Harvard undergrad’s AI model helps to predict TB resistance
One of the greatest challenges in treating tuberculosis — the top infectious killer worldwide, according to the World Health Organization (WHO) — is the bacterium’s ability to shapeshift rapidly and become resistant to multiple drugs. Identifying resistant strains quickly and choosing the right antibiotics to treat them remains difficult for several reasons, including the bacterium’s propensity to grow slowly in the lab, which can delay drug-sensitivity test results by as much as six weeks after initial diagnosis. New tests that can quickly and reliably detect resistance to the most commonly used drugs before a patient begins treatment are urgently needed to improve outcomes and help curb the spread of the infection. Now, Harvard College applied math student Michael Chen ’20, working with biomedical researchers at Harvard Medical School’s Blavatnik Institute, has designed a computer program that sets the stage for the development of such tests. The program, described April 29 in EBioMedicine, can accurately predict...