<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>2795-5001</journal-id>
<journal-title><![CDATA[Portuguese Journal of Dermatology and Venereology]]></journal-title>
<abbrev-journal-title><![CDATA[Port J Dermatol Venereol.]]></abbrev-journal-title>
<issn>2795-5001</issn>
<publisher>
<publisher-name><![CDATA[Permanyer Publications]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S2795-50012026000100011</article-id>
<article-id pub-id-type="doi">10.24875/pjdv.25000080</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Unveiling leprosy through machines: a review of artificial intelligence in a neglected tropical disease]]></article-title>
<article-title xml:lang="pt"><![CDATA[Revelando a lepra através de máquinas: uma revisão da inteligência artificial numa doença tropical negligenciada]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Goswami]]></surname>
<given-names><![CDATA[Aniket]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Verma]]></surname>
<given-names><![CDATA[Shikha]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Marak]]></surname>
<given-names><![CDATA[Anita]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,NEIGRIHMS Department of Dermatology ]]></institution>
<addr-line><![CDATA[Shillong ]]></addr-line>
<country>India</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>03</month>
<year>2026</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>03</month>
<year>2026</year>
</pub-date>
<volume>84</volume>
<numero>1</numero>
<fpage>11</fpage>
<lpage>18</lpage>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_arttext&amp;pid=S2795-50012026000100011&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_abstract&amp;pid=S2795-50012026000100011&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://scielo.pt/scielo.php?script=sci_pdf&amp;pid=S2795-50012026000100011&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract Leprosy persists as a major public health challenge in many areas of the world, with nearly 200,000 new cases reported annually despite the success of multidrug therapy. Timely diagnosis remains pivotal to preventing disability and interrupting transmission; however, dependence on clinical acumen and variable diagnostic infrastructure continues to impede early detection. Recent advances in artificial intelligence (AI) herald transformative potential across diagnostic, classification, monitoring, and epidemiological dimensions. Convolutional neural networks and hybrid deep learning architectures have demonstrated diagnostic accuracies exceeding 90% in differentiating leprosy from phenotypically similar dermatoses, while explainable AI frameworks enhance interpretability and clinician confidence. Machine learning algorithms leveraging registry and questionnaire-based data enable reliable classification of paucibacillary and multibacillary forms, facilitating community-level triage. Integration of biochemical, spectroscopic, and geospatial analytics further supports therapeutic monitoring and targeted surveillance. Persistent challenges include limited dataset diversity, insufficient external validation, and unresolved ethical issues surrounding data governance, bias, and privacy. Future directions lie in federated learning, multimodal integration, and patient-centric digital platforms. The fusion of computational precision with human compassion may ultimately redefine early detection and accelerate global leprosy elimination.]]></p></abstract>
<abstract abstract-type="short" xml:lang="pt"><p><![CDATA[Resumo A lepra persiste como um grande desafio de saúde pública em muitas partes do mundo, com quase 200.000 novos casos reportados anualmente, apesar do sucesso da poliquimioterapia. O diagnóstico atempado continua a ser fundamental para prevenir a incapacidade e interromper a transmissão; no entanto, a dependência da experiência clínica e a infraestrutura de diagnóstico variável continuam a dificultar a deteção precoce. Os recentes avanços na inteligência artificial (IA) anunciam um potencial transformador nas dimensões de diagnóstico, classificação, monitorização e epidemiologia. As redes neuronais convolucionais e as arquiteturas híbridas de aprendizagem profunda demonstraram uma precisão diagnóstica superior a 90% na diferenciação entre lepra e dermatoses fenotipicamente semelhantes, enquanto as estruturas de IA explicáveis potenciam a interpretabilidade e a confiança do médico. Os algoritmos de aprendizagem automática que utilizam dados de registos e questionários permitem a classificação fiável de formas pauci- e multibacilares, facilitando a triagem a nível comunitário. A integração de análises bioquímicas, espectroscópicas e geoespaciais apoia ainda mais a monitorização terapêutica e a vigilância dirigida. Os desafios persistentes incluem a diversidade limitada dos conjuntos de dados, a validação externa insuficiente e questões éticas não resolvidas relacionadas com a propriedade e tratamento dos dados, o enviesamento e a privacidade. As direções futuras apontam para a aprendizagem federada, a integração multimodal e as plataformas digitais centradas no doente. A fusão da precisão computacional com a compaixão humana pode, em última análise, redefinir a deteção precoce e acelerar a eliminação global da lepra.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[Leprosy]]></kwd>
<kwd lng="en"><![CDATA[Artificial intelligence]]></kwd>
<kwd lng="en"><![CDATA[Neglected tropical disease]]></kwd>
<kwd lng="en"><![CDATA[Machine learning]]></kwd>
<kwd lng="pt"><![CDATA[Lepra]]></kwd>
<kwd lng="pt"><![CDATA[Inteligência artificial]]></kwd>
<kwd lng="pt"><![CDATA[Doença tropical negligenciada]]></kwd>
<kwd lng="pt"><![CDATA[Aprendizagem de máquina]]></kwd>
</kwd-group>
</article-meta>
</front><back>
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