» Articles » PMID: 30342029

Automated Polyp Detection in the Colorectum: a Prospective Study (with Videos)

Overview
Date 2018 Oct 21
PMID 30342029
Citations 37
Authors
Affiliations
Soon will be listed here.
Abstract

Background And Aims: Adenoma detection is a highly personalized task that differs markedly among endoscopists. Technical advances are therefore desirable for the improvement of the adenoma detection rate (ADR). An automated computer-driven technology would offer the chance to objectively assess the presence of colorectal polyps during colonoscopy. We present here the application of a real-time automated polyp detection software (APDS) under routine colonoscopy conditions.

Methods: This was a prospective study at a university hospital in Germany. A prototype of a novel APDS ("KoloPol," Fraunhofer IIS, Erlangen, Germany) was used for automated image-based polyp detection. The software functions by highlighting structures of possible polyp lesions in a color-coded manner during real-time colonoscopy procedures. Testing the feasibility of APDS deployment under real-time conditions was the primary goal of the study. APDS polyp detection rates (PDRs) were defined as secondary endpoints provided that endoscopists' detection served as criterion standard.

Results: The APDS was applied in 55 routine colonoscopies without the occurrence of any clinically relevant adverse events. Endoscopists' PDRs and ADRs were 56.4% and 30.9%, respectively. The PDRs and ADRs of the APDS were 50.9% and 29.1%, respectively. The APDS detected 55 of 73 polyps (75.3%). Smaller polyp size and flat polyp morphology were correlated with insufficient polyp detection by the APDS.

Conclusion: Computer-assisted automated low-delay polyp detection is feasible during real-time colonoscopy. Efforts should be undertaken to improve the APDS with respect to smaller and flat shaped polyps. (Clinical trial registration number: NCT02838888.).

Citing Articles

A colonic polyps detection algorithm based on an improved YOLOv5s.

Li J, Zhao J, Wang Y, Zhu J, Wei Y, Zhu J Sci Rep. 2025; 15(1):6852.

PMID: 40011590 PMC: 11865574. DOI: 10.1038/s41598-025-91467-1.


Comparison of the Ability of Artificial-Intelligence-Based Computer-Aided Detection (CAD) Systems and Endoscopists to Detect Colorectal Neoplastic Lesions on Endoscopy Video.

Misumi Y, Nonaka K, Takeuchi M, Kamitani Y, Uechi Y, Watanabe M J Clin Med. 2023; 12(14).

PMID: 37510955 PMC: 10381252. DOI: 10.3390/jcm12144840.


Computer-aided automated diminutive colonic polyp detection in colonoscopy by using deep machine learning system; first indigenous algorithm developed in India.

Mazumdar S, Sinha S, Jha S, Jagtap B Indian J Gastroenterol. 2023; 42(2):226-232.

PMID: 37145230 DOI: 10.1007/s12664-022-01331-7.


The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy.

Vilkoite I, Tolmanis I, Abu Meri H, Polaka I, Mezmale L, Anarkulova L Diagnostics (Basel). 2023; 13(4).

PMID: 36832189 PMC: 9955100. DOI: 10.3390/diagnostics13040701.


A Real-Time Polyp-Detection System with Clinical Application in Colonoscopy Using Deep Convolutional Neural Networks.

Krenzer A, Banck M, Makowski K, Hekalo A, Fitting D, Troya J J Imaging. 2023; 9(2).

PMID: 36826945 PMC: 9967208. DOI: 10.3390/jimaging9020026.