Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches

• Published in: Diagnostics (Basel) Vol. 14; no. 13; p. 1376
• Main Authors: Ligato, Irene, De Magistris, Giorgio, Dilaghi, Emanuele, Cozza, Giulio, Ciardiello, Andrea, Panzuto, Francesco, Giagu, Stefano, Annibale, Bruno, Napoli, Christian, Esposito, Gianluca
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 28.06.2024
• Subjects: Accuracy; Algorithms; Artificial intelligence; Biopsy; BLI; Classification; CNN; Datasets; Endoscopy; gastric intestinal metaplasia; imaging diagnostics; Meta-analysis; Neural networks; ResNet50; virtual chromoendoscopy
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics14131376

Gastric cancer (GC) is a significant healthcare concern, and the identification of high-risk patients is crucial. Indeed, gastric precancerous conditions present significant diagnostic challenges, particularly early intestinal metaplasia (IM) detection. This study developed a deep learning system to assist in IM detection using image patches from gastric corpus examined using virtual chromoendoscopy in a Western country. Utilizing a retrospective dataset of endoscopic images from Sant’Andrea University Hospital of Rome, collected between January 2020 and December 2023, the system extracted 200 × 200 pixel patches, classifying them with a voting scheme. The specificity and sensitivity on the patch test set were 76% and 72%, respectively. The optimization of a learnable voting scheme on a validation set achieved a specificity of 70% and sensitivity of 100% for entire images. Despite data limitations and the absence of pre-trained models, the system shows promising results for preliminary screening in gastric precancerous condition diagnostics, providing an explainable and robust Artificial Intelligence approach.