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  Published Paper Details:

  Paper Title

A Comprehensive Review on Artificial Intelligence Based Approaches for Prenatal Depression Detection

  Authors

  F HILDA,  K ANUSUDHA

  Keywords

Prenatal depression, Artificial Intelligence (AI), Clinical interviews, Questionnaires and Physiological signals.

  Abstract


Prenatal depression is a critical health condition that may have significant consequences not only on the maternal health but also on the outcomes of fetal health. Early intervention is crucial as it helps to prevent the potential long-term effects and avoid them. This survey paper provides an in-depth analysis of various Artificial Intelligence (AI) methods used for the detection of prenatal depression based on different data sources, including clinical interviews, questionnaires and physiological data. The paper highlights their effectiveness, strengths and limitations of these AI-based approaches. This review analyzed the various performance metrics in the detection of prenatal depression. This study aims to provide a comprehensive insight into existing methodologies and identify the scope for future research, ultimately aiming to improve both the mother's mental health and the child's growth.

  IJCRT's Publication Details

  Unique Identification Number - IJCRT2510087

  Paper ID - 294628

  Page Number(s) - a671-a678

  Pubished in - Volume 13 | Issue 10 | October 2025

  DOI (Digital Object Identifier) -   

  Publisher Name - IJCRT | www.ijcrt.org | ISSN : 2320-2882

  E-ISSN Number - 2320-2882

  Cite this article

  F HILDA,  K ANUSUDHA,   "A Comprehensive Review on Artificial Intelligence Based Approaches for Prenatal Depression Detection", International Journal of Creative Research Thoughts (IJCRT), ISSN:2320-2882, Volume.13, Issue 10, pp.a671-a678, October 2025, Available at :http://www.ijcrt.org/papers/IJCRT2510087.pdf

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Call For Paper December 2025
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ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
ISSN and 7.97 Impact Factor Details


ISSN
ISSN
ISSN: 2320-2882
Impact Factor: 7.97 and ISSN APPROVED
Journal Starting Year (ESTD) : 2013
ISSN
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