Defending maternal well being in Rwanda | MIT Information

The world is dealing with a maternal well being disaster. In accordance with the World Well being Group, roughly 810 ladies die every day as a consequence of preventable causes associated to being pregnant and childbirth. Two-thirds of those deaths happen in sub-Saharan Africa. In Rwanda, one of many main causes of maternal mortality is contaminated Cesarean part wounds.

An interdisciplinary crew of docs and researchers from MIT, Harvard College, and Companions in Well being (PIH) in Rwanda have proposed an answer to handle this downside. They’ve developed a cellular well being (mHealth) platform that makes use of synthetic intelligence and real-time laptop imaginative and prescient to foretell an infection in C-section wounds with roughly 90 p.c accuracy.

“Early detection of an infection is a vital challenge worldwide, however in low-resource areas akin to rural Rwanda, the issue is much more dire as a consequence of a scarcity of educated docs and the excessive prevalence of bacterial infections which are immune to antibiotics,” says Richard Ribon Fletcher ’89, SM ’97, PhD ’02, analysis scientist in mechanical engineering at MIT and know-how lead for the crew. “Our concept was to make use of cellphones that could possibly be utilized by neighborhood well being employees to go to new moms of their houses and examine their wounds to detect an infection.”

This summer season, the crew, which is led by Bethany Hedt-Gauthier, a professor at Harvard Medical College, was awarded the $500,000 first-place prize within the NIH Know-how Accelerator Problem for Maternal Well being.

“The lives of ladies who ship by Cesarean part within the growing world are compromised by each restricted entry to high quality surgical procedure and postpartum care,” provides Fredrick Kateera, a crew member from PIH. “Use of cellular well being applied sciences for early identification, believable correct analysis of these with surgical web site infections inside these communities could be a scalable sport changer in optimizing ladies’s well being.”

Coaching algorithms to detect an infection

The mission’s inception was the results of a number of likelihood encounters. In 2017, Fletcher and Hedt-Gauthier ran into one another on the Washington Metro throughout an NIH investigator assembly. Hedt-Gauthier, who had been engaged on analysis initiatives in Rwanda for 5 years at that time, was looking for an answer for the hole in Cesarean care she and her collaborators had encountered of their analysis. Particularly, she was interested by exploring using mobile phone cameras as a diagnostic software.

Fletcher, who leads a gaggle of scholars in Professor Sanjay Sarma’s AutoID Lab and has spent many years making use of telephones, machine studying algorithms, and different cellular applied sciences to world well being, was a pure match for the mission.

“As soon as we realized that most of these image-based algorithms might help home-based care for ladies after Cesarean supply, we approached Dr. Fletcher as a collaborator, given his in depth expertise in growing mHealth applied sciences in low- and middle-income settings,” says Hedt-Gauthier.

Throughout that very same journey, Hedt-Gauthier serendipitously sat subsequent to Audace Nakeshimana ’20, who was a brand new MIT pupil from Rwanda and would later be a part of Fletcher’s crew at MIT. With Fletcher’s mentorship, throughout his senior yr, Nakeshimana based Insightiv, a Rwandan startup that’s making use of AI algorithms for evaluation of scientific pictures, and was a prime grant awardee on the annual MIT IDEAS competitors in 2020.

Step one within the mission was gathering a database of wound pictures taken by neighborhood well being employees in rural Rwanda. They collected over 1,000 pictures of each contaminated and non-infected wounds after which educated an algorithm utilizing that knowledge.

A central downside emerged with this primary dataset, collected between 2018 and 2019. Most of the images had been of poor high quality.

“The standard of wound pictures collected by the well being employees was extremely variable and it required a considerable amount of handbook labor to crop and resample the photographs. Since these pictures are used to coach the machine studying mannequin, the picture high quality and variability basically limits the efficiency of the algorithm,” says Fletcher.

To unravel this challenge, Fletcher turned to instruments he utilized in earlier initiatives: real-time laptop imaginative and prescient and augmented actuality.

Enhancing picture high quality with real-time picture processing

To encourage neighborhood well being employees to take higher-quality pictures, Fletcher and the crew revised the wound screener cellular app and paired it with a easy paper body. The body contained a printed calibration colour sample and one other optical sample that guides the app’s laptop imaginative and prescient software program.

Well being employees are instructed to put the body over the wound and open the app, which supplies real-time suggestions on the digital camera placement. Augmented actuality is utilized by the app to show a inexperienced test mark when the telephone is within the correct vary. As soon as in vary, different components of the pc imaginative and prescient software program will then mechanically stability the colour, crop the picture, and apply transformations to right for parallax.

“Through the use of real-time laptop imaginative and prescient on the time of knowledge assortment, we’re in a position to generate lovely, clear, uniform color-balanced pictures that may then be used to coach our machine studying fashions, with none want for handbook knowledge cleansing or post-processing,” says Fletcher.

Utilizing convolutional neural internet (CNN) machine studying fashions, together with a way known as switch studying, the software program has been in a position to efficiently predict an infection in C-section wounds with roughly 90 p.c accuracy inside 10 days of childbirth. Ladies who’re predicted to have an an infection by way of the app are then given a referral to a clinic the place they’ll obtain diagnostic bacterial testing and could be prescribed life-saving antibiotics as wanted.

The app has been properly acquired by ladies and neighborhood well being employees in Rwanda.

“The belief that girls have in neighborhood well being employees, who had been a giant promoter of the app, meant the mHealth software was accepted by ladies in rural areas,” provides Anne Niyigena of PIH.

Utilizing thermal imaging to handle algorithmic bias

One of many largest hurdles to scaling this AI-based know-how to a extra world viewers is algorithmic bias. When educated on a comparatively homogenous inhabitants, akin to that of rural Rwanda, the algorithm performs as anticipated and may efficiently predict an infection. However when pictures of sufferers of various pores and skin colours are launched, the algorithm is much less efficient.

To deal with this challenge, Fletcher used thermal imaging. Easy thermal digital camera modules, designed to connect to a mobile phone, price roughly $200 and can be utilized to seize infrared pictures of wounds. Algorithms can then be educated utilizing the warmth patterns of infrared wound pictures to foretell an infection. A examine revealed final yr confirmed over a 90 p.c prediction accuracy when these thermal pictures had been paired with the app’s CNN algorithm.

Whereas dearer than merely utilizing the telephone’s digital camera, the thermal picture method could possibly be used to scale the crew’s mHealth know-how to a extra numerous, world inhabitants.

“We’re giving the well being workers two choices: in a homogenous inhabitants, like rural Rwanda, they’ll use their customary telephone digital camera, utilizing the mannequin that has been educated with knowledge from the native inhabitants. In any other case, they’ll use the extra common mannequin which requires the thermal digital camera attachment,” says Fletcher.

Whereas the present technology of the cellular app makes use of a cloud-based algorithm to run the an infection prediction mannequin, the crew is now engaged on a stand-alone cellular app that doesn’t require web entry, and in addition appears to be like in any respect features of maternal well being, from being pregnant to postpartum.

Along with growing the library of wound pictures used within the algorithms, Fletcher is working carefully with former pupil Nakeshimana and his crew at Insightiv on the app’s growth, and utilizing the Android telephones which are regionally manufactured in Rwanda. PIH will then conduct person testing and field-based validation in Rwanda.

Because the crew appears to be like to develop the great app for maternal well being, privateness and knowledge safety are a prime precedence.

“As we develop and refine these instruments, a better consideration should be paid to sufferers’ knowledge privateness. Extra knowledge safety particulars must be integrated in order that the software addresses the gaps it’s meant to bridge and maximizes person’s belief, which can ultimately favor its adoption at a bigger scale,” says Niyigena.

Members of the prize-winning crew embrace: Bethany Hedt-Gauthier from Harvard Medical College; Richard Fletcher from MIT; Robert Riviello from Brigham and Ladies’s Hospital; Adeline Boatin from Massachusetts Normal Hospital; Anne Niyigena, Frederick Kateera, Laban Bikorimana, and Vincent Cubaka from PIH in Rwanda; and Audace Nakeshimana ’20, founding father of Insightiv.ai.