Diagnostic value of artificial intelligence computer-assisted diagnosis (computer assisted-diagnosis eye function) for colorectal polyps

doi:10.35712/aig.v6.i3.107528

Advanced Search

BPG is committed to discovery and dissemination of knowledge

Home / Archive / Volume 6, Issue 3

This Article

Academic Content and Language Evaluation of This Article

CrossCheck and Google Search of This Article

Academic Rules and Norms of This Article

Citation of this article

Corresponding Author of This Article

Research Domain of This Article

Article-Type of This Article

Open-Access Policy of This Article

Times Cited Counts in Google of This Article

Number of Hits and Downloads for This Article

Total Article Views (209)

All Articles published online

The chart showing PDF series, HTML series, Tables (1-9) series.

Item

Count

PDF

HTML

Tables (1-9)

Sum=41

Featured Article

The chart showing Browse series, Download series.

Item

Count

Browse

Download

Sum=123

Publishing Process of This Article

Item

Count

Browse

Download

Sum=38

Nov 8, 2025 (publication date) through Nov 14, 2025

Times Cited of This Article

Times Cited (0)

Journal Information of This Article

Publication Name

Artificial Intelligence in Gastroenterology

ISSN

2644-3236

Publisher of This Article

Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

Observational Study

Artif Intell Gastroenterol. Nov 8, 2025; 6(3): 107528
Published online Nov 8, 2025. doi: 10.35712/aig.v6.i3.107528

Diagnostic value of artificial intelligence computer-assisted diagnosis (computer assisted-diagnosis eye function) for colorectal polyps

Hendra Asputra, Hasan Maulahela, Achmad Fauzi, Cleopas M Rumende, Amanda Pitarini, Nina Kemala Sari, Hamzah Shatri

Hendra Asputra, Hasan Maulahela, Achmad Fauzi, Amanda Pitarini, Division of Gastroenterology, Pancreatobiliary and Digestive Endoscopy, Department of Internal Medicine, Dr. Cipto Mangunkusumo Hospital, Jakarta 10430, Indonesia

Hendra Asputra, Department of Internal Medicine, Faculty of Medicine, University of Riau/RSUD Arifin Achmad, Pekanbaru 28125, Indonesia

Cleopas M Rumende, Division of Respirology and Critical Medicine, Department of Internal Medicine, Dr. Cipto Mangunkusumo Hospital, Jakarta 10430, Indonesia

Nina Kemala Sari, Division of Geriatrics, Department of Internal Medicine, Dr. Cipto Mangunkusumo Hospital, Jakarta 10430, Indonesia

Hamzah Shatri, Division of Psychosomatic and Palliative Care, Department of Internal Medicine, Dr. Cipto Mangunkusumo Hospital, Jakarta 10430, Indonesia

Author contributions: Asputra H conceived the presented idea, developed the theory, wrote the manuscript, performed the calculations, developed the theoretical formalism, performed the analytic calculations and the numerical simulations, and wrote the manuscript with input from all authors; Asputra H and Maulahela H verified the analytical methods, carried out the experiment, contributed to the final version of the manuscript, designed the model and the computational framework, and analyzed the data; Asputra H, Pitarini A, and Sari NK carried out the implementation; Maulahela H supervised the project; Fauzi A encouraged Asputra H to investigate gastroenterohepatology in artificial intelligence and supervised the findings of this work; Sari NK and Shatri H conceived the study, and were in charge of overall direction and planning; All authors contributed to the design and implementation of the research, the analysis of the results and the writing of the manuscript, conceived and planned the study, provided critical feedback and helped shape the research, analysis and manuscript, discussed the results, and contributed to the final manuscript.

Institutional review board statement: The study has received approval from the Health Research Ethics Committee at the Faculty of Medicine, University of Indonesia – Dr. Cipto Mangunkusumo Hospital, No. KET.272/UN2.F1/ETIK/PPM.00.02/2024.

Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.

Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.

STROBE statement: The authors have read the STROBE Statement–checklist of items, and the manuscript was prepared and revised according to the STROBE Statement–checklist of items.

Data sharing statement: No additional data are available.

Open Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/

Corresponding author: Hendra Asputra, MD, Division of Gastroenterology, Pancreatobiliary and Digestive Endoscopy, Department of Internal Medicine, Dr. Cipto Mangunkusumo Hospital, Jl. Diponegoro No. 71, Jakarta Pusat, Jakarta 10430, Indonesia. hendraasputra13@gmail.com

Received: March 26, 2025
Revised: April 14, 2025
Accepted: October 13, 2025
Published online: November 8, 2025
Processing time: 226 Days and 16.7 Hours

Abstract

BACKGROUND

The gold standard for colorectal polyp screening is currently colonoscopy, but the miss rate is still high and the adenoma detection rate and polyp detection rate are still low. The risk factors include the patient, operators, and the tools used. The use of artificial intelligence (AI) in colonoscopy has gained popularity by assisting endoscopists in the detection and characterization of polyps.

AIM

To evaluate the diagnostic performance of AI-assisted colonoscopy [computer assisted diagnosis (CAD) eye function] for colorectal polyp characterization.

METHODS

This study used a cross-sectional design conducted at the Gastrointestinal Endoscopy Center of Dr. Cipto Mangunkusumo Hospital in January-May 2024 on adult patients with suspected colorectal polyps.

RESULTS

A total of 60 patients with 100 polyps were involved in this study. Based on the results of the examination, it was found that the AI CAD eye function examination had a sensitivity of 79.17%, specificity of 75.00%, positive predictive value (PPV) of 89.06%, negative predictive value (NPV) of 58.33%, and accuracy of 78.00%. In polyps with diminutive size, sensitivity was 86.27%, specificity was 60.00%, PPV was 95.65%, NPV was 30.00%, and accuracy was 83.93%. Meanwhile, in polyps with non-diminutive size, sensitivity was 61.90%, specificity was 78.26%, PPV was 72.22%, NPV was 69.23%, and accuracy was 70.45%. In polyps on the left side of the colon, sensitivity was 78.85%, specificity was 81.25%, PPV was 93.18%, NPV was 54.17%, and accuracy was 79.41%. Meanwhile, in right-sided polyps the sensitivity was 80.00%, specificity was 66.67%, PPV was 80.00%, NPV was 66.67%, and accuracy was 75.00%. In sessile polyps the sensitivity was 81.54%, specificity was 50.00%, PPV was 91.38%, NPV was 29.41%, and accuracy was 77.33%. Meanwhile, in non-sessile polyps, the sensitivity was 57.14%, specificity was 88.89%, PPV was 66.67%, NPV was 84.21%, and accuracy was 80.00%.

CONCLUSION

AI CAD eye function examination had a high sensitivity value in diminutive, sessile polyps and right-sided polyps and a high specificity in non-diminutive, non-sessile polyps and left-sided polyps.

Keywords: Artificial intelligence; Computer-assisted diagnosis eye function; Colorectal polyps; Inflammatory bowel disease; Artificial intelligence assisted

Core Tip: Colonoscopy is currently the gold standard for screening colorectal polyps. However, the miss rate remains high, and the adenoma detection rate and polyp detection rate remain low. The risk factors included patients, operators, and the tools used. The use of artificial intelligence in colonoscopy has gained popularity because it helps endoscopists detect and characterize polyps. This cross-sectional study was conducted on adult patients with suspected colorectal polyps. The overall performance of the artificial intelligence (computer-assisted diagnosis eye function) showed a sensitivity of 79.17%, specificity of 75.00%, positive predictive value of 89.06%, negative predictive value of 58.33%, and accuracy of 78.00%.