A groundbreaking study led by Penn State researchers introduces a revolutionary tool that empowers doctors to examine placentas at the bedside using just a smartphone. Published in the Dec. 13 edition of Patterns, the tool employs computer vision and artificial intelligence (AI) to enhance placental examination, especially in resource-limited settings, improving neonatal care and saving lives by enabling early identification of critical conditions.
The placenta is a critical organ in pregnancy, serving as the lifeline between the parent and the baby. However, it is often discarded without thorough analysis after birth, missing opportunities to detect health issues early, such as neonatal sepsis, a life-threatening infection. With complications from such conditions affecting millions of newborns globally, this new technology could significantly improve health outcomes for both mothers and babies.
The tool, called PlacentaCLIP+, was designed to address the gaps in placental examination. Led by doctoral candidate Yimu Pan and under the guidance of distinguished professor James Z. Wang, the research team developed a machine learning model capable of detecting abnormalities from photos of placentas. The model identifies risks, including neonatal sepsis, that could prompt immediate clinical actions such as antibiotic administration.
The research team gathered over 31,700 anonymized placental images, paired with pathology reports, spanning a 12-year period from the United States and Uganda. By using cross-modal contrastive learning, a technique that links different types of data, the team trained the model to analyze placental images and predict health outcomes, making this tool highly effective in a variety of medical settings.
PlacentaCLIP+ is poised to benefit both low-resource and high-resource settings. In areas where hospitals lack pathology labs or specialists, the tool can quickly identify infection or other issues. In well-equipped hospitals, it can help prioritize which placentas require further examination, streamlining the process and improving efficiency.
Designed to be user-friendly, the tool could potentially operate through a smartphone app or be integrated into existing medical record software. The researchers have tested it under real-world conditions—such as blurry or poorly lit photos—confirming its effectiveness and consistency across diverse populations.
Researchers plan to integrate PlacentaCLIP+ into a broader system called PlacentaVision, which would offer a mobile app for medical professionals with limited resources. The app aims to enable quick, efficient placenta examinations with minimal training, improving care for mothers and infants at risk.
In the future, the researchers also intend to enhance the tool’s capabilities by including additional placental features and clinical data, refining predictions and expanding its research contributions on long-term health outcomes.
Alison D. Gernand, a Penn State associate professor, emphasizes the potential for this tool to transform neonatal and maternal care worldwide, particularly in areas where placental examinations are infrequent. As the tool evolves, it promises greater accessibility to timely interventions that can prevent severe health outcomes and improve the lives of mothers and infants globally.
The collaborative research effort also involved contributors from Boston Children’s Hospital, Harvard Medical School, Massachusetts General Hospital, and Mbara University of Science and Technology. The National Institutes of Health (NIH) National Institute of Biomedical Imaging and Bioengineering funded the study, with supercomputing resources provided through the ACCESS program supported by the U.S. National Science Foundation.
As researchers continue to refine the PlacentaCLIP+ model, this innovative tool offers hope for improving neonatal and maternal health care across the globe, particularly in regions with limited medical resources.
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