Diogo Libânio

Stomach cancer is the third deadliest type of cancer in the world (0.86 million deaths in 2017). In 2035, a 20% increase will be observed both in incidence and mortality due to demographic effects if no interventions are foreseen. Upper GI endoscopy (UGIE) plays a paramount role in early diagnosis and, therefore, improved survival rates. On the other hand, human and technical factors can contribute to misdiagnosis while performing UGIE. In this scenario, artificial intelligence (AI) has recently shown its potential in compensating for the pitfalls of UGIE, by leveraging deep learning architectures able to efficiently recognize endoscopic patterns from UGIE video data. This work presents a review of the current state-of-the-art algorithms in the application of AI to gastroscopy. It focuses specifically on the threefold tasks of assuring exam completeness (i.e., detecting the presence of blind spots) and assisting in the detection and characterization of clinical findings, both gastric precancerous conditions and neoplastic lesion changes. Early and promising results have already been obtained using well-known deep learning architectures for computer vision, but many algorithmic challenges remain in achieving the vision of AI-assisted UGIE. Future challenges in the roadmap for the effective integration of AI tools within the UGIE clinical practice are discussed, namely the adoption of more robust deep learning architectures and methods able to embed domain knowledge into image/video classifiers as well as the availability of large, annotated datasets.

Chronic hepatitis C is a leading cause of morbidity and mortality, mainly related to fibrosis/cirrhosis and portal hypertension. Direct antiviral agents are highly effective and safe and can now cure > 90% of the patients. Sustained viral response (SVR) after interferon-based regimens has been associated with improvement in liver function, fibrosis and portal hypertension in a significant proportion of patients, although a point of no return seems to exist from which viral elimination is no longer capable of preventing portal hypertension progression and liver decompensation. Indeed, although SVR is associated with improvement of hepatic venous pressure gradients and therefore a decreased risk of de novo esophageal varices, several studies show that viral clearance does not eliminate the risk of variceal progression, liver decompensation and death in patients with pre-established portal hypertension. Although evidence about the effects of direct antiviral agents (DAAs) on clinically significant outcomes is still scarce and with short follow-up, DAAs can decrease the burden of the disease if patients are timely treated before significant fibrosis and portal hypertension develops. Studies with longer follow-up are waited to establish the real magnitude of hepatitis C treatment on portal hypertension. Future studies should also focus on predictors of portal hypertension resolution since it can influence management and avoid unnecessary monitoring