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Chagas disease, an infection caused by the protozoan Trypanosoma cruzi and transmitted by the Reduuvid insect vector, remains a major cause of morbidity in Central and South America over a century after its discovery in 1909. Though major advances in preventing the spread of this disease have been made in recent decades, millions of individuals remain chronically infected due to prior exposure to T. cruzi and are at risk for future complications from the disease. Dermatologic manifestations of acute infection may include localized swelling at the site of inoculation (chagoma), conjunctivitis (Romaña’s sign), and a generalized morbilliform eruption (schizotrypanides). Reactivation of quiescent infection in immunocompromised hosts due to the acquired immunodeficiency syndrome or organ transplantation can present with fever and skin lesions including panniculitis. The wide-spread emigration of chronic carriers of T. cruzi to North America, Europe, and Australia makes it imperative that dermatologists worldwide be familiar with this entity to ensure proper diagnosis and treatment.

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Context.-Most deep learning (DL) studies have focused on neoplastic pathology, with the realm of inflammatory pathology remaining largely untouched.Objective.-To investigate the use of DL for nonneoplastic gastric biopsies.Design.-Gold standard diagnoses were blindly established by 2 gastrointestinal pathologists. For phase 1, 300 classic cases (100 normal, 100 Helicobacter pylori, 100 reactive gastropathy) that best displayed the desired pathology were scanned and annotated for DL analysis. A total of 70% of the cases for each group were selected for the training set, and 30% were included in the test set. The software assigned colored labels to the test biopsies, which corresponded to the area of the tissue assigned a diagnosis by the DL algorithm, termed area distribution (AD). For Phase 2, an additional 106 consecutive nonclassical gastric biopsies from our archives were tested in the same fashion.Results.-For Phase 1, receiver operating curves showed near perfect agreement with the gold standard diagnoses at an AD percentage cutoff of 50% for normal (area under the curve [AUC] ¼ 99.7%) and H pylori (AUC ¼ 100%), and 40% for reactive gastropathy (AUC ¼ 99.9%). Sensitivity/specificity pairings were as follows: normal (96.7%, 86.7%), H pylori (100%, 98.3%), and reactive gastropathy (96.7%, 96.7%). For phase 2, receiver operating curves were slightly less discriminatory, with optimal AD cutoffs reduced to 40% across diagnostic groups. The AUCs were 91.9% for normal, 100% for H pylori, and 94.0% for reactive gastropathy. Sensitivity/ specificity parings were as follows: normal (73.7%, 79.6%), H pylori (95.7%, 100%), reactive gastropathy (100%, 62.5%).Conclusions.-A convolutional neural network can serve as an effective screening tool/diagnostic aid for H pylori gastritis.

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