Protecting maternal health in Rwanda

The world is going through a maternal well being disaster. In response to the World Well being Group, roughly 810 ladies die every day because 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 group 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 pc imaginative and prescient to foretell an infection in C-section wounds with roughly 90 p.c accuracy.

“Early detection of an infection is a crucial difficulty worldwide, however in low-resource areas resembling rural Rwanda, the issue is much more dire because of an absence of educated docs and the excessive prevalence of bacterial infections which are proof against antibiotics,” says Richard Ribon Fletcher ’89, SM ’97, PhD ’02, analysis scientist in mechanical engineering at MIT and expertise lead for the group. “Our concept was to make use of cellphones that may very well be utilized by neighborhood well being staff to go to new moms of their houses and examine their wounds to detect an infection.”

This summer season, the group, 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 girls who ship by Cesarean part within the creating world are compromised by each restricted entry to high quality surgical procedure and postpartum care,” provides Fredrick Kateera, a group 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 can be a scalable recreation changer in optimizing ladies’s well being.”

Coaching algorithms to detect an infection

The mission’s inception was the results of a number of probability 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 tasks in Rwanda for 5 years at that time, was searching for an answer for the hole in Cesarean care she and her collaborators had encountered of their analysis. Particularly, she was curious about exploring the usage of mobile phone cameras as a diagnostic instrument.

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

“As soon as we realized that some of these image-based algorithms might assist home-based care for ladies after Cesarean supply, we approached Dr. Fletcher as a collaborator, given his in depth expertise in creating 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 scholar from Rwanda and would later be part of Fletcher’s group at MIT. With Fletcher’s mentorship, throughout his senior 12 months, Nakeshimana based Insightiv, a Rwandan startup that’s making use of AI algorithms for evaluation of medical photos, 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 photos taken by neighborhood well being staff in rural Rwanda. They collected over 1,000 photos of each contaminated and non-infected wounds after which educated an algorithm utilizing that information.

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

“The standard of wound photos collected by the well being staff was extremely variable and it required a considerable amount of guide labor to crop and resample the pictures. Since these photos 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 difficulty, Fletcher turned to instruments he utilized in earlier tasks: real-time pc imaginative and prescient and augmented actuality.

Enhancing picture high quality with real-time picture processing

To encourage neighborhood well being staff to take higher-quality photos, Fletcher and the group 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 pc imaginative and prescient software program.

Well being staff are instructed to position the body over the wound and open the app, which gives real-time suggestions on the digicam 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 elements of the pc imaginative and prescient software program will then mechanically stability the colour, crop the picture, and apply transformations to appropriate for parallax.

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

Utilizing convolutional neural internet (CNN) machine studying fashions, together with a way referred to 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. Girls who’re predicted to have an an infection by means of the app are then given a referral to a clinic the place they’ll obtain diagnostic bacterial testing and will be prescribed life-saving antibiotics as wanted.

The app has been effectively obtained by ladies and neighborhood well being staff in Rwanda.

“The belief that girls have in neighborhood well being staff, who have been a giant promoter of the app, meant the mHealth instrument 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 expertise to a extra international viewers is algorithmic bias. When educated on a comparatively homogenous inhabitants, resembling that of rural Rwanda, the algorithm performs as anticipated and may efficiently predict an infection. However when photos of sufferers of various pores and skin colours are launched, the algorithm is much less efficient.

To sort out this difficulty, Fletcher used thermal imaging. Easy thermal digicam modules, designed to connect to a mobile phone, value roughly $200 and can be utilized to seize infrared photos of wounds. Algorithms can then be educated utilizing the warmth patterns of infrared wound photos to foretell an infection. A examine revealed final 12 months confirmed over a 90 p.c prediction accuracy when these thermal photos have been paired with the app’s CNN algorithm.

Whereas dearer than merely utilizing the telephone’s digicam, the thermal picture strategy may very well be used to scale the group’s mHealth expertise to a extra numerous, international inhabitants.

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

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

Along with creating the library of wound photos used within the algorithms, Fletcher is working carefully with former scholar Nakeshimana and his group 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 group appears to develop the great app for maternal well being, privateness and information safety are a prime precedence.

“As we develop and refine these instruments, a better consideration have to be paid to sufferers’ information privateness. Extra information safety particulars must be included in order that the instrument 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 group embody: Bethany Hedt-Gauthier from Harvard Medical College; Richard Fletcher from MIT; Robert Riviello from Brigham and Girls’s Hospital; Adeline Boatin from Massachusetts Basic Hospital; Anne Niyigena, Frederick Kateera, Laban Bikorimana, and Vincent Cubaka from PIH in Rwanda; and Audace Nakeshimana ’20, founding father of Insightiv.ai.