Deep learning for AI-based diagnosis of skin-related neglected tropical diseases: a pilot study

Artificial Intelligence on Diagnostic Aid of Leprosy: A Systematic Literature Review

Leprosy is a neglected tropical disease that can cause physical injury and mental disability. Diagnosis is primarily clinical, but can be inconclusive due to the absence of initial symptoms and similarity to other dermatological diseases. Artificial intelligence (AI) techniques have been used in dermatology, assisting clinical procedures and diagnostics. In particular, AI-supported solutions have been proposed in the literature to aid in the diagnosis of leprosy, and this Systematic Literature Review (SLR) aims to characterize the state of the art. This SLR followed the preferred reporting items for systematic reviews and meta-analyses (PRISMA) framework and was conducted in the following databases: ACM Digital Library, IEEE Digital Library, ISI Web of Science, Scopus, and PubMed. Potentially relevant research articles were retrieved. The researchers applied criteria to select the studies, assess their quality, and perform the data extraction process. Moreover, 1659 studies were retrieved, of which 21 were included in the review after selection. Most of the studies used images of skin lesions, classical machine learning algorithms, and multi-class classification tasks to develop models to diagnose dermatological diseases. Most of the reviewed articles did not target leprosy as the study's primary objective but rather the classification of different skin diseases (among them, leprosy). Although AI-supported leprosy diagnosis is constantly evolving, research in this area is still in its early stage, then studies are required to make AI solutions mature enough to be transformed into clinical practice. Expanding research efforts on leprosy diagnosis, coupled with the advocacy of open science in leveraging AI for diagnostic support, can yield robust and influential outcomes.

Public Health Research Priorities for Fungal Diseases: A Multidisciplinary Approach to Save Lives

Fungal infections can cause severe disease and death and impose a substantial economic burden on healthcare systems. Public health research requires a multidisciplinary approach and is essential to help save lives and prevent disability from fungal diseases. In this manuscript, we outline the main public health research priorities for fungal diseases, including the measurement of the fungal disease burden and distribution and the need for improved diagnostics, therapeutics, and vaccines. Characterizing the public health, economic, health system, and individual burden caused by fungal diseases can provide critical insights to promote better prevention and treatment. The development and validation of fungal diagnostic tests that are rapid, accurate, and cost-effective can improve testing practices. Understanding best practices for antifungal prophylaxis can optimize prevention in at-risk populations, while research on antifungal resistance can improve patient outcomes. Investment in vaccines may eliminate certain fungal diseases or lower incidence and mortality. Public health research priorities and approaches may vary by fungal pathogen.