Machine learning analysis of posturography in panic disorder
A pilot study for objective physiological biomarker identification
Bibliographic Data
| ID | 15522661 |
|---|---|
| Authors | Luiz Antônio Vesco Gaiotto (Faculdade de Ciências Médicas da Santa Casa de São Paulo, corresponding author), Felipe O Aguiar (0000-0002-1013-2088, German Research Centre for Artificial Intelligence), Thales Marcon (Faculdade de Ciências Médicas da Santa Casa de São Paulo), Joseph J Gallo (0000-0002-7878-0321, Faculdade de Ciências Médicas da Santa Casa de São Paulo), Julia Souza Gallo, Lucas Murrins Marques (0000-0003-2999-2448, German Research Centre for Artificial Intelligence), Ricardo Riyoiti Uchida (0000-0002-4209-8830, German Research Centre for Artificial Intelligence) |
| Year | 2025 |
| Volume | 16 |
| Pages | 1663556-1663556 |
| Publication date | 2025-10-16 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Psychiatry (JOURNAL) |
| Journal identifiers | ISSN: 1664-0640 • E-ISSN: 1664-0640 |
| Publisher | Frontiers Media (PUBLISHER • CH) |
| DOI | 10.3389/fpsyt.2025.1663556 |
| PMID | 41179807 |
| OpenAlex | W4415245441 |
| Language | EN |
| References cited | 24 |
This is the first study applying ML to posturography for identifying physiological markers of panic disorder. Using ML for stabilometric data improves classification accuracy, highlighting static posturography as superior to clinical screening tools like the Patient Health Questionnaire for Panic Disorder (PHQ-PD) and Panic Disorder Severity Scale (PDSS). Larger, externally validated cohorts and portable measurement solutions are needed to translate these findings into routine clinical assessment
Biomarker · Identification (biology · Panic · Panic disorder · Posturography · Functional Brain Connectivity Studies · Heart Rate Variability and Autonomic Control · Mental Health Research Topics
| Highly cited | No |
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