Integration of artificial intelligence in the diagnosis of cervical tuberculous lymphadenitis: A case study

<b>Introduction:</b> 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.<br /> <b>Main symptoms and clinical findings:</b> 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.<br /> <b>Diagnosis and intervention:</b> 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.<br /> <b>Conclusion: </b>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.