AI-Powered PlacentaVision Tool Revolutionizes Neonatal Care Through Rapid Placenta Analysis

AI-driven tool revolutionizes placenta examination at bedside

Penn StateDec 17 2024 The placenta, the temporary organ grown to support fetal development, plays a vital role during pregnancy and contains crucial information about the health of both the parent and the baby. Yet, it is often not thoroughly examined at birth, especially in areas with limited medical resources. This can lead to missed opportunities for early detection and intervention of critical conditions such as neonatal sepsis, a life-threatening infection that affects millions of newborns globally. A multi-national, multi-institutional team led by Penn State researchers developed a new tool that enables doctors to examine placentas right at the bedside using just a phone. The tool harnesses computer vision and artificial intelligence to make placenta examination more accessible for low-resource and more-advanced health care institutions alike. The work was published in the Dec. 13 print edition of Patterns and featured on the journal's cover. This research could save lives and improve health outcomes. It could make placental examination more accessible, benefitting research and care for future pregnancies, especially for mothers and babies at higher risk of complications." Yimu Pan, doctoral candidate in the informatics program in the College of Information Sciences and Technology and lead author on the study Most placentas are quickly discarded without thorough analysis, according to the researchers. This means potentially vital health information that clinicians could use to identify concerns earlier is often missed. 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. "We developed PlacentCLIP+, a robust machine learning model that can analyze 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." The researchers used cross-modal contrastive learning, an artificial intelligence 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 analyze pictures of placentas. They developed a large dataset of more than 31,700 anonymized 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. "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," Pan said. "In well-equipped hospitals, the tool can help doctors determine which placentas need further, detailed examination, making the process more efficient and prioritizing 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. "Our next steps include embedding this model into a larger program that we created, called PlacentaVision, to offer medical professionals in clinics or hospitals with limited resources, where neonatal health outcomes are poor, a user-friendly mobile app," Pan said. "The app would require minimal training and allow doctors and nurses to photograph placentas and get immediate feedback and improve care." 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." According to Jeffery A. Goldstein, director of perinatal pathology at Northwestern University Feinberg School of Medicine and a principal investigator on the study, placenta is one of the most common specimens seen in his lab. "When the neonatal intensive care unit is treating a sick kid, even a few minutes can make a difference in medical decision making," he said. "With a diagnosis from these photographs, we can have an answer days earlier than we would in our normal process." In addition to Gernand, Pan and Wang, Penn State contributors included Kelly Gallagher, assistant research professor in the Ross and Carol Neese College of Nursing; Manas Mehta, a doctoral student in the College of IST; and Rachel Walker, a postdoctoral scholar in the College of HHD Department of Nutritional Sciences. Researchers from Boston Children's Hospital, Harvard Medical School, Massachusetts General Hospital and Mbara University of Science and Technology contributed to this work. The National Institutes of Health National Institute of Biomedical Imaging and Bioengineering supported this research. The researchers used supercomputing resources provided through the Advanced Cyberinfrastructure Coordination Ecosystem: Services and Support (ACCESS) program, funded by the U.S. National Science Foundation. Penn State Journal reference: Pan, Y., et al. (2024). Cross-modal contrastive learning for unified placenta analysis using photographs. Patterns. doi.org/10.1016/j.patter.2024.101097.

AI tool could revolutionize placenta examination and improve neonatal care

Northwestern UniversityDec 14 2024 A newly developed tool that harnesses computer vision and artificial intelligence (AI) may help clinicians rapidly evaluate placentas at birth, potentially improving neonatal and maternal care, according to new research from scientists at Northwestern Medicine and Penn State. The study, which was published Dec. 13 in the print edition of the journal Patterns and featured on the journal's cover, describes a computer program named PlacentaVision that can analyze a simple photograph of the placenta to detect abnormalities associated with infection and neonatal sepsis, a life-threatening condition that affects millions of newborns globally. Placenta is one of the most common specimens that we see in the lab. When the neonatal intensive care unit is treating a sick kid, even a few minutes can make a difference in medical decision making. With a diagnosis from these photographs, we can have an answer days earlier than we would in our normal process." Dr. Jeffery Goldstein, study co-author, director of perinatal pathology and associate professor of pathology at Northwestern University Feinberg School of Medicine Northwestern provided the largest set of images for the study, and Goldstein led the development and troubleshooting of the algorithms. Alison D. Gernand, contact principal investigator on the project, conceived the original idea for this tool through her global health work, particularly with pregnancies where women deliver in their homes due to lack of health care resources. "Discarding the placenta without examination is a common but often overlooked problem," said Gernand, associate professor in the Penn State College of Health and Human Development (HHD) Department of Nutritional Sciences. "It is a missed opportunity to identify concerns and provide early intervention that can reduce complications and improve outcomes for both the mother and the baby." Why early examination of the placenta matters The placenta plays a vital role in the health of both the pregnant individual and baby during pregnancy, yet it is often not thoroughly examined at birth, especially in areas with limited medical resources. "This research could save lives and improve health outcomes," said Yimu Pan, a doctoral candidate in the informatics program from the College of Information Sciences and Technology (IST) and lead author on the study. "It could make placental examination more accessible, benefitting research and care for future pregnancies, especially for mothers and babies at higher risk of complications." Early identification of placental infection through tools like PlacentaVision might enable clinicians to take prompt actions, such as administering antibiotics to the mother or baby and closely monitoring the newborn for signs of infection, the scientists said. PlacentaVision is intended for use across a range of medical demographics, according to the researchers. "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," Pan said. "In well-equipped hospitals, the tool may eventually help doctors determine which placentas need further, detailed examination, making the process more efficient and ensuring the most important cases are prioritized." "Before such a tool can be deployed globally, core technical obstacles we faced were to make the model flexible enough to handle various diagnoses related to the placenta and to ensure that the tool can be robust enough to handle various delivery conditions, including variation in lighting conditions, imaging quality and clinical settings" said James Z. Wang, distinguished professor in the College of IST at Penn State and one of the principal investigators on the study. "Our AI tool needs to maintain accuracy even when many training images come from a well-equipped urban hospital. Ensuring that PlacentaVision can handle a wide range of real-world conditions was essential." How the tool learned how to analyze pictures of placentas The researchers used cross-modal contrastive learning, an AI method for aligning and understanding relationship between different types of data -; in this case, visual (images) and textual (pathological reports) -; to teach a computer program how to analyze pictures of placentas. They gathered a large, diverse dataset of placental images and pathological reports spanning a 12-year period, studied how these images relate to health outcomes and built a model that could make predictions based on new images. The team also developed various image alteration strategies to simulate different photo-taking conditions so the model's resilience can be evaluated properly. The result was PlacentaCLIP+, a robust machine-learning model that can analyze photos of placentas to detect health risks with high accuracy. It was validated cross-nationally to confirm consistent performance across populations. According to the researchers, PlacentaVision is designed to be easy to use, potentially working through a smartphone app or integrated into medical record software so doctors can get quick answers after delivery. Next step: A user-friendly app for medical staff "Our next steps include developing a user-friendly mobile app that can be used by medical professionals -; with minimal training -; in clinics or hospitals with low resources," Pan said. "The user-friendly app would allow doctors and nurses to photograph placentas and get immediate feedback and improve care." The researchers 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 different hospitals to ensure it works in a variety of settings. "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," Gernand said. "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." This research was supported by the National Institutes of Health National Institute of Biomedical Imaging and Bioengineering (grant R01EB030130). The team used supercomputing resources from the National Science Foundation-funded Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program. Northwestern University Journal reference: Pan, Y., et al. (2024). Cross-modal contrastive learning for unified placenta analysis using photographs. Patterns. doi.org/10.1016/j.patter.2024.101097.

AI tool analyzes placentas at birth for faster detection of neonatal, maternal problems

The study, which was published Dec. 13 in the print edition of the journal Patterns and featured on the journal's cover, describes a computer program named PlacentaVision that can analyze a simple photograph of the placenta to detect abnormalities associated with infection and neonatal sepsis, a life-threatening condition that affects millions of newborns globally. "Placenta is one of the most common specimens that we see in the lab," said study co-author Dr. Jeffery Goldstein, director of perinatal pathology and an associate professor of pathology at Northwestern University Feinberg School of Medicine. "When the neonatal intensive care unit is treating a sick kid, even a few minutes can make a difference in medical decision making. With a diagnosis from these photographs, we can have an answer days earlier than we would in our normal process." Northwestern provided the largest set of images for the study, and Goldstein led the development and troubleshooting of the algorithms. Alison D. Gernand, contact principal investigator on the project, conceived the original idea for this tool through her global health work, particularly with pregnancies where women deliver in their homes due to lack of health care resources. "Discarding the placenta without examination is a common but often overlooked problem," said Gernand, associate professor in the Penn State College of Health and Human Development (HHD) Department of Nutritional Sciences. "It is a missed opportunity to identify concerns and provide early intervention that can reduce complications and improve outcomes for both the mother and the baby." Why early examination of the placenta matters The placenta plays a vital role in the health of both the pregnant individual and baby during pregnancy, yet it is often not thoroughly examined at birth, especially in areas with limited medical resources. "This research could save lives and improve health outcomes," said Yimu Pan, a doctoral candidate in the informatics program from the College of Information Sciences and Technology (IST) and lead author on the study. "It could make placental examination more accessible, benefitting research and care for future pregnancies, especially for mothers and babies at higher risk of complications." Early identification of placental infection through tools like PlacentaVision might enable clinicians to take prompt actions, such as administering antibiotics to the mother or baby and closely monitoring the newborn for signs of infection, the scientists said. PlacentaVision is intended for use across a range of medical demographics, according to the researchers. "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," Pan said. "In well-equipped hospitals, the tool may eventually help doctors determine which placentas need further, detailed examination, making the process more efficient and ensuring the most important cases are prioritized." "Before such a tool can be deployed globally, core technical obstacles we faced were to make the model flexible enough to handle various diagnoses related to the placenta and to ensure that the tool can be robust enough to handle various delivery conditions, including variation in lighting conditions, imaging quality and clinical settings" said James Z. Wang, distinguished professor in the College of IST at Penn State and one of the principal investigators on the study. "Our AI tool needs to maintain accuracy even when many training images come from a well-equipped urban hospital. Ensuring that PlacentaVision can handle a wide range of real-world conditions was essential." How the tool learned how to analyze pictures of placentas The researchers used cross-modal contrastive learning, an AI method for aligning and understanding relationship between different types of data -- in this case, visual (images) and textual (pathological reports) -- to teach a computer program how to analyze pictures of placentas. They gathered a large, diverse dataset of placental images and pathological reports spanning a 12-year period, studied how these images relate to health outcomes and built a model that could make predictions based on new images. The team also developed various image alteration strategies to simulate different photo-taking conditions so the model's resilience can be evaluated properly. The result was PlacentaCLIP+, a robust machine-learning model that can analyze photos of placentas to detect health risks with high accuracy. It was validated cross-nationally to confirm consistent performance across populations. According to the researchers, PlacentaVision is designed to be easy to use, potentially working through a smartphone app or integrated into medical record software so doctors can get quick answers after delivery. Next step: A user-friendly app for medical staff "Our next steps include developing a user-friendly mobile app that can be used by medical professionals -- with minimal training -- in clinics or hospitals with low resources," Pan said. "The user-friendly app would allow doctors and nurses to photograph placentas and get immediate feedback and improve care." The researchers 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 different hospitals to ensure it works in a variety of settings. "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," Gernand said. "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." This research was supported by the National Institutes of Health National Institute of Biomedical Imaging and Bioengineering (grant R01EB030130). The team used supercomputing resources from the National Science Foundation-funded Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program.

AI tool analyzes placentas at birth for faster detection of neonatal and maternal problems

A newly developed tool that harnesses computer vision and artificial intelligence (AI) may help clinicians rapidly evaluate placentas at birth, potentially improving neonatal and maternal care, according to new research from scientists at Northwestern Medicine and Penn State. The study, which is published in the journal Patterns and featured on the cover, describes a computer program named PlacentaVision that can analyze a simple photograph of the placenta to detect abnormalities associated with infection and neonatal sepsis, a life-threatening condition that affects millions of newborns globally. "Placenta is one of the most common specimens that we see in the lab," said study co-author Dr. Jeffery Goldstein, director of perinatal pathology and an associate professor of pathology at Northwestern University Feinberg School of Medicine. "When the neonatal intensive care unit is treating a sick kid, even a few minutes can make a difference in medical decision making. With a diagnosis from these photographs, we can have an answer days earlier than we would in our normal process." Northwestern provided the largest set of images for the study, and Goldstein led the development and troubleshooting of the algorithms. Alison D. Gernand, contact principal investigator on the project, conceived the original idea for this tool through her global health work, particularly with pregnancies where women deliver in their homes due to lack of health care resources. "Discarding the placenta without examination is a common but often overlooked problem," said Gernand, associate professor in the Penn State College of Health and Human Development (HHD) Department of Nutritional Sciences. "It is a missed opportunity to identify concerns and provide early intervention that can reduce complications and improve outcomes for both the mother and the baby." Why early examination of the placenta matters The placenta plays a vital role in the health of both the pregnant individual and baby during pregnancy, yet it is often not thoroughly examined at birth, especially in areas with limited medical resources. "This research could save lives and improve health outcomes," said Yimu Pan, a doctoral candidate in the informatics program from the College of Information Sciences and Technology (IST) and lead author on the study. "It could make placental examination more accessible, benefitting research and care for future pregnancies, especially for mothers and babies at higher risk of complications." Early identification of placental infection through tools like PlacentaVision might enable clinicians to take prompt actions, such as administering antibiotics to the mother or baby and closely monitoring the newborn for signs of infection, the scientists said. PlacentaVision is intended for use across a range of medical demographics, according to the researchers. "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," Pan said. "In well-equipped hospitals, the tool may eventually help doctors determine which placentas need further, detailed examination, making the process more efficient and ensuring the most important cases are prioritized." "Before such a tool can be deployed globally, core technical obstacles we faced were to make the model flexible enough to handle various diagnoses related to the placenta and to ensure that the tool can be robust enough to handle various delivery conditions, including variation in lighting conditions, imaging quality and clinical settings" said James Z. Wang, distinguished professor in the College of IST at Penn State and one of the principal investigators on the study. "Our AI tool needs to maintain accuracy even when many training images come from a well-equipped urban hospital. Ensuring that PlacentaVision can handle a wide range of real-world conditions was essential." How the tool learned how to analyze pictures of placentas The researchers used cross-modal contrastive learning, an AI method for aligning and understanding relationship between different types of data -- in this case, visual (images) and textual (pathological reports) -- to teach a computer program how to analyze pictures of placentas. They gathered a large, diverse dataset of placental images and pathological reports spanning a 12-year period, studied how these images relate to health outcomes and built a model that could make predictions based on new images. The team also developed various image alteration strategies to simulate different photo-taking conditions so the model's resilience can be evaluated properly. The result was PlacentaCLIP+, a robust machine-learning model that can analyze photos of placentas to detect health risks with high accuracy. It was validated cross-nationally to confirm consistent performance across populations. According to the researchers, PlacentaVision is designed to be easy to use, potentially working through a smartphone app or integrated into medical record software so doctors can get quick answers after delivery. Next step: A user-friendly app for medical staff "Our next steps include developing a user-friendly mobile app that can be used by medical professionals -- with minimal training -- in clinics or hospitals with low resources," Pan said. "The user-friendly app would allow doctors and nurses to photograph placentas and get immediate feedback and improve care." The researchers 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 different hospitals to ensure it works in a variety of settings. "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," Gernand said. "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."