Evaluating Traditional Medicine Syndromes in Male Infertility With Oligoasthenoteratozoospermia

Male Infertility With OAT

Traditional Medicine Pattern, OAT, Male Infertility, Oligoasthenoteratozoospermia

The goal of this observational study is to identify and evaluate the characteristics of Traditional Medicine (TM) syndromes in men aged 18 to 60 with male infertility and oligoasthenoteratozoospermia (OAT) syndrome. The main questions it aims to answer are: * What are the common Traditional Medicine syndromes and symptoms associated with male infertility based on Traditional Medicine literature? * What are the specific Traditional Medicine syndromes and symptoms found in men with OAT syndrome at Binh Dan Hospital when analyzed using Latent Tree Models? The research will be conducted in two phases: * Phase 1 (Literature Review): Researchers will collect and analyze Traditional Medicine texts to list the symptoms and syndromes related to male infertility. This phase will help create a standardized clinical survey. * Phase 2 (Clinical Survey): Researchers will recruit 300 male participants with OAT syndrome. * Participants will answer a survey questionnaire about their Traditional Medicine symptoms. * Researchers will apply Latent Tree Models (a mathematical approach) to the collected data to objectively classify the TM syndromes.

* 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.

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