CNN reporter Allen Kim writes about how CSAIL researchers developed a new system that enables a robot to disinfect surfaces and neutralize aerosolized forms of the coronavirus. In the future, the researchers hope the robot could be used to enable autonomous UV disinfection “in other environments such as supermarkets, factories and restaurants.”
WHDH reporter Emily Pritchard spotlights how CSAIL researchers have developed a new robotic system that is being used to help disinfect the Greater Boston Food Bank during the coronavirus pandemic. “We believe that is one piece of the puzzle in figuring out how to mitigate the spread of coronavirus,” says research scientist Alyssa Pierson.
A new robotic system developed by CSAIL researchers uses UV-C light to kill viruses and bacteria on surfaces and aerosols, reports Darrell Etherington for TechCrunch. “Via cameras and sensors, the robot can map an indoor space, then navigate designed waypoints within that mapped area and disinfect as it goes, keeping track of the areas it has to disinfect,” writes Etherington.
MIT researchers have developed a new robotic system that uses a UV-C light fixture to disinfect surfaces at the Greater Boston Food Bank’s warehouse staging area, reports Matt Reed for WCVB. Research scientist Alyssa Pierson explains that the ultraviolet light "breaks apart the kind of outer incasing or shell of these pathogens."
MIT researchers have developed an AI system that can predict Alzheimer’s risk by forecasting how patients will perform on a test measuring cognitive decline up to two years in advance, reports Casey Ross for STAT.
The Department of Veterans Affairs is participating in a series of MIT “GrandHacks,” problem-solving sessions aimed at tackling some of the VA’s biggest health care challenges, reports Patricia Kime for Military.com. The sessions, explains Kime, “bring together teams of students, entrepreneurs, tech gurus, health providers, patients, insurers and academicians to find solutions to problems in a short amount of time.”
A sensor developed by MIT researchers could make diagnosing sepsis easier, quicker and more affordable, reports Darrell Etherington for TechCrunch. Etherington explains that the sensor, which “employs microfluidics to detect the presence of key proteins in the blood,” could have “a huge potential impact, as sepsis is one of the leading causes of death in hospitals.”
Writing for STAT, Prof. Jonathan Gruber examines his research showing that while doctors have more information about different tests and treatments, they make decisions similar to their patients when receiving care. Gruber says this finding suggests that to improve health care decision-making, financial incentives and other approaches are needed that go beyond providing patients with more information.
CNN reporter Nell Lewis spotlights how MIT researchers have developed an algorithm that can help predict from a mammogram a patient’s risk of developing breast cancer. “In the early stages cancer is a treatable disease,” says Barzilay. “If we can identify many more women early enough, and either prevent their disease or treat them at the earliest stages, this will make a huge difference.”
A study co-authored by Prof. Cynthia Breazeal found that a “social robot” teddy bear “boosted spirits, eased anxiety and even lowered perceived pain levels” among Boston Children’s Hospital patients aged 3 to 10 years old, reports Robert Preidt for US News & World Report. “We want technology to support everyone who's invested in the quality care of a child," says Breazeal.
A new AI prediction model developed at MIT could detect breast cancer up to five years in advance. The researchers hope this technique “can also be used to improve detection of other diseases that have similar problems with existing risk models, with far too many gaps and lower degrees of accuracy,” writes Darrell Etherington for TechCrunch.
Writing for the Financial Times about how technology is advancing the field of health care, John Browne spotlights Prof. Bob Langer’s work developing new methods of delivering drugs with improved precision. Browne explains that Langer is working on “a device smaller than a grain of rice that he can inject into a tumour to test the efficacy of dozens of chemotherapy agents in parallel.”
Writing for Times Higher Education, senior lecturer Anjali Sastry argues that entrepreneurship is a key component in finding solutions to complex global health problems. Sastry spotlights how MIT students are provided with hands-on opportunities to “learn analytics, systems thinking, effective business models and entrepreneurial processes. They aren’t just learning how to maximize profits, but ways to understand the market and craft systems.”
Fast Company reporter Michael Grothaus writes that CSAIL researchers have developed a deep learning model that could predict whether a woman might develop breast cancer. The system “could accurately predict about 31% of all cancer patients in a high-risk category,” Grothaus explains, which is “significantly better than traditional ways of predicting breast cancer risks.”
WCVB-TV’s Jennifer Eagan reports that researchers from MIT and MGH have developed a deep learning model that can predict a patient’s risk of developing breast cancer in the future from a mammogram image. Prof. Regina Barzilay explains that the model “can look at lots of pixels and variations of the pixels and capture very subtle patterns.”