Application of artificial intelligence-driven endoscopic screening and diagnosis of gastric cancer

Abstract

The landscape of gastrointestinal endoscopy continues to evolve as new technologies and techniques become available. The advent of image-enhanced and magnifying endoscopies has highlighted the step toward perfecting endoscopic screening and diagnosis of gastric lesions. Simultaneously, with the development of convolutional neural network, artificial intelligence (AI) has made unprecedented breakthroughs in medical imaging, including the ongoing trials of computer-aided detection of colorectal polyps and gastrointestinal bleeding. In the past demi-decade, applications of AI systems in gastric cancer have also emerged. With AI’s efficient computational power and learning capacities, endoscopists can improve their diagnostic accuracies and avoid the missing or mischaracterization of gastric neoplastic changes. So far, several AI systems that incorporated both traditional and novel endoscopy technologies have been developed for various purposes, with most systems achieving an accuracy of more than 80%. However, their feasibility, effectiveness, and safety in clinical practice remain to be seen as there have been no clinical trials yet. Nonetheless, AI-assisted endoscopies shed light on more accurate and sensitive ways for early detection, treatment guidance and prognosis prediction of gastric lesions. This review summarizes the current status of various AI applications in gastric cancer and pinpoints directions for future research and clinical practice implementation from a clinical perspective.

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

“…Training these models on diverse datasets is essential to account for patient demographic factors and subtle differences in disease presentation. AI systems like SSD-GPNet and YOLOv4 are good at detecting gastric pathologies, particularly polyps [24,25,56,57]. However, their real-world effectiveness remains uncertain, as variables like endoscopy lighting conditions, image quality, and inter-operator variability can impact performance.…”

Clinically applicable optimized periprosthetic joint infection diagnosis via AI-based pathology

“…diagnosis by saving time and improving the e ciency and accuracy of radiological and pathological examination. Neural networks have shown promising performance in identifying suspicious tumors [18][19][20][21] or speci c tissues, such as renal glomeruli [38], but few attempts have been made to recognize infected tissues. This might be because cells with identi able features in infected tissues are usually dispersed or scattered, unlike tumors, in which diagnostically relevant features are con ned to a speci c area.…”

“…However, developing an AI model for pathological diagnosis to identify PJI-infected tissues substantially differs from wellestablished models used for cancer diagnosis [17]. The intelligent cancer pathology diagnosis model, by precisely delineating the boundaries of speci c areas, can achieve an accuracy comparable to that attained by pathological experts for the diagnosis of super cial tissues such as dermal, cervical, and gastric cancers [18][19][20][21]. Nonetheless, PJI pathology lacks well-de ned boundaries for infection-positive indicators, making it di cult to employ existing model training strategies directly.…”

Advances in the Aetiology & Endoscopic Detection and Management of Early Gastric Cancer

“…Endoscopic ultrasonography can be helpful in the assessment of depth of invasion before endoscopic resection of early gastric cancer is considered [ 152 ]. Finally, artificial intelligence in the detection of early gastric cancer has shown promise [ 185 , 186 , 187 , 188 , 189 , 190 ] and may constitute standard practice in the near future.…”