5 minute read
CESAREAN DELIVERIES ARE RISING IN RWANDA – AI COULD REDUCE THE RISKS: Smartphone app helps health workers detect post surgery infections
Over the past 2 decades, Rwanda, like many other countries, has witnessed a significant increase in cesarean deliveries. Nearly 16% of the nation’s newborns were delivered by the surgical procedure in 2020, up from about 2% in 2000, according to recent research. The rise has been fueled by improved maternal health services and increased access to affordable care, researchers say, but also greater demand for the procedure from more affluent patients.
Although the use of cesarean delivery reduces the risk of morbidity among mothers and babies, it also poses problems. In particular, the surgical wounds can become infected, leading to illness and even death. That risk is particularly acute in rural areas where medical care can be scarce.
“With the increasing number of cesarean deliveries, you’re going to see more complications,” says Bethany Hedt-Gauthier, a biostatistician at Harvard Medical School. “It is important to monitor for those complications in a way that is feasible, acceptable, and safe for the mother.”
Now, Hedt-Gauthier is part of a research project field testing a mobile phone app that uses artificial intelligence (AI) to detect infections, potentially speeding treatment.
“The app is helping us assist the local community without the need to visit a health center,” says Laban Bikorimana, research coordinator at the Rwanda office of Partners in Health (PIH), a Boston-based nonprofit that is testing the app.
Research conducted at a hospital in Kirehe, a rural district in Rwanda’s Eastern province, has highlighted the infection threat. There, a 2019 study in which doctors examined women 10 days after cesarean delivery found about 11% had bacterial infections. By comparison, the infection rate is about 7% in more developed countries. The study, published in the British Journal of Surgery, found that Rwandans can find postoperative care—which includes monitoring infections and changing wound dressings—burdensome, in part because they must make long, costly trips to the hospital.
Community health workers in Kirehe can now use the mobile app— developed by an interdisciplinary team from the Massachusetts Institute of Technology (MIT), Harvard, and PIH—to take a picture of surgical wounds. The software then uses computer-vision techniques and AI to detect signs of infection. Initial studies show that the app, which can be used without an internet connection, is able to diagnose infections with roughly 90% accuracy within 10 days of childbirth. Once a problem is recognized, the health worker provides the appropriate care or advises the patient visit a doctor.
Before the app, researchers had tested several strategies for addressing infections. They provided health workers with short questionnaires to help them identify problems, for example. But “identifying and monitoring postcesarean wounds is the responsibility of doctors, and teaching the community workers to do exactly the same thing was very challenging,” says Vincent Cubaka, a physician and director of research and training at PIH’s Rwanda office.
The team then explored automating the process, which came with its own challenges. First, researchers needed to collect highquality images of cesarean wounds to train the underlying algorithm. But variations in phone camera settings, lighting, and other conditions affected image quality. “The problem is you give me a camera and I will take a photo from one particular angle, but another photographer might use a different angle,” Hedt-Gauthier says.
To create consistent images, the researchers deployed software that automatically scaled, color calibrated, cropped, and rotated the photos. “All the images were now exactly the same size, the same magnification, and square,” says MIT engineer Richard Fletcher. “That’s perfect data to use.”
The researchers are now improving the app so it can be used across more diverse populations such as in Ghana and parts of South America. “In Rwanda the homogeneity of the skin tones was fairly high,” but the current version doesn’t work well with people with lighter skins, Fletcher says. The team is now experimenting with using a thermal camera where the brightness of the image is a function of the skin temperature rather than skin color.
To avoid misuse of apps that use AI, Fletcher says doctors and clinicians should be informed about the data that were used to train the software. “Otherwise, I think there is a strong danger of AI models being used where they were not intended to,” Fletcher says. “Then you get bad results.”
Training local health workers to use the app can be a challenge, Bikorimana says. “Some of them had never even touched a smartphone.” Still, he sees promise. “I can see [it] being implemented throughout Rwanda.”
The Dalla Lana School of Public Health at the University of Toronto supported this reporting.
