Clinical trial NCT06899490: "Machine Learning Analysis of Lingual Colorimetry and MADRS Anxiety-Depression Score in Acupuncture Patients"
Official title: Machine Learning Analysis of Lingual Colorimetry and MADRS Score in Acupuncture Patients: a Prospective Observational Study
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Anxiety Disorders
Keywords: Tongue Colorimetry
MADRS
Depression
Anxiety
Machine Learning
Acupuncture
Artificial Intelligence
Image Analysis
Lead sponsor: Centre Hospitalier de Narbonne
Sponsor class: OTHER
First posted: 2025-03-28
Enrollment: 350
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: This is a prospective observational monocentric study conducted in an acupuncture practice. The study aims to establish a correlation between tongue colorimetric features and MADRS scores in patients consulting for symptoms related to anxiety.

Tongue photography and MADRS assessment present no risk to participants. A non-opposition declaration for photography and consent for anonymous and confidential data analysis will be obtained.

Brief summary: This observational study aims to assess the potential relationship between tongue colorimetry (using standardized photographic techniques) and anxiety-depression scores measured by the Montgomery-Åsberg Depression Rating Scale (MADRS) in acupuncture patients. Data will be analyzed using machine learning methods to determine whether tongue color features correlate with MADRS scores, possibly contributing to a novel, non-invasive diagnostic tool for anxiety and depression assessment in clinical practice.

Participation involves only tongue photography and completion of questionnaires, without any invasive procedures or treatment modifications.

Detailed description: 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.

Eligibility criteria: Inclusion Criteria:

* Patients aged 18 years or older
* Native French speakers
* No medical conditions or medications affecting tongue coloration
* Consulting for acupuncture with symptoms involving an anxious component

Exclusion Criteria:

* Presence of psychosis or substance abuse
* Eating or taking medication within one hour before the examination
* Refusal to complete the Montgomery-Åsberg Depression Rating Scale (MADRS) questionnaire
* Refusal to have their tongue photographed

Primary outcomes:
measure: Correlation using Machine Learning between Tongue Colorimetric Features and MADRS Scores
timeFrame: Baseline (single evaluation at enrollment)
Secondary outcomes:
measure: Machine Learning Analysis of Tongue Features and MADRS Scores
timeFrame: Baseline (single evaluation at enrollment)
Other outcomes:
Locations:
city: Narbonne
country: France
facility: Cabinet Médical d'Acupuncture
Study officials:
References: