by A recent study published in Scientific Reports suggests that an artificial intelligence (AI) model has the potential to detect signs of childbirth-related post-traumatic stress disorder (CB-PTSD) by analyzing short narrative statements provided by postpartum patients.
By incorporating additional details from medical records and birth experiences of diverse populations, the model could potentially identify a large percentage of individuals at risk of this disorder.
Currently, diagnosing CB-PTSD typically involves a physician evaluation, which is both time-consuming and costly. Having an effective screening method, such as the AI model studied, could enable rapid and cost-effective identification of postpartum patients who may benefit from diagnosis and treatment.
Untreated CB-PTSD can have serious consequences, including difficulties with breastfeeding, bonding with the infant, and implications for future pregnancies. It may also exacerbate maternal depression, leading to suicidal thoughts and behaviors.
During the study, participants completed the CB-PTSD Checklist and provided brief narratives about their childbirth experiences. The AI model was trained to analyze the narratives of participants who exhibited high CB-PTSD symptoms on the questionnaire. Subsequently, the model successfully identified narratives that indicated a likelihood of CB-PTSD based on questionnaire scores.
The researchers suggest that their findings could potentially improve the accessibility of diagnosing childbirth post-traumatic stress disorder, addressing past disparities based on socioeconomic status, race, and ethnicity.
The study was led by senior author Sharon Dekel, Ph.D., of Massachusetts General Hospital and Harvard Medical School, Boston, and conducted by Alon Bartal, Ph.D., of Bar Ilan University in Israel.
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1. Source: Coherent Market Insights, Public sources, Desk research
2. We have leveraged AI tools to mine information and compile it
