Description of leprosy classification at baseline among patients enrolled at the uniform multidrug therapy clinical trial for leprosy patients in Brazil

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.

[Leprosy-related mortality in the State of Piauí, Brazil: time trends and spatial patterns, 2000-2015]

The study aimed to analyze spatial patterns and time trends in leprosy-related mortality in the State of Piauí, Brazil, from 2000 to 2015. This was a mixed ecological study with a spatial and temporal state-based approach, using data from the Brazilian Mortality Information System. The analysis includes epidemiological characteristics, mortality trends by Joinpoint regression, and spatial analysis, using the state's 224 municipalities (counties) as the geographic unit. Of the 245,413 deaths identified, leprosy was identified in 234 death certificates, 135 (41.7%) as the underlying cause of death and 189 (58.3%) as an associated cause of death. The highest leprosy-related mortality rates were associated with male gender (relative risk - RR = 2.38; 95%CI: 1.87; 3.03), elderly age (RR = 10.52; 95%CI: 7.16; 15.46), brown skin color (RR = 2.22; 95%CI: 1.47; 3.35), and residents of the state's interior (RR = 5.72; 95%CI: 4.54; 7.21). The crude leprosy-related mortality rate showed a significant increase among the elderly (70 years), brown race/color, cities with fewer than 20,000 inhabitants, and the Central region of the state, but not significant for the State of Piauí as a whole. The spatial distribution by age-adjusted mortality was heterogeneous in the municipalities, concentrating high mortality rates in the northern region of the state, close to the coastline. There was a pattern of increasing smoothed mortality rates over the course of the study's four-year periods in the Mid-northern Central and Semiarid regions. Leprosy mortality was spatially heterogeneous and growing over the years. The findings highlight the importance of enhancing integrated surveillance and healthcare activities.