Integration of artificial intelligence in the diagnosis of cervical tuberculous lymphadenitis: A case study
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Herodes Ramírez-Ramírez 1 2 * , Yaneya Acosta-Aguirre 3 4 , David Zapata-Hernández 4 , Susely Figueroa-Iglesias 2 * , Pedro Martínez-Lozano 5 6 , Juan Nicolás Cuenca-Zaldívar 6 7 8 * , Eleuterio Atanasio Sánchez Romero 6 7 9 *
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1 University of Medical Sciences of Havana, Havana, CUBA2 Belle-Torus Corporation, Cambridge, MA, USA3 Pediatrics E.A.P, Health Center C. S. La Unión, Murcia, SPAIN4 Pediatrics E.A.P, Health Center C. S. Mar Menor, Murcia, SPAIN5 Department of Physiotherapy, Faculty of Medicine, Health and Sports, Universidad Europea de Madrid, Madrid, SPAIN6 Interdisciplinary Research Group on Musculoskeletal Disorders, Madrid, SPAIN7 Nursing and Health Care Research Group, Puerta de Hierro-Segovia de Arana Health Research Institute, Majadahonda, SPAIN8 El Abajón Primary Health Centre, Madrid, SPAIN9 Physiotherapy and Orofacial Pain Working Group, Spanish Society of Craniomandibular Dysfunction and Orofacial Pain, Madrid, SPAIN
10 Department of Rehabilitation Sciences, Florida Gulf Coast University, Fort Myers, FL, USA* Corresponding Author

Abstract

Introduction: Cervical tuberculous lymphadenitis (CTL) or scrofula is the most common extrapulmonary presentation of tuberculosis (TB), accounting for nearly 50% of the cases. This case report illustrates the role of artificial intelligence (AI)-based image analysis tool in aiding CTL diagnosis.
Main symptoms and clinical findings: A 3-year-old male patient presented with a persistent, non-resolving cervical mass. The patient showed no systemic symptoms such as fever or night sweats. Clinical examination revealed firm, non-tender, lateral cervical adenopathy.
Diagnosis and intervention: The patient underwent multiple diagnostic tests including Mantoux, polymerase chain reaction, and fine-needle aspiration biopsy. AI-assisted imaging analysis suggested TB-related lymphadenopathy, prompting further microbiological confirmation. The patient was prescribed a two-months regimen of first-line anti-TB medication.
Conclusion: This case highlights the potential of AI in assisting in the early identification of CTL through image analysis. AI can complement conventional diagnostics, especially in resource-limited settings, by streamlining clinical decision making and reducing diagnostic delays.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Case Report

ELECTRON J GEN MED, Volume 22, Issue 6, December 2025, Article No: em693

https://doi.org/10.29333/ejgm/17170

Publication date: 01 Nov 2025

Online publication date: 30 Sep 2025

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Article Downloads: 238

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