AI model offers breakthrough in early detection of postpartum depression

A new machine learning model shows promise in identifying parents at high risk for postpartum depression, potentially enabling targeted preventive care for those most vulnerable to this widespread condition. This predictive technology represents a significant step forward in maternal mental health care, addressing a condition that affects 17% of birthing parents globally and often goes undetected until symptoms become severe.

The big picture: Researchers have developed an AI tool that can identify individuals with a high likelihood of developing postpartum depression, allowing healthcare providers to focus limited mental health resources where they’re most needed.

• The model identified a high-risk group in which 30% went on to develop postpartum depression—three times the average rate among study participants.
• The research specifically focused on people without a prior history of depression in the year before delivery, targeting those who might otherwise fly under the clinical radar.

Key details: The predictive model was built and validated using data from more than 29,000 people who gave birth in the United States between 2017 and 2022.

• Researchers used electronic health records and postpartum depression screening scores to train the model on approximately half the dataset and validate it on the remainder.
• The findings were published in the American Journal of Psychiatry on May 19, 2025.

What they’re saying: “If we know that someone’s at higher risk, we might try to develop strategies to help prevent depression,” explains Roy Perlis, a psychiatrist at Mass General Brigham and co-author of the study.

Why this matters: Early identification of at-risk parents could enable timely interventions such as therapy and stress management techniques before symptoms develop or worsen.

• Postpartum depression causes intense feelings of sadness, anxiety, or despair and can occur anytime within the first year after childbirth.

Behind the numbers: While the model isn’t perfect—70% of those flagged as high-risk did not develop postpartum depression—researchers argue its predictive value still represents meaningful clinical utility.

• “Even some degree of prediction can be helpful because we simply don’t have the resources to give everyone the follow-up care that we wish we could as far as postpartum mental health,” Perlis notes.