In this 60-minute webinar, researchers and pathologists from the University of New Mexico will discuss their recently published study using the HALO AI™ deep learning convolutional neural network (CNN) to determine whether a CNN can differentiate autoimmune gastritis from Helicobacter pylori gastritis.

AI has been widely studied in neoplastic pathology, but this technology also holds promise in the realm of inflammatory pathology. In a prior publication, pathologists at the University of New Mexico demonstrated that a CNN can accurately discriminate between the three most common histologic patterns in medical gastric biopsies: H. pylori gastritis, reactive gastropathy, and normal gastric mucosa. In their latest publication, they demonstrate that HALO AI can distinguish between two morphologically similar inflammatory patterns: H. pylori gastritis, and autoimmune gastritis, with equal accuracy of GI pathologists. As many pathologists who lack specialty training in inflammatory GI pathology find it challenging to distinguish these diagnoses, this has the potential to improve clinical practice in the future.

The webinar will include:

1) A brief introduction to the histologic features of H. pylori gastritis, autoimmune gastritis, and normal gastric mucosa

2) An overview of how the HALO AI algorithm was trained and how the test set images were evaluated

3) A detailed description of the results and potential future applications