Two Cornell Ph.D. students passionate about improving patient health care are embarking upon an entrepreneurial journey after developing a new software to reduce MRI scan times and allow for earlier detection of chronic diseases.
Biomedical engineers Madhur Srivastava and Ruisheng Wang met while attending a graduate student program at Weill Cornell, and combined Srivastava’s technical experience in electrical and computational engineering and Wang’s background in diagnostics of low-resource development and business to develop their project.
Last week, the pair won a $1,375 award in a pitch competition at eHub Collegetown through the Clinton Global Initiative University.
CGIU enables innovators to meet, discuss and create innovative solutions to social issues around the world. Students create Commitments of Action to solve challenges in global communities in many areas, including public health.
The MRI software the pair developed will allow for earlier detection of chronic diseases and cancer, according to Srivastava. If the pair succeeds in reducing scan times and therefore cutting patient wait time, more patients can be scanned in the same amount of time it currently takes to scan one individual.
“Through a business opportunity, we reinforced with CGIU that our project will reduce costs because if diseases are diagnosed early with improved MRI efficiency, this will lead to less healthcare cost to treat these diseases, which solves a societal problem,” Srivastava said.
“Furthermore, CGIU emphasized the importance of improving access to healthcare, which our work will do,” Wang said.
According to Srivastava, MRIs currently pose several challenges, ranging from the financial cost of obtaining an MRI scan to the inaccessibility of MRIs across the world — and even in the United States.
“In the United States, patients must wait two to four weeks to get an MRI even though the U.S. has the second highest number of MRI machines in the world,” Wang said.
Even if the appropriate infrastructure is available, patients cannot access MRIs readily unless in emergencies. This consequence is graver in less developed regions of the world, where MRI machines are in lower supply.
When hospital consultants studied the operation efficiency of MRI machines, they found that scan time — rather than the quantity of MRI machines in the U.S. — is the main reason patients face long wait times.
The team hopes that their work can lead to less patient backlog and greater efficiency.
“Despite MRIs being the best to diagnose diseases, it is the last to be employed because people say that it is too costly so why not use cheaper methods,” Srivastava said.
Srivastava, who studies nuclear magnetic resonance — the technology used for MRI machines — hopes that the software will reduce background noise in NMR readings. This noise leads to an increased amount of time for an MRI machine to scan patients.
Furthermore, the cost of an MRI correlates to the time it takes to conduct a scan. By lowering the cost of an MRI machine, hospitals and countries that do not have strong healthcare systems can spend less money operating the machine and can use them in lower risk cases, such as earlier detection of diseases and cancer.
Srivastava and Wang are already beginning a pilot study at the National Cancer Institute over spring break. Wang also added that they hoped to further develop their startup over the summer using their connections with manufacturing companies like Siemens.