka University Chikushi HospitalDepartment of Gastroenterology, Fukuoka University Chikushi HospitalDepartment of Gastroenterology, Fukuoka University Chikushi HospitalDepartment of Pathology, Fukuoka University Chikushi HospitalDepartment of Endoscopy, Fukuoka University Chikushi HospitalObjectives: Colonoscopy is the gold standard for screening cancer and precancerous lesions in the large intestine. Recently, remarkable advances in artificial intelligence (AI) have led to the development of various computer-aided detection (CADe) systems for colonoscopy. This study aimed to evaluate the usefulness of AI for colonoscopy using CAD-EYEⓇ (Fujifilm, Tokyo, Japan) to calculate the adenoma miss rate (AMR). Methods: This randomized, open-label, single-center, tandem study was conducted at Fukuoka University Chikushi Hospital from February 2022 to November 2022. Patients were randomly assigned to the CADe or non-CADe group. Immediately after the completion of the first endoscopy by an endoscopist, a new endoscopist was assigned to perform the second endoscopy. As a result, different endoscopists performed the examinations in a tandem fashion. A missed lesion was defined as a newly detected colorectal polyp by the second endoscopy. Finally, the AMR was compared between the two groups. Results: The study population comprised 48 patients in the CADe group and 46 patients in the non-CADe group. The AMR was 17.4% in the CADe group and 30.3% in the non-CADe group. Therefore, the AMR in the CADe group was statistically significantly lower than that in the non-CADe group (P=0.009). Conclusions: The application of CAD-EYEⓇ to colonoscopy reduced the AMR. Overall, CAD-EYEⓇ might be useful for reducing missed colorectal adenomas.https://www.jstage.jst.go.jp/article/jarc/9/1/9_2024-057/_pdf/-char/enartificial intelligencecomputer-aided detectionadenoma miss rateadenoma detection rate |
| spellingShingle | Yuya Hiratsuka Takashi Hisabe Kensei Ohtsu Tatsuhisa Yasaka Kazuhiro Takeda Masaki Miyaoka Yoichiro Ono Takao Kanemitsu Kentaro Imamura Teruyuki Takeda Satoshi Nimura Kenshi Yao Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps Journal of the Anus, Rectum and Colon artificial intelligence computer-aided detection adenoma miss rate adenoma detection rate |
| title | Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps |
| title_full | Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps |
| title_fullStr | Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps |
| title_full_unstemmed | Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps |
| title_short | Evaluation of Artificial Intelligence: Computer-aided Detection of Colorectal Polyps |
| title_sort | evaluation of artificial intelligence computer aided detection of colorectal polyps |
| topic | artificial intelligence computer-aided detection adenoma miss rate adenoma detection rate |
| url | https://www.jstage.jst.go.jp/article/jarc/9/1/9_2024-057/_pdf/-char/en |
| work_keys_str_mv | AT yuyahiratsuka evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT takashihisabe evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT kenseiohtsu evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT tatsuhisayasaka evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT kazuhirotakeda evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT masakimiyaoka evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT yoichiroono evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT takaokanemitsu evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT kentaroimamura evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT teruyukitakeda evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT satoshinimura evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps AT kenshiyao evaluationofartificialintelligencecomputeraideddetectionofcolorectalpolyps |