DIsease Progression & Deep Learning
DIsease Progression & Deep Learning
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The most fatal form of tuberculosis (TB), tuberculous meningitis (TBM), occurs in 1–5% of those with TB. The diagnosis of TBM can be challenging since its clinical patterns may be indistinguishable from other forms of meningitis. The resulting difficulty in diagnosis often leads to delayed treatments and increased mortality. Also, the patient's prognosis is also challenging, especially when impacted by other pre-conditions and underlying illnesses. Using imaging data, such as MRI, and clinical data it is possible to predict the patient's prognosis using state-of-the-art artificial intelligence (AI) tools.
Main Projects
Main Projects
Unveiling the pathways of TBM
Unveiling the pathways of TBM
This project aims the (1) encoding of quantitative imaging features, and (2) the prediction of the progression of the disease.
Encoding imaging features:
Extraction of quantitative imaging features:
Learning approach correlating the imaging features with the MRC scale.
Encoding of imaging features per time point t;
Disease Progression Model:
Prediction of the progression of the disease and subjects’ prognostication:
MRS prediction for next time point – t+1.
Anticipate severe neurological events.
Paper under submission. Updates soon!
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