Clinical and electrophysiological characteristics of neuropathic pain in leprosy patients: A prospective cross-sectional study

Neural leprosy investigation using electroneuromyography and the ML Flow rapid test: a case report

Neural leprosy, which is characterized by nerve involvement without visible skin lesions, presents a diagnostic challenge. This case report examined the significance of diverse diagnostic modalities in the identification of pure neural leprosy. A 28-year-old patient with symptoms of edema, pain, paresthesia, and diminished sensitivity in the lower limbs underwent various tests. A stilt skin smear yielded negative results on bacilloscopy, whereas a Fast ML Flow leprosy test and electroneuromyography supported the diagnosis. This discussion highlights the importance of accessible methods for early investigation. This study emphasizes the multidisciplinary approach and value of the Fast ML Flow leprosy test and electroneuromyography for diagnosing neural leprosy.

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.