"Machine Learning Analysis of Lingual Colorimetry and MADRS Anxiety-Depression Score in Acupuncture Patients"

This prospective observational study investigates the correlation between lingual colorimetry, captured using readily available and standardized modern photographic tools (iPhone cameras), and anxiety-depression scores evaluated by the Montgomery-Åsberg Depression Rating Scale (MADRS) among patients attending routine acupuncture consultations.

Participants will undergo a simple and non-invasive photographic recording of their tongue using an iPhone, ensuring consistent lighting and standardized positioning to minimize variability. Simultaneously, participants will complete the MADRS questionnaire, a widely validated instrument for assessing anxiety and depression severity. No invasive procedures or therapeutic interventions beyond their usual acupuncture care will be performed.

The acquired photographic data will be analyzed using machine learning algorithms to identify potential predictive relationships between distinct colorimetric characteristics of the tongue and the MADRS scores. The objective is to determine whether lingual imaging could serve as a reliable, non-invasive biomarker or complementary diagnostic tool for assessing psychological status in clinical practice.

This approach leverages everyday technology (smartphones), promoting ease of replication and broader accessibility in clinical environments. Ultimately, findings from this study could facilitate early detection and monitoring of anxiety-depressive disorders, thus enhancing individualized patient care in complementary and integrative medicine.

• Machine Learning Analysis of Tongue Features and MADRS Scores [Срок оценки: Baseline (single evaluation at enrollment)]