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Depression: Overview

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Depression is a prevalent mental illness marked by persistent sadness and lack of interest in previously enjoyable activities. It can take several forms, including major depression, persistent depressive disorder, and bipolar I and II disorders. Symptoms range from emotional changes like chronic worry to physical changes like sleep disturbances and suicidal thoughts. From a neurobiological perspective, depression is believed to be triggered by abnormalities in the brain's prefrontal cortex,...

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Updated: Jan 13, 2026

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

Using a Murine Model of Psychosocial Stress in Pregnancy as a Translationally Relevant Paradigm for Psychiatric Disorders in Mothers and Infants

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AI for Detecting and Predicting Postpartum Depression: Scoping Review.

Mais Alkhateeb^1,2,3, Ajisha Nayeem², Arfan Ahmed²

¹College of Education and Art, Lusail University, Doha, Qatar, +974440119502.

Journal of Medical Internet Research

|January 8, 2026

Summary

This summary is machine-generated.

Artificial intelligence (AI) shows promise in identifying mothers at risk for postpartum depression (PPD). However, current research on AI for PPD detection and prediction needs more robust validation and diverse data.

Keywords:

artificial intelligence computer-aided diagnosis deep learning machine learning maternal mental health natural language processing perinatal depression postpartum depression prediction models

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Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents

Using Chronic Social Stress to Model Postpartum Depression in Lactating Rodents

Published on: June 10, 2013

Area of Science:

Medical Informatics
Computational Psychiatry
Machine Learning in Healthcare

Background:

Postpartum depression (PPD) affects a significant portion of mothers globally, necessitating improved early detection methods.
Existing screening tools for PPD often lack scalability and predictive accuracy.
Artificial intelligence (AI), encompassing machine learning, deep learning, and natural language processing, offers enhanced capabilities for accurately identifying mothers at risk of PPD.

Purpose of the Study:

To systematically map and analyze the existing literature on artificial intelligence (AI)-based methods for detecting and predicting postpartum depression (PPD).

Main Methods:

A comprehensive scoping review was conducted following PRISMA-ScR guidelines.
Empirical studies utilizing AI for PPD detection or prediction were systematically searched across eight major databases.
Data extraction focused on study characteristics, AI models, data sources, preprocessing, validation, and performance metrics, followed by narrative synthesis.

Main Results:

65 studies met inclusion criteria, with the majority from the United States and published in 2024.
AI models were predominantly used for PPD prediction (80%) over detection (22%), with sociodemographic, psychological, and obstetric data being primary inputs.
Machine learning, particularly ensemble-based boosting models, showed superior performance, though studies often lacked external validation and standardized reporting.

Conclusions:

The review highlights the dominance of classical machine learning in AI-driven PPD research.
There is limited adoption of deep learning and advanced preprocessing techniques, alongside inconsistent validation strategies.
Future research should address limitations such as small sample sizes, geographic bias, and the need for standardized, multimodal data approaches for more reliable PPD prediction.