New AI model predicts personalised TB treatments, study shows
tuberculosis (TB) patients, an advance that may lead to personalised treatments for the bacterial disease. The study, published in the journal iScience, analysed multimodal data including diverse biomedical data from clinical tests, genomics, medical imaging and drug prescriptions from TB patients.
By analysing data from patients with varying levels of drug resistance, the researchers discovered biomedical features predictive of treatment failure.
They also uncovered drug regimens effective against specific sets of drug-resistant TB patients.
«Our multimodal AI model accurately predicted treatment prognosis and outperformed existing models that focus on a narrow set of clinical data,» said Sriram Chandrasekaran, corresponding author and associate professor at the University of Michigan, US.
«We identified drug regimens that were effective against certain types of drug-resistant TB across countries, which is very important due to the spread of drug-resistant TB,» added study first author Awanti Sambarey, a postdoctoral fellow at the University of Michigan.
Using AI, the team examined more than 5,000 patients.
«This is real-world data we're talking about, so patients from different countries have different admission protocols. We worked with more than 200 biomedical features in our analysis; we examined demographic information such as age and gender as well as prior treatment history,» Sambarey said.
«We also noted if the patients had other comorbidities, such as HIV, and then we worked with several imaging
