Evidence map›Paper›PMID 41618255›Full record

ArticleBMC psychiatry2026

Voice-based machine learning for rapid screening of bipolar disorder and major depressive disorder in children and adolescents: a robust and low-complexity diagnostic model.

Zhaojun Li, Xushan Li, Zhuo Wang, Jie Gao, Jie Luo, Fan He, Yi Zheng, Lihui Feng, Jihua Lu

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Zhaojun LiSchool of Optics and Photonics, Beijing Institute of Technology, Beijing, China.
Xushan LiMental Health Education and Counseling Center, Beijing Institute of Technology, Beijing, China.
Zhuo WangSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
Jie GaoSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
Jie LuoBeijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders, National Clinical Research Center for Mental Disorders, Capital Medical University, Beijing, China.
Fan HeBeijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders, National Clinical Research Center for Mental Disorders, Capital Medical University, Beijing, China.
Yi ZhengBeijing Key Laboratory of Mental Disorders, Beijing Anding Hospital, Beijing Institute for Brain Disorders, National Clinical Research Center for Mental Disorders, Capital Medical University, Beijing, China.
Lihui FengSchool of Optics and Photonics, Beijing Institute of Technology, Beijing, China. lihui.feng@bit.edu.cn.
Jihua LuSchool of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China. lujihua@bit.edu.cn.

Funding

Beijing Hospitals Authority Clinical Medicine Development of Special Funding Support ZYLX202128Capital's Funds for Health Improvement and Research CFH2024-1-2123High-level Public Health Technology Talent Construction Project Discipline Backbone-01-029National Key Research and Development Program of China 2022YFC2405500National Natural Science Foundation of China 62075012
6 · The paper itself

Abstract

backgroundMajor depressive disorder (MDD) and bipolar disorder (BD) are psychiatric disorders that seriously impact physical and mental health. They are increasingly prevalent among children and adolescents, and the absence of objective physiological indicators makes diagnosis difficult. However, existing studies have primarily focused on adults, and few practical diagnostic tools have been developed and clinically deployed. Therefore, we investigated the voice features of children and adolescents and proposed a low-complexity automatic detection method for early recognition and self-screening.

methodsA reading paradigm with 7 segments of text is applied for voice data collection. After dividing the recording, a well-developed feature set is extracted, and the double feature selection method is proposed to select the most effective features. Finally, traditional classification models are applied to reduce complexity.

resultsThe energy, spectral slope, amplitude spectrum, and RASTA-style filtered auditory spectrum of voice are effective features. Results show that 92.4% and 95.6% for voice and subject accuracy are achieved in the ternary classification of 50 BD, 50 MDD, and 50 healthy controls (HC). Besides the satisfactory accuracies, the robustness to recording devices and environments is validated.

conclusionsVoice features are potential biomarkers for diagnosing psychiatric disorders in children and adolescents. Based on optimized feature selection algorithms, traditional classifiers can achieve accurate and robust classification of BD, MDD, and HC with a small number of interpretable features, providing a feasible tool for auxiliary diagnosis.

Indexed as

Bipolar DisorderMachine LearningMajor Depressive DisorderVoiceAdolescentChildClassification AlgorithmsFemaleHumansMaleBipolar disorderChild and adolescentLow-complexityMajor depressive disorderRobustVoice features

Identifiers

PMID41618255
PMCPMC12860134

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LicenceCC BY-NC-ND
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.