Background: To identify novel gene combinations and to develop an early diagnostic model for Polycystic Ovary Syndrome (PCOS) through the integration of artificial neural networks (ANN) and random forest (RF) methods. Methods: We retrieved and processed gene expression datasets for PCOS from the Gene Expression Omnibus (GEO) database. Differential expression analysis of genes (DEGs) within the training set was performed using the “limma” R package. Enrichment analyses on DEGs using gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG), and immune cell infiltration. The identification of critical genes from DEGs was then performed using random forests, followed by the developing of new diagnostic models for PCOS using artificial neural networks. Results: We identified 130 up-regulated genes and 132 down-regulated genes in PCOS compared to normal samples. Gene Ontology analysis revealed significant enrichment in myofibrils and highlighted crucial biological functions related to myofilament sliding, myofibril, and actin-binding. Compared with normal tissues, the types of immune cells expressed in PCOS samples are different. A random forest algorithm identified 10 significant genes proposed as potential PCOS-specific biomarkers. Using these genes, an artificial neural network diagnostic model accurately distinguished PCOS from normal samples. The diagnostic model underwent validation using the independent validation set, and the resulting area under the receiver operating characteristic curve (AUC) values was consistent with the anticipated outcomes. Conclusion: Utilizing unique gene combinations, this research created a diagnostic model by merging random forest techniques with artificial neural networks. The AUC indicated a notably superior performance of the diagnostic model.

Introduction The therapeutic needs of women with diminished ovarian reserve (DOR), coupled with the increasing application of acupuncture in improving ovarian function, have highlighted the need to verify the efficacy and safety of acupuncture for DOR. This study aims to provide high-quality evidence by evaluating both ovarian reserve and in vitro fertilisation (IVF) outcomes. Methods and analysis A large-scale, multicentre, randomised controlled trial will be carried out across seven hospitals in China. 400 women with DOR will be randomised in a 1:1 ratio to an acupuncture group or a sham acupuncture group. Acupuncture or sham acupuncture will consist of 36 sessions per participant over 12 weeks. The primary outcome will be the change in antral follicle count (AFC) at week 12 from baseline. Secondary outcomes are AFC at week 24, the serum levels of basal follicle-stimulating hormone and anti-Mullerian hormone at weeks 12 and 24, the scores of the Self-Rating Anxiety Scale at weeks 12 and 24, clinical pregnancy rate, and IVF embryo transfer related outcomes. Any adverse events during treatment will be documented. Ethics and dissemination The study protocol has been approved by all the participating institutions. Written informed consent will be obtained prior to participant enrolment. The results of this study will be published in peer-reviewed publications. Trial registration number ChiCTR2200062295 Protocol version V2.0-20220317