{Reference Type}: Journal Article
{Title}: Convolutional Neural Network Model for Intestinal Metaplasia Recognition in Gastric Corpus Using Endoscopic Image Patches.
{Author}: Ligato I;De Magistris G;Dilaghi E;Cozza G;Ciardiello A;Panzuto F;Giagu S;Annibale B;Napoli C;Esposito G;
{Journal}: Diagnostics (Basel)
{Volume}: 14
{Issue}: 13
{Year}: 2024 Jun 28
{Factor}: 3.992
{DOI}: 10.3390/diagnostics14131376
{Abstract}: 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.