AI Model Reads Chest Scans to Tell Fungal Infection and Tuberculosis Apart
Researchers in China have developed an AI system that uses chest CT scans and clinical data to accurately distinguish between tuberculosis and a fungal infection that mimics its symptoms. The model achieved high accuracy, potentially reducing the time required for correct diagnosis in HIV/AIDS patients.
Why it matters
This technology could significantly improve patient outcomes by allowing for faster, more accurate treatment of life-threatening lung infections in immunocompromised individuals.
<p>Doctors treating people with HIV/AIDS in Southeast Asia and southern China face a diagnostic dilemma that can cost weeks of precious time: the lung-dwelling fungus Talaromyces marneffei and Mycobacterium tuberculosis, the bacterium behind pulmonary tuberculosis, produce symptoms that overlap so heavily — prolonged fever, weight loss, cough, swollen lymph nodes — that clinicians frequently cannot tell them apart until laboratory cultures come back. A new study from Nanning, in China's Guangxi region, reports that an artificial intelligence system combining clinical data, quantitative image features and deep learning on chest CT scans can make that distinction with substantially higher accuracy than any single data source alone, achieving an area under the receiver operating characteristic curve of 0.931 and a specificity of 0.981 on an independent test set.</p><p>The research, published in Frontiers in Cellular and Infection Microbiology, was conducted by a team at the Fourth People's Hospital of Nanning working with Guangxi…
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