Name: A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph (Video) | JoVE Journal
Uploaded: 2026-05-29T15:20:00.000+00:00
Duration: 8 min 43 s
Description: Learn how to develop an intelligent acupuncture diagnosis and treatment system for diminished ovarian reserve using knowledge graphs and BiLSTM-CRF models. This method achieved 86.7% diagnostic agreement with experts in clinical evaluation.

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This research was supported by the Jiangsu Provincial Science and Technology Plan Special Foundation (BE2022712) and the Special Research Project on the Development Plan of Traditional Chinese Medicine Technology in Jiangsu Province (ZT202120).

This study was conducted in accordance with institutional ethical standards and was approved by the Ethics Committee of the General Hospital of Eastern Theater Command (Approval No. DZQH-KYLLFS-25-38). Written informed consent was obtained from all participants prior to inclusion. All patient data were anonymized and de-identified to ensure privacy protection. The research tools for this protocol have been listed in the Table of Materials.
1. Knowledge graph construction
The knowledge graph was constructed using a multi-source data integration strategy. Data collection and annotation were performed by trained personnel with formal education in Traditional Chinese Medicine (TCM) and acupuncture. Data sources included classical acupuncture texts, Chinese-language literature from CNKI, English-language literature from the Web of Science Core Collection, clinical data from reproductive centers at tertiary hospitals, and standardized knowledge bases (The detailed list of classical texts is available in Supplementary File 1). These sources were selected to ensure both historical depth and contemporary clinical relevance. A total of 400 clinical cases were included to support the identification of diagnostic and therapeutic patterns. The overall workflow of knowledge graph construction is illustrated in Figure 1. 
<img alt="Knowledge extraction process using LLMs; diagram showing manual annotation and graph construction." class="xfigimg" src="/files/ftp_upload/71182/71182fig1.jpg" data-altupdatedat="1784001604000" data-alt="Figure 1"> 
Figure 1: Knowledge graph construction workflow. Overview of the data collection, extraction, and integration process used to construct the knowledge graph. <a href="https://www.jove.com/files/ftp_upload/71182/71182fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Inclusion criteria required that all data be directly related to acupuncture diagnosis and treatment, with complete and clearly structured content suitable for entity and relationship extraction. Classical texts were required to be critically collated and free of obvious errors, while modern literature was limited to peer-reviewed publications or documents issued by authoritative institutions. Clinical data were required to be de-identified and ethically approved. Exclusion criteria included duplicate records, incomplete text fragments, content unrelated to acupuncture diagnosis and treatment, and unsupported or erroneous statements.
A hybrid knowledge extraction approach was implemented, combining manual annotation and automated extraction. Approximately 30% of the data, including classical texts and semantically complex content, was manually annotated by domain experts using a standardized annotation platform. The remaining 70% of the data were processed using large language models (LLMs) for automated extraction, including entity recognition, relationship extraction, and terminology normalization. All automatically generated results were reviewed and corrected by experts to ensure accuracy and consistency.
Automated extraction was performed using structured prompt templates designed to guide LLM behavior (Template examples are presented in Supplementary File 2). These templates defined the model role as a domain-specific knowledge extraction expert and incorporated few-shot learning examples in JSON triplet format to improve extraction performance. Preprocessed literature abstracts were submitted through application programming interfaces (APIs), and parallel processing strategies were implemented to improve computational efficiency. Extraction outputs were parsed and validated using JSON validation tools to ensure structural integrity. Validated knowledge triplets were serialized into independent JSON files, named according to their source documents to maintain traceability.
The finalized knowledge graph was stored in a graph database and implemented using Python-based frameworks. Visualization was performed using built-in graph database tools to support inspection and validation.
Entities in the knowledge graph were categorized into seven types: diseases, symptoms, syndrome patterns, acupoints, meridians, treatment methods, and effector substances and biological mechanisms. Relationships between entities were constructed to reflect clinical diagnostic and therapeutic logic, supporting multi-hop reasoning and structured inference. The ontology and relationship structure are illustrated in Figure 2. The detailed knowledge graph schema and examples of relationship constraints are provided in Supplementary File 3 and Supplementary File 4.
<img alt="Medical diagnosis flowchart, diagram: Syndrome, symptom, examination, treatment methods." class="xfigimg" src="/files/ftp_upload/71182/71182fig2.jpg" data-altupdatedat="1784001604000" data-alt="Figure 2"> 
Figure 2: Knowledge graph ontology and relationship model. Schematic representation of entity types and relationships within the knowledge graph. Core entities include disease, symptom, syndrome, acupoint, and treatment method, and they illustrate diagnostic and therapeutic relationships. 
<a href="https://www.jove.com/files/ftp_upload/71182/71182fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
2. Application system development
The application system was developed based on the constructed knowledge graph to enable clinical decision support. The system processes user input data and performs structured analysis through natural language processing and knowledge graph reasoning.
Natural language understanding was implemented using the BiLSTM-CRF model for named entity recognition and entity linking; the model architecture is shown in Figure 3. The model architecture consisted of an embedding layer trained on domain-specific corpora, a bidirectional long short-term memory layer to capture contextual information, and a conditional random field layer to ensure optimal sequence labeling. The model was trained on manually annotated clinical and literature data, with annotation performed independently by domain experts. Inter-annotator agreement was assessed using Cohen’s kappa coefficient, with a threshold of ≥0.85. Discrepancies were resolved through expert consensus.
<img alt="Neural network diagram of transformer architecture, layers with input-output flow, sequence modeling." class="xfigimg" src="/files/ftp_upload/71182/71182fig3.jpg" data-altupdatedat="1784001604000" data-alt="Figure 3"> 
Figure 3: BiLSTM-CRF model architecture. Schematic diagram of the BiLSTM-CRF model used for named entity recognition, including embedding, BiLSTM, and CRF layers. 
<a href="https://www.jove.com/files/ftp_upload/71182/71182fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
During inference, the model extracted structured entities, including symptoms, diseases, and syndrome patterns, from clinical input data. These entities were mapped to standardized nodes in the knowledge graph using terminology normalization, cosine similarity-based vector matching, and contextual disambiguation.
Knowledge retrieval was performed using a dual-mode strategy that combined keyword and semantic-based approaches. Keyword retrieval utilized full-text indexing for precise matching of structured entities, while semantic retrieval employed vector representations to identify relevant unstructured knowledge. Retrieved results were merged, deduplicated, and structured for downstream processing. A multi-dimensional ranking strategy was applied to prioritize results based on exact matching, semantic similarity, and keyword relevance, with weighting adjusted for gynecological and DOR-related contexts.
The system generated diagnostic and treatment outputs using natural language generation techniques, converting structured knowledge into clinically interpretable responses.
3. Clinical application and evaluation
The developed system was applied in a clinical setting to evaluate its practical performance. Study participants were patients diagnosed with diminished ovarian reserve at a tertiary hospital in Nanjing who consented to receive acupuncture treatment.
Participants were eligible if they were aged 20 to 40 years and met diagnostic criteria for diminished ovarian reserve according to the 2016 POSEIDON criteria and the 2020 Chinese Expert Consensus. Diagnostic indicators included reduced anti-Müllerian hormone levels and antral follicle count &lt; 5. Patients were excluded if they had conditions affecting fertility, serious comorbidities, or had received acupuncture treatment for fertility within the previous three months. The patient inclusion and exclusion flowchart is shown in Figure 4.
<img alt="Flowchart: patient inclusion/exclusion; eligibility assessment; clinical study participant filtering." class="xfigimg" src="/files/ftp_upload/71182/71182fig4.jpg" data-altupdatedat="1784001604000" data-alt="Figure 4"> 
Figure 4: Patient inclusion and exclusion flowchart. Flowchart showing patient selection. A total of 127 patients met inclusion criteria, and 90 patients were included after applying exclusion criteria and accounting for incomplete data. <a href="https://www.jove.com/files/ftp_upload/71182/71182fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Patient data, including demographic and clinical information, were collected and recorded in a structured format. Participants completed standardized questionnaires to capture symptom and clinical information (the detailed questionnaire is provided in Supplementary File 5). The system generated diagnostic outputs and acupuncture prescriptions based on the input data.
System performance was evaluated using diagnostic accuracy and treatment appropriateness. Diagnostic accuracy was assessed by comparing system-generated syndrome differentiation results with those provided by three senior gynecological TCM physicians, who served as the reference standard. The diagnostic agreement rate was calculated as the proportion of consistent cases among all cases.
Treatment appropriateness was evaluated by a panel of three senior acupuncturists with at least five years of clinical experience and senior professional titles. Experts independently assessed system-generated prescriptions using a 5-point Likert scale. Inter-rater agreement was evaluated using Kendall’s coefficient of concordance (W).

The authors declare no competing interests.

The present study describes a knowledge graph-based intelligent decision-support system tailored for acupuncture treatment of diminished ovarian reserve, representing a transition from general-purpose traditional Chinese medicine knowledge modeling to a disease-specific clinical application.
Compared with conventional acupuncture practice, which relies heavily on practitioner experience, the proposed system introduces a structured and reproducible diagnostic framework. By integrating multi-sou...

Diminished ovarian reserve (DOR) is characterized by a decline in the quantity and/or quality of oocytes, leading to reduced fertility, menstrual irregularities, and hormonal imbalance1. The incidence of DOR has increased in recent years and is progressively affecting younger populations. Epidemiological data from the United States indicate an increase from 19% in 2004 to 26% in 20112, while a report from the Chinese Medical Association documented a prevalence of 13.58% among infertile patients undergoing assisted reproductive technology2,3.
Current management strategies in Western medicine include controlled ovarian hyperstimulation, adjuvant pharmacological treatments, and hormone replacement therapy. However, these approaches are associated with several limitations, including low oocyte retrieval rates, limited improvement in live birth outcomes, high treatment discontinuation rates, contraindications, and potential adverse effects4,5.
Acupuncture has been investigated as a complementary therapeutic approach for improving ovarian function in patients with DOR. Evidence from clinical studies and systematic reviews suggests potential benefits6,7,8. Nevertheless, acupuncture diagnosis and treatment remain highly dependent on practitioner experience, vary across different theoretical schools, and lack standardized integration with modern clinical data. These limitations restrict the broader implementation of acupuncture guidelines and increase the learning barrier for clinicians without formal training in traditional Chinese medicine.
Recent advances in artificial intelligence (AI) and knowledge graph (KG) technologies have enabled new approaches for structuring and utilizing complex medical knowledge in acupuncture and TCM9,10. Prior studies include visual analyses of acupuncture research trends11, acupoint recommendation models based on graph convolutional networks12, knowledge graph frameworks incorporating prompt learning for acupuncture and tuina13, and systematic reviews of TCM knowledge graph construction and applications14. Furthermore, the integration of knowledge graphs with large language models (LLMs) has emerged as a promising direction, as demonstrated by systems such as TCM-KLLaMA for prescription generation and case-based question-answering systems combining LLMs with knowledge graphs15,16. These studies highlight the potential of AI-driven approaches to support structured reasoning and decision-making in TCM. In addition, complex network methodologies have been applied to analyze acupuncture theoretical systems, providing methodological support for modeling structured medical knowledge17.
Our group has previously developed a general acupuncture knowledge graph for infertility, including the Transformer-based dual-channel ACUBERT model18 and a large-scale acupuncture knowledge base with multi-hop reasoning capability19. However, these systems were designed as general-purpose frameworks and lack disease-specific optimization for gynecological conditions. In addition, they have not been clinically validated in a DOR-focused application scenario.
In this context, the present study focuses on gynecological reproductive disorders, using DOR as the clinical entry point. Building upon the existing general acupuncture knowledge graph, a domain-specific knowledge graph tailored to DOR was constructed, and an intelligent clinical decision-support system was developed. The system was evaluated using real-world clinical cases to assess diagnostic consistency and prescription appropriateness, aiming to provide structured and reproducible support for acupuncture-based clinical decision-making.

<table><tbody><tr><td>BiLSTM-CRF</td><td>Open Source (https://github.com/jmhIcoding/bilstm-crf)</td><td>N/A</td><td>Deep learning model architecture employed for named entity recognition in clinical text extraction.</td></tr><tr><td>CNKI Database</td><td>China National Knowledge Infrastructure</td><td>N/A</td><td>Literature repository utilized for the retrieval of relevant Chinese-language academic publications.</td></tr><tr><td>JSON Viewer &amp;amp; Formatter</td><td>Megaish</td><td>1.6.0</td><td>Software tool used for JSON data structure viewing and validation.</td></tr><tr><td>KGs Annotation Platform</td><td>Nanjing KGI Data Technology Co., Ltd.</td><td>N/A</td><td>Web-based collaborative annotation platform utilized for the manual labeling of medical entity and relationship corpora to support knowledge graph construction.</td></tr><tr><td>Large Language Model API</td><td>Moonshot AI</td><td>N/A</td><td>Kimi LLM API employed for semantic understanding and automated knowledge extraction from biomedical literature; interface compatible with OpenAI API format.</td></tr><tr><td>MySQL</td><td>Oracle Corporation</td><td>8.0.42</td><td>Relational database management system used for the storage of structured clinical data.</td></tr><tr><td>Neo4j Browser</td><td>Neo4j,Inc.</td><td>5.26.10</td><td>Graph database interface used for the interactive visualization and exploratory querying of the gynecological knowledge graph.</td></tr><tr><td>Python</td><td>Python Software Foundation</td><td>3.11.4</td><td>Primary programming language utilized for automated literature crawling, LLM API invocation, multiprocessing coordination, data parsing, and knowledge graph construction.</td></tr><tr><td>Web of Science Core Collection</td><td>Clarivate Analytics</td><td>N/A</td><td>Literature repository utilized for the retrieval of relevant English-language academic publications.</td></tr></tbody></table>

a study on an intelligent diagnosis and treatment assistant system for acupuncture in diminished ovarian reserve based on a knowledge graph

A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph

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&#8217;s coefficient of concordance, was W &#8776; 0.13 (P &#60; 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.

The constructed knowledge graph integrates heterogeneous data from classical traditional Chinese medicine texts, modern clinical literature, and real-world case records, following established principles of knowledge graph construction and evaluation. A total of 374 Chinese-language sources, 267 English-language articles, and 82 classical TCM texts were incorporated, ensuring both historical depth and contemporary clinical relevance (Figure 5). 
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A gynecology-specific acupuncture knowledge graph and decision-support system for diminished ovarian reserve (DOR) was developed using literature, classical texts, and clinical data. Clinical evaluation demonstrated 86.7% diagnostic agreement and high prescription scores, with statistically significant but limited inter-expert agreement, supporting its role as an auxiliary clinical decision-support tool.

Diminished ovarian reserve (DOR) is increasing in incidence and affecting younger women. Acupuncture has been used clinically for DOR; however, practice ...

Our research focuses on developing an intelligent acupuncture system for diminished ovarian reserve diagnosis and treatment. This protocol can be applied to acupuncture research involving diminished ovarian reserve and other geologic reproductive disease. To begin, collect data from classical acupuncture texts, Chinese language literature from the China National Knowledge Infrastructure, or CNKI, clinical data from reproductive centers at tertiary hospitals, English language literature from the Web of Science core collection, and standardized knowledge bases.Perform data annotation and select data according to the inclusion and exclusion criteria. Include 400 clinical cases to support the identification of diagnostic and therapeutic patterns. Using a standardized annotation platform, manually annotate approximately 30%of the data, including classical texts and semantically complex content.With large language models, or LLMS, process 70%of the data for entity recognition, relationship extraction, and terminology normalization. Review the generated results. Using structured prompt templates designed to guide LLM, perform automated extraction.Once the model role is defined as a domain specific knowledge extraction expert, include a few shot learning example in JavaScript object notation or JSON triplet format to improve extraction performance. Using JSON validation tools, serialize validated knowledge triplets into independent JSON files named according to their source documents to maintain traceability. Store the finalized knowledge graph in a graph database using Python-based frameworks.Visualize the knowledge graph using built-in graph database tools to support inspection and validation. Develop the application system based on the constructed knowledge graph to enable clinical decision support and process user input data accordingly. Using the bidirectional long short-term memory conditional random field or bi-LSTM CRF model, implement natural language understanding for named entity recognition and entity linking.Build the model architecture with an embedding layer trained on domain-specific corpora, a bidirectional long short-term memory layer to capture contextual information and a conditional random field layer to ensure optimal sequence labeling. Train the model on manually annotated clinical and literature data with annotations performed independently by domain experts. Using Cohen's Kappa Coefficient, assess inter-annotator agreement with a threshold of at least 0.85 and resolve discrepancies through expert consensus.During inference, extract structured entities including symptoms, diseases, and syndrome patterns from clinical input data. Map these entities to standardized nodes in the knowledge graph. Using a dual mode strategy, perform knowledge retrieval, combining keyword-based and semantic-based approaches.Use vector representations to identify relevant unstructured knowledge. Merge keyword-based and semantic-based retrieved results. Identify and remove duplicates and structure results for downstream processing.Adjust weights for gynecological and diminished ovarian reserve related contexts. Using natural language generation techniques, generate diagnostic and treatment outputs and convert structured knowledge into clinically interpretable responses. After applying the developed system in a clinical setting, recruit participants diagnosed with diminished ovarian reserve according to the inclusion and exclusion criteria.Collect and record patient demographic and clinical information in a structured format. Generate diagnostic outputs and acupuncture prescriptions based on the input data. Once the system performance is evaluated using diagnostic accuracy and treatment appropriateness, compare system generated syndrome differentiation results with results provided by three senior gynecological traditional Chinese medicine physicians serving as the reference standard.Calculate the diagnostic agreement rate as the proportion of consistent cases among all cases. Evaluate treatment appropriateness using a panel of three senior acupuncturists with at least five years of clinical experience and senior professional titles and have experts independently assess system generated prescriptions using a five-point Likert scale. Evaluate interrater agreement using Kendall's coefficient of concordance.A total of 374 Chinese language sources, 267 English language articles, and 82 classical traditional Chinese medicine texts were incorporated into the knowledge graph. The full knowledge graph contained 16, 558 entities across seven predefined categories and 80, 084 relations spanning 21 relation types. Among 90 patients, age ranged from 22 to 54 years with a peak at 32 years and the highest concentration in the 31 to 35-year age group, self-rating anxiety scale assessment showed that 57%of patients had normal scores.28%had subclinical anxiety levels and smaller proportions had mild, moderate, or severe anxiety. Diagnostic agreement between system generated outputs and expert clinician assessments was 86.7%Treatment appropriateness had a mean score of 4.65 on a five point Likert scale. Interrater agreement among expert evaluators measured using Kendall's coefficient of concordance was approximately 0.13, suggesting a statistically significant level of agreement, although the overall concordance remained limited.This approach enables researchers to access the diagnostic agreement and the acupuncture prescription appropriateness. Researchers can additionally perform large language mode based reasoning analysis, multicenter validation, and the long-term eventual follow-up using this procedure. Future studies can build on this work by expanding applications to additional disease integrating multimodal data and conducting prospective studies.

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Learn how to develop an intelligent acupuncture diagnosis and treatment system for diminished ovarian reserve using knowledge graphs and BiLSTM-CRF models. This method achieved 86.7% diagnostic agreement with experts in clinical evaluation.

A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph (Video) | JoVE Journal

A Study on an Intelligent Diagnosis and Treatment Assistant System for Acupuncture in Diminished Ovarian Reserve Based on a Knowledge Graph

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