Current Status of Artificial Intelligence-Based Computer-Assisted Diagnosis Systems for Gastric Cancer in Endoscopy

Abstract

Artificial intelligence (AI) is gradually being utilized in various fields as its performance has been improving with the development of deep learning methods, availability of big data, and the progression of computer processing units. In the field of medicine, AI is mainly implemented in image recognition, such as in radiographic and pathologic diagnoses. In the realm of gastrointestinal endoscopy, although AI-based computer-assisted detection/diagnosis (CAD) systems have been applied in some areas, such as colorectal polyp detection and diagnosis, so far, their implementation in real-world clinical settings is limited. The accurate detection or diagnosis of gastric cancer (GC) is one of the challenges in which performance varies greatly depending on the endoscopist’s skill. The diagnosis of early GC is especially challenging, partly because early GC mimics atrophic gastritis in the background mucosa. Therefore, several CAD systems for GC are being actively developed. The development of a CAD system for GC is considered challenging because it requires a large number of GC images. In particular, early stage GC images are rarely available, partly because it is difficult to diagnose gastric cancer during the early stages. Additionally, the training image data should be of a sufficiently high quality to conduct proper CAD training. Recently, several AI systems for GC that exhibit a robust performance, owing to being trained on a large number of high-quality images, have been reported. This review outlines the current status and prospects of AI use in esophagogastroduodenoscopy (EGDS), focusing on the diagnosis of GC.

Deep Learning and Gastric Cancer: Systematic Review of AI-Assisted Endoscopy

“…The addition of computationally efficient DL computer vision models allows endoscopists to identify more potentially treatable gastric lesions during real-time upper endoscopy [15][16][17][18]. AI also shows promise in diagnosing and managing GC, as supported by two recent systematic reviews [8,9]. Our review delves into the use of deep learning (DL) models to detect gastric pre-malignant, early-stage, and neoplastic lesions.…”

“…This vast data set can provide AI models a deep understanding of GC patterns. This may allow the models to identify subtle signs and predictive markers that may be missed by human analysis [8][9][10].…”

“…Improved endoscopic detection rates of pre-malignant gastric lesions and early GC could make large-scale screening more effective. Using a screening-based approach, earlier diagnosis of premalignant gastric lesions and gastric neoplasias would allow a greater proportion of patients to be treated with endoscopic interventions that have high cure rates and low complication rates [8,51]. Further studies are necessary to assess the feasibility and structure of such screening programs across different populations.…”

“…Recent advancements in artificial intelligence (AI)assisted endoscopy offer hope. Computer-aided detection (CADe) and diagnosis (CADx) AI systems aim to improve the real-time detection of gastric neoplasias by detecting subtle neoplastic changes in gastric mucosa with high accuracy [8,9].…”

Importance of Patient History in Artificial Intelligence–Assisted Medical Diagnosis: Comparison Study

“…With the advent of artificial intelligence (AI) in recent years, numerous studies have focused on AI-assisted diagnoses, including cancer screening and treatment [ 13-15 ], diagnostic ultrasound imaging [ 16-19 ], x-ray imaging [ 20 ], computed tomography [ 21 ], magnetic resonance imaging [ 22 ], and endoscopy [ 15 23 ]. Other reports on AI-assisted imaging diagnoses include AI’s applications in radiology, pathology, and dermatological imaging [ 13 24 ].…”