Helping Doctors Make Better Decisions About Surgical Intervention During Childbirth
Multi-disciplinary Team from Stony Brook University Receives $3.2 Million Award from NIH
Since the 1970’s, the number of C-sections performed during childbirth has climbed from two percent to 36 percent. While more women are electing to have the procedure, that is not the main driver for this dramatic increase. Most C-sections are the result of doctor decisions during childbirth. According to studies in more than 20 countries that were reviewed by Trinity College in Dublin and published in Science Daily in 2018, a key driver is doctors’ fear of litigation.
CEAS Faculty: Co-PI IV Ramakrishnan and PI Petar Djurić
During labor, a fetus can be deprived of adequate levels of oxygen. If the oxygen supply drops below a certain threshold, asphyxia occurs, which can lead to permanent brain damage or even death of the fetus/newborn. Current technology employs fetal heart rate (FHR) and uterine activity monitoring to inform decisions taken in the delivery room. The use of these patterns has not reduced the number of unwanted neurologic outcomes despite the increase in surgical interventions, in contrast to what had been widely expected.
A multi-disciplinary team led by the College of Engineering and Applied Sciences (CEAS) has received $3.2 million under the National Institutes of Health (NIH) Research Project Grant Program (RO1) to investigate machine learning methods for classification of intrapartum signals (FHR and uterine activity) that has the potential to significantly outperform the accuracy of contemporary methods. The project is called “Rethinking Electronic Fetal Monitoring to Improve Perinatal Outcomes and Reduce Frequency of Operative Vaginal and Cesarean Deliveries.”
“Dr. Gerald Quirk and I began looking at this problem five-plus years ago, receiving a small grant to do some exploratory research,” says Professor Petar Djurić, Principal Investigator (PI) and Chair of the Department of Electrical and Computer Engineering . “Through our initial work, we discovered what we believe is a way to use machine learning to ‘see’ what doctors can’t see to provide data-driven guidance for decision-making in the delivery room. This should serve to greatly reduce the number of surgical interventions while leading to better fetal outcomes. It will also lower healthcare costs as C-sections are much more expensive than normal deliveries.”
PI Dr. Gerard Quirk, Renaissance School of Medicine,
Co-PI Christian Luhmann, College of Arts and Sciences
“Indeed,” affirms Dr. Quirk, “we believe that through the collaborative efforts of our team, coming from different disciplines, we will develop systems able to more precisely identify the fetus truly at risk for an adverse neurologic outcome such as cerebral palsy or hypoxic/ischemic encephalopathy while sparing the majority of women who undergo a cesarean for an “abnormal” fetal heart rate pattern but in fact deliver a normal baby.”
Because the intrapartum signals (FHR and uterine activity) are not the only source of information about the fetus, the proposed methods also exploit various physiological data that are acquired on a routine basis such as age and ethnicity of the mother and whether the fetus is a first child or not. The emphasis of this research is on sequential signal processing methods that will capture the dynamics of fetus well-being on a minute-by-minute basis. It is expected that the high accuracy of the proposed methods will place them at the heart of computerized decision support systems, which in turn will pave the way for the wide adoption of these systems in the future.
“This project exemplifies convergence research at the intersection of electrical and computer engineering, computer science and medicine. It is exactly the kind of project we envisioned taking shape at Stony Brook when we established our two brand new institutes on Engineering-Driven Medicine and AI Driven Discovery and Innovation. It clearly illustrates how physicians can leverage AI algorithms and big data to improve the efficacy of clinical interventions and save lives."
“Another key outcome of this project will be to build a large, de-identified database on deliveries that will continue to increase the accuracy of information available in real-time for doctors in the delivery room,” adds IV Ramakrishnan, Co-PI, Associate Dean of Research and Professor of Computer Science. “It will be an open source learning tool using language familiar to doctors that they can access. Plus, what we are creating will benefit areas of medicine beyond childbirth. For example, the applied methodology may help neurologists monitor brain signals and assist in bringing patients in coma back to normal, if possible.”
Perhaps one of the most crucial pieces for the development of this program is the
addition of a psychological component. An understanding of how doctors make decisions
is critical to optimizing the exchange between doctor and machine to ensure the best
choice is made in any
“Even the most powerful machine learning systems will be ineffective if human users
do not incorporate the provided recommendations into their decision making,” explains
Dr. Luhmann. "Successful adoption of the proposed system will require doctors to thoroughly
trust the guidance it generates. Medical doctors are highly trained experts and are
unlikely to embrace 'black box' recommendations.”
Principal Investigators (PIs):
Petar M. Djurić , Chair, Department of Electrical and Computer Engineering, CEAS
Gerald Quirk MD, Professor Obstetrics, Gynecology and Reproductive Medicine, Stony Brook Medicine