과소활동성기형정자증을 동반한 남성 불임의 전통 의학 증후군 평가

• Study Rationale and Methodology Oligoasthenoteratozoospermia (OAT) is a significant contributor to male infertility, characterized by concurrent abnormalities in sperm concentration, motility, and morphology. While Traditional Medicine has been recognized by the World Health Organization (WHO) for its role in improving semen parameters, the classification of TM syndromes often relies on subjective clinical experience. This study utilizes Latent Tree Models (LTMs), a sophisticated probabilistic graphical model, to objectively identify the distribution and characteristics of TM syndromes in men with OAT.

• Phase 1: Literature-Based Framework Development The study begins with a systematic survey of classical and modern TM literature published between July 2025 and October 2025.

  • Source Selection: Literature includes classical texts recognized by the WHO/WPRO, textbooks from major medical universities in Vietnam and China, and expert consensus from Traditional Medicine associations.
  • Survey Tool Construction: Symptoms and syndromes related to male infertility are extracted and tabulated. Symptoms with a frequency of higher 30% in the literature are selected to build the formal clinical survey questionnaire.
  • Standardization: All Traditional Medicine terms are standardized according to WHO international terminologies.

• Phase 2: Clinical Implementation and Data Collection Clinical data will be collected at the Department of Andrology, Binh Dan Hospital, from November 2025 to August 2026.

Clinical Screening: Patients are first diagnosed with OAT by an andrologist based on the WHO Laboratory Manual (6th Edition).

Traditional Examination: Eligible participants undergo a non-invasive TM examination, including the "Four Examinations" (observation, listening/smelling, inquiring, and palpation).

Symptom Mapping: Symptoms are recorded as binary variables (presence or absence) to facilitate mathematical modeling.

*Statistical Analysis Using Latent Tree Models (LTMs) The core analysis employs the Lantern 5.0 software to discover the hidden (latent) structure of TM syndromes.

• Structure Learning: The Extension-Adjustment-Simplify-Transfer (EAST) algorithm is used to automatically group symptoms that frequently co-occur or are mutually exclusive.
• Parameter Estimation: The Expectation-Maximization (EM) algorithm estimates the probability of each patient belonging to a specific latent syndrome class.
• Syndrome Identification: Latent variables are interpreted and named as TM syndromes based on the symptoms that provide at least 95% Cumulative Mutual Information (CMI).
• Classification Algorithm: The study establishes a scoring threshold for each syndrome based on Naïve Bayes principles, allowing for a quantitative diagnosis of TM patterns in the OAT population.