An insight on using deep learning algorithm in diagnosing gastritis

Dublin Core

Title

An insight on using deep learning algorithm in diagnosing gastritis

Subject

Deep learning
Diagnosis system
Gastritis detection
GoogleNet
ResNet
TResNet
VGGNet

Description

Chronic autoimmune gastritis (CAG) is a condition in which the stomach membrane is significantly impacted by inflammation. Despite the availability of numerous modern medical techniques, the detection of this condition continues to be a difficult challenge. White light endoscopy (WLE) has been employed to diagnose gastritis, but it has been subject to certain constraints. This technique is most effective when executed by an endoscopist who possesses a high level of expertise. In the present day, WLE is frequently accompanied by artificial intelligence (AI) due to its superior ability to detect defects that lead to damage. Recently, there has been a substantial increase in the efficacy of AI in conjunction with the expertise of endoscopists in the detection of CAG. The 25,216 intriguing case studies were examined in the eight selected studies. The collection comprised 84,678 frames and 10,937 images. The AI was 94% sensitive (95% CI: 0.88-0.97, I2 = 96.2%) and 96% specific (95% CI: 0.88-0.98, I2 = 98.04%). The receiver operating characteristic curve had an area of 0.98 (95% confidence interval: 0.96–0.99). A camera is highly effective when combined with AI to assist in the identification of CAG and is advantageous for clinical review.

Creator

Ragu P. J.1, Ashok Vajravelu1, Muhammad Mahadi bin Abdul Jamil1, Syed Riyaz Ahammed2

Source

Journal homepage: http://journal.uad.ac.id/index.php/TELKOMNIKA

Date

Oct 19, 2025

Contributor

PERI IRAWAN

Format

PDF

Language

ENGLISH

Type

TEXT

Files

Collection

Citation

Ragu P. J.1, Ashok Vajravelu1, Muhammad Mahadi bin Abdul Jamil1, Syed Riyaz Ahammed2, “An insight on using deep learning algorithm in diagnosing gastritis,” Repository Horizon University Indonesia, accessed January 12, 2026, https://repository.horizon.ac.id/items/show/10389.