Principal Research Scientist Leo Anthony Celi co-authored a study that found “a lack of racial and gender diversity could be hindering the efforts of researchers working to improve the fairness of artificial intelligence (AI) tools in health care,” reports Carissa Wong for Nature.
Researchers at MIT developed a system that uses artificial intelligence to help predict future risk of developing breast cancer, reports Poppy Harlow for CNN. What this work does “is identifies risk. It can tell a woman that you’re at high risk for developing breast cancer before you develop breast cancer,” says Larry Norton, medical director of the Lauder Breast Center at the Memorial Sloan Kettering Cancer Center.
Fast Company reporter Amelia Hemphill spotlights the work of Alicia Chong Rodriguez SM ’17, SM ’18, and her startup Bloomer Tech, which is “dedicated to transforming women’s underwear into a healthcare device.” “Our big goal is to generate digital biomarkers,” says Chong Rodriguez. “Digital biomarkers work more like a video, so it will definitely allow a more personalized care from the physician to their patient.”
Researchers at MIT and elsewhere are analyzing patients’ speech patterns to see if they can detect Lou Gehrig’s disease in its early stages, reports Ben Leonard, Ruth Reader, Carmen Paun and Erin Schumaker for Politico. “Catching it early and beginning treatment can improve patients’ quality of life and delay symptom onset,” they write.
Researchers at MIT and Massachusetts General Hospital have developed “Sybil” – an artificial intelligence tool that can predict the risk of a patient developing lung cancer within six years, reports Mallika Marshall for CBS Boston.
MIT researchers have developed a new AI tool called Sybil that could help predict whether a patient will get lung cancer up to six years in advance, reports Pranshu Verma for The Washington Post. “Much of the technology involves analyzing large troves of medical scans, data sets or images, then feeding them into complex artificial intelligence software,” Verma explains. “From there, computers are trained to spot images of tumors or other abnormalities.”
Researchers from MIT and Harvard have developed “a new type of electrically conductive hydrogel ‘scaffold’ that could eventually be used to create a soft brain-computer interface (or BCI) that translates neural signals from the brain into machine-readable instructions,” reports Adam Bluestein for Fast Company.
Researchers from MIT and Mass General Hospital have developed “a deep learning model named ‘Sybil’ that can be used to predict lung cancer risk, using data from just a single CT scan,” writes Sai Balasubramanian for Forbes. “Sybil is able to predict a patient’s future lung cancer risk to a certain extent of accuracy, using the data from just one LDCT [low-dose computed tomography scan],” writes Balasubramanian.
Harry McNamara PhD ’19, David Heller ’18, and Shara Ticku co-founded C16 Biosciences, a biotechnology company that uses synthetic biology to address environmental concerns, reports John Cumbers for Forbes. The company “wants to replace conflict palm oil with a sustainable alternative made in yeast using precision fermentation,” writes Cumbers.
Rosina Samadani ’89, MS ’92 co-developed EyeBox, an algorithm-based non-invasive diagnostic test for concussions, reports Geri Stengel for Forbes. “Patients watch a video, and the device watches their eyes for 220 seconds with a very high-quality, high-frequency infrared camera that measures eye movements and provides a score based on those eye movements,” explains Stengel. “The score is correlated with the absence or presence of a concussion.”
Prof. Robert Langer and his colleagues write for Physics Today about how physics could help contribute to predicting tissue behaviors and accelerate the regeneration of human tissues and organs. “The physics of tissue engineering in general and of bioprinting in particular is a relatively new field that could provide numerous opportunities for tissue and organ fabrication and regeneration,” they write.
Researchers at MIT have found indoor humidity levels can influence the transmission of Covid-19, reports Dennis Thompson for US News & World Report. “We found that even when considering countries with very strong versus very weak Covid-19 mitigation policies, or wildly different outdoor conditions, indoor — rather than outdoor — relative humidity maintains an underlying strong and robust link with Covid-19 outcomes,” explains Prof. Lydia Bourouiba.
MIT researchers have found that relative humidity “may be an important metric in influencing the transmission of Covid-19,” reports Sophie Mellor for Fortune, “Maintaining an indoor relative humidity between 40% and 60% – a Goldilocks climate, not too humid, not too dry – is associated with relatively lower rates of Covid-19 infections and deaths,” writes Mellor.
Researchers from MIT and Boston Children’s Hospital are working on developing new technology that could help predict and identify diseases through audio recordings of a patient’s voice, reports Jim Morelli for Boston 25 News. “It’s almost like being Sherlock Holmes to voice, taking voice as a signal and trying to understand what’s going on behind it,” said Satrajit Ghosh, a principal research scientist at the McGovern Institute. “And can we backtrack from voice and say this is ‘Disorder A’ versus ‘Disorder B’?”
CNBC reporter Catherine Clifford spotlights C16 Biosciences, a startup co-founded by MIT alumni that is developing a palm oil alternative called Palmless. “What we are building is a platform technology that can produce all different kinds of microbial oils,” explains David Heller ’18, co-founder and head of operations at C16 Biosciences. “It’s definitely possible that we’re able to make other kinds of vegetable oil replacements in the future.”