A Study on An Intelligent Diagnosis and Treatment Assistant System For Acupuncture in Diminished Ovarian Reserve...

Diminished ovarian reserve (DOR) is increasing in incidence and affecting younger women. Acupuncture has been used clinically for DOR; however, practice remains subjective, experience-dependent, and lacks standardized guideline implementation. Building on our previous general intelligent acupuncture system, this study targets gynecological diseases and uses DOR as the clinical entry point to develop a specialized knowledge graph and decision-support system. This study included 82 classical acupuncture texts, Chinese and English literature, and clinical data. Knowledge was extracted using a hybrid approach combining large language models and manual annotation to construct a full acupuncture knowledge graph and a DOR-specific gynecology subgraph. The system employs BiLSTM-CRF-based entity recognition and dual-mode retrieval to automate syndrome differentiation and acupuncture prescription generation. The full graph contains 16,558 entities and 80,084 relations, while the gynecology subgraph contains 8,677 entities and 27,092 relations. In a clinical evaluation of 90 patients with DOR, the system achieved a diagnostic agreement rate of 86.7% compared with expert assessments. The mean prescription appropriateness score was 4.65. Inter-rater agreement among experts, measured using Kendall's coefficient of concordance, was W ≈ 0.13 (P < 0.05), indicating statistically significant but limited agreement. These findings suggest that the system can provide structured support for acupuncture-based syndrome differentiation and prescription generation in DOR, with potential as an auxiliary clinical decision-support tool.