Worldwide, almost 140 million women give birth each year. A considerable fraction of this population will experience childbirth-related post-traumatic stress disorder (CB-PTSD), and about one-third of them may experience significant acute stress or trauma as a result of giving birth. In the past, serious sexual assault or military combat has been linked to PTSD. However, the recognition of delivery as a significant trigger for PTSD has grown in recent years. 

Approximately 8 million women give birth each year to children worldwide, and the current standard of care for diagnosing CB-PTSD necessitates a time-consuming and expensive physician evaluation.  

A successful screening technique could quickly and affordably identify a significant proportion of postpartum patients who would benefit from a diagnosis and course of treatment.  

Breastfeeding, developing a bond with the baby, and wanting to get pregnant again may all be hampered by untreated CB-PTSD. Additionally, it might exacerbate maternal depression, which raises the risk of suicidal thoughts and actions. 

AI to understand early signs of CB-PTSD 

By analyzing the brief narrative comments of patients who have given birth, researchers have modified an Artificial Intelligence software to detect signs of childbirth-related post-traumatic stress disorder (CB-PTSD). The software effectively determined a sizable fraction of participants who were likely to develop the disorder, and with additional improvements such as information from medical records and data on birth experiences from various populations, the model might be able to determine a sizable fraction of individuals who are at risk.

Leading author Sharon Dekel, PhD, of Massachusetts General Hospital and Harvard Medical School, Boston, oversaw the study, which Alon Bartal, PhD, of Bar Ilan University in Israel, carried out. The work was published in Scientific Reports. 

Identifying the accuracy of the model 

Investigators gave 1,295 postpartum individuals the CB-PTSD checklist, a questionnaire intended to screen for the disorder. Also, participants shared brief accounts of their delivery experiences, lasting no more than thirty words. After that, a subset of patient narratives from those who also scored highly on the questionnaire for CB-PTSD symptoms were analyzed by researchers using an AI model they had developed. Subsequently, a second selection of narratives was examined using the model to look for indications of CB-PTSD. In general, the model accurately recognized the stories of individuals who, based on their high questionnaire scores, were most likely to have CB-PTSD. 

The authors hope that their research will eventually level the playing field in terms of socioeconomic, racial, and ethnic disparities by making the diagnosis of post-traumatic stress disorder related to delivery readily accessible.  

• https://www.nature.com/articles/s41598-024-54242-2
• Photo by Hollie Santos on Unsplash