Smartphone app for assessing placentas set to improve neonatal and after-birth care

The placenta, the temporary organ which grows to support foetal development in the uterus, contains crucial information about the health of both the parent and baby. Still, it is not often thoroughly examined at birth, especially in areas with limited medical resources. This can mean the opportunity for early detection of the critical condition of neonatal sepsis, which affects millions of newborns globally each year, is missed.

A multi-national, multi-institutional team led by researchers at Penn State university have developed a new tool that enables doctors to examine placentas right at the bedside using just a smart phone. The tool harnesses computer vision ─ a field of artificial intelligence (AI) that uses machine learning and neural networks to teach computers and systems to derive meaningful information from digital images, videos and other visual inputs ─ and other AI to make placenta examination more accessible, even in a low-resource institutional setting.

The researchers’ goal was to create an accurate, robust tool based on data-driven learning that could be used to reduce complications and improve outcomes across a range of medical demographics, according to James Z. Wang, distinguished professor in the College of IST and one of the principal investigators on the study.

The researchers used cross-modal contrastive learning, an AI method for aligning and understanding the relationship between different types of data ─ in this case visual images and textual pathological reports ─ to teach a computer program how to analyse pictures of placentas. They developed a large dataset of more than 31,700 anonymised placental images and accompanying pathological reports spanning a 12-year period from the United States and Uganda and studied how the images relate to health outcomes. With this understanding, they built the PlacentaCLIP+ model to make predictions based on new images.

“We developed PlacentCLIP+, [which is] a robust machine learning model that can analyse photos of placentas to detect abnormalities and risks such as neonatal sepsis and other critical conditions,” Wang said.

“This early identification might enable clinicians to take prompt actions, such as administering antibiotics to the parent or baby and closely monitoring the newborn for signs of infection.”

“In low-resource areas ─ places where hospitals don’t have pathology labs or specialists ─ this tool could help doctors quickly spot issues like infections from a placenta,” said Yimu Pan, a doctoral candidate in the informatics program in the College of Information Sciences and Technology and lead author on the study.

“In well-equipped hospitals, the tool can help doctors determine which placentas need further, detailed examination, making the process more efficient and prioritising the most important cases.”

According to the researchers, the PlacentaCLIP+ program is designed to be easy to use and could potentially work through a smartphone app or be integrated into medical record software so doctors can get quick answers after delivery.

The team tested the program under different conditions to see how it handled real-world challenges, like blurry or poorly lit photos, and validated it cross-nationally, confirming consistent performance across populations.

The researchers said they plan to make the tool even smarter by including more types of placental features and adding clinical data to improve predictions while also contributing to research on long-term health. They’ll also test the tool in a variety of settings across different hospitals.

“This tool has the potential to transform how placentas are examined after birth, especially in parts of the world where these exams are rarely done,” said Alison D. Gernand, associate professor the Penn State College of Health and Human Development (HHD) Department of Nutritional Sciences and the corresponding author on the project.

“This innovation promises greater accessibility in both low- and high-resource settings. With further refinement, it has the potential to transform neonatal and maternal care by enabling early, personalized interventions that prevent severe health outcomes and improve the lives of mothers and infants worldwide.”

To read more: 10.1016/j.patter.2024.101097 

-- EurekAlert.org