AI model has potential to detect risk of childbirth-related post-traumatic stress disorder

Researchers adapted an artificial intelligence (AI) program to identify signs of childbirth-related post-traumatic stress disorder (CB-PTSD) by evaluating short narrative statements from patients who have given birth. The program was successful in identifying a large proportion of participants likely to have the condition, and with further improvements, such as medical record details and childbirth experience data from diverse populations, the model could potentially identify a high percentage of people at risk. The study, funded by the National Institutes of Health, appears in Scientific Reports.

Worldwide, CB-PTSD affects approximately 8 million people giving birth each year, and the current practice to diagnose CB-PTSD requires medical evaluation, which is time-consuming and expensive. An effective screening method has the potential to quickly and inexpensively identify a large number of postpartum patients who could benefit from diagnosis and treatment. Untreated CB-PTSD can interfere with breastfeeding, bonding with the infant, and desire for a future pregnancy. It can also worsen maternal depression, which can lead to suicidal thoughts and behaviors.

Investigators administered the CB-PTSD Checklist, which is a questionnaire designed to screen for the disorder, to 1,295 postpartum people. Participants also provided short stories of approximately 30 words about their birth experience. The researchers then trained an AI model to analyze a subset of patient stories that also tested high for CB-PTSD symptoms on the questionnaire. Next, the model was used to analyze a different subset of narratives for evidence of CB-PTSD. Overall, the model correctly identified the narratives of participants likely to have CB-PTSD because they scored high on the questionnaire.

The authors believe their work could eventually make the diagnosis of post-traumatic stress disorder in childbirth more accessible, providing a way to compensate for past socioeconomic, racial and ethnic disparities.

The study was conducted by Alon Bartal, Ph.D., of Bar Ilan University in Israel, and led by lead author Sharon Dekel, Ph.D., of Massachusetts General Hospital and Harvard Medical School in Boston. Funding was provided by the NIH Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD).

Source: NIH