Investigating the Association Between Diagnostic and Therapeutic Management Models and Prognosis of Intrauterine Adhesions Based on a Large-Scale Prospective Cohort Study
Phase: NA
Conditions: Intrauterine Adhesion
Sponsor: The Third Xiangya Hospital of Central South University

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A large-scale, prospective cohort study on 
the association between treatment 
management models and the prognosis of 
intrauterine adhesions. 
 
 
 
Sponsor: Xiangya Third Hospital, Central South University 
 
Team Leader: Xiangya Third Hospital, Central South University 
 
 
Funding source: Self-raised funds 
 
Research Institution: Xiangya Third Hospital, Central South 
University 
 
Research Leader: Xu Dabao 
 
Version number: 2.0 
 

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Version Date: August 29, 2024 
 
 
 
List of research institution numbers 
Name of research institution 
serial number 
Xiangya Third Hospital of Central South 
University 
01 
Changsha Jiangwan Maternity Hospital 
02 
Changsha Maternal and Child Health 
Hospital 
03 
 
 
 
 
 
 
 
 
 
 
 
 

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Table of contents 
 
Research Proposal Summary  ....................................................................................... 4 
Research Text  ............................................................................................................... 6 
1. Research Background  .............................................................................................. 6 
2. Research Objectives  ............................................................................................... 11 
3. Research Design ..................................................................................................... 11 
4. Research Content  ................................................................................................... 13 
5. Key Evaluation Indicators ...................................................................................... 18 
6. Safety considerations  ............................................................................................. 19 
7. Statistical Analysis  ................................................................................................. 19 
8. Data integrity and quality assurance  ...................................................................... 20 
9. Ethical Statement  ................................................................................................... 20 
10. References  ............................................................................................................ 20 
 
 
 

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Research Proposal Summary 
Trial Title: A Large-Scale, Prospective Cohort Study on the Association Between 
Treatment Management Models and Prognostic Factors of Intrauterine Adhesions 
Research Objective: This project aims to explore the efficacy of the new IUA 
treatment model through a multi-center real-world study and demonstrate its high-
quality medical service model, laying the foundation for its future promotion. 
Experimental Design: Based on previous research, our team proposed a "new 
model for the whole-course management and treatment of IUA": preoperative four-
dimensional color ultrasound diagnosis and assessment, intraoperative "immediate 
diagnosis and treatment " cold knife plowing- style adhesion separation using an 
integrated hysteroscope, postoperative application of a patented intrauterine stent to 
prevent re-adhesion, postoperative adjuvant therapy with renowned doctors and 
prescriptions to promote endometrial repair, postoperative four-dimensional color 
ultrasound assessment and follow-up to replace multiple hysteroscopy 
examinations, and combined with intraoperative hysteroscopic AI counting of 
endometrial gland openings and gland density, multimodal data fusion to predict 
and assess pregnancy prognosis after IUA treatment. 
Experimental Procedure: Prospective, Observational Real- Event Study 
Selection criteria: 
Patients with intrauterine adhesions diagnosed and treated at our hospital and the 
branch centers of this project: aged 18-45 years; collection began after ethical review 
was approved, with a total of 5,000 cases collected, of which 2,000 cases were 
collected by our center. 
Exclusion criteria: 
Patients with abnormal ovarian function, male infertility, and inability to cooperate 
with diagnosis, treatment, and follow-up. 
 
Statistical methods: A super-resolution IUA ultrasound image enhancement 
algorithm based on transfer learning was used. Lesions in the reproductive terminal 
chain (ovary, follicle, uterus, endometrium, myometrium- mesenchymal junction, 
and fallopian tubes) were annotated on the enhanced 3D ultrasound images. Based 
on the enhanced 3D ultrasound image features of the IUA reproductive terminal chain 
from the second part of the study, and the multimodal large dataset from the first part 

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of the study, multimodal data feature extraction and fusion were achieved using deep 
learning and natural language processing techniques to realize automatic detection of 
IUA clinical subtyping, thereby constructing an AI-based IUA classification and 
grading algorithm. 
 
 
 

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Research Text 
1. Research Background 
1.1 Preoperative diagnosis of intrauterine adhesions 
There is currently a consensus on the diagnosis and assessment of intrauterine 
adhesions. The gold standard is hysteroscopy [4] . The existing IUA severity scoring 
standards are all based on hysteroscopic findings. Commonly used scoring standards 
include the American Fertility Society (AFS) (1988) score [5] . The European Society of 
Gynecological Endoscopy (ESGE) (1998) score [6] , the March (1978) score [7] and the 
Chinese expert consensus on the clinical diagnosis and treatment of intrauterine 
adhesions published in 2015 [ 8] . However, hysteroscopy is an invasive examination 
that depends on the surgeon's skill and subjective impression [6] . When the cervical 
canal, the lower segment of the uterine cavity, or even the uterine cavity is completely 
closed, hysteroscopy may fail, and the condition of the uterine cavity cannot be fully 
assessed. It may even cause uterine perforation. In IUA patients with reproductive tract 
malformations such as unicornuate uterus , it is difficult to determine the correct shape 
of the uterine cavity under hysteroscopy. Hysteroscopic distension fluid may cause 
water intoxication. Furthermore, the above limitations, such as the need for anesthesia 
and high cost, limit its use as a routine screening for intrauterine adhesions. Currently, 
a large number of studies have demonstrated that four-dimensional ultrasound has a 
high accuracy in diagnosing intrauterine adhesions. Correlation analysis with 
hysteroscopy results shows that four-dimensional ultrasound diagnosis of intrauterine 
adhesions is consistent with hysteroscopy results [6] . Studies have also confirmed that 
multiple ultrasound parameters can accurately screen for intrauterine adhesions [7] . 
Menstrual status, number of intrauterine procedures, uterine cavity morphology, 
endometrial continuity, endometrial homogeneity, endometrial-myolipin boundary, 
endometrial blood flow, peristaltic waves, etc. can all be used as evaluation criteria for 
diagnosing intrauterine adhesions [7-8] . 
1.2 Surgical treatment of intrauterine adhesions 
Since the advent of hysteroscopy, the surgical approach for IUA has gradually 

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shifted from traditional open surgery, blind curettage, and cervical dilation to... TCRA 
has become the preferred treatment for IUA . At present, there is still controversy in the 
clinical practice regarding whether to use energy devices in TCRA. Scholars who 
support the application of energy devices in TCRA believe that its advantages are 
simple operation, short operation time, and obvious effects on tissue cutting and 
hemostasis of bleeding points during operation. However, when energy devices cut the 
surgical area, the heat released by them can damage the surrounding normal 
endometrial tissue [ 24] . Non-energy devices mainly use various types of micro forceps 
and micro scissors to separate and loosen the adhesions in the uterine cavity through 
blunt and sharp separation, so as to restore the shape of the uterine cavity. This method 
does not release heat and there is no possibility of thermal damage to the remaining 
endometrium in the uterine cavity. In order to try to improve the efficacy of traditional 
cold knife method for TCRA, cold knife plowing- style intrauterine adhesion separation 
was proposed and gradually began to be used in clinical practice [ 23] . The cold-blade 
plowing technique , similar to plowing a field, longitudinally severs adhesions and scar 
tissue within the uterine cavity, exposing the underlying myometrium and any 
remaining endometrial tissue. Under postoperative medication stimulation, the 
endometrium has room to grow, and the blood supply from the myometrium can extend 
upwards to the endometrial layer, improving endometrial blood flow. This is consistent 
with the approach used by Sun Kun et al. [32] This aligns with the view that endometrial 
volume is related to blood flow parameters. Furthermore, the longitudinal plowing 
method reduces surgical trauma, which may decrease the likelihood of bleeding and 
TURP syndrome. Relieving uterine contractions after scar plowing can increase uterine 
cavity volume, creating more space for endometrial growth . 
1.3 Research on Anti-Adhesion Barriers 
Since intrauterine adhesions may cause obstruction of menstrual flow, leading 
to abdominal pain, amenorrhea, and decreased fertility in women of childbearing age, 
surgical treatment is usually required for patients with intrauterine adhesions who have 
obstructed menstrual flow and desire to have children. However, after surgery, the 
wounds separated in the uterine cavity are exposed, and they may still adhere again 

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after contact, leading to recurrence of adhesions. Therefore, some barriers are needed 
after surgery to separate the wounds and prevent recurrence of adhesions 
错误!未找到引用源。, 
错误!未找到引用源。. Over the years, scholars have conducted studies on intrauterine anti-
adhesion barriers for intrauterine adhesions, either individually or in different 
combinations. The most commonly used are intrauterine devices (IUDs), hyaluronic 
acid gel, Foley catheters, intrauterine balloons, and amniotic membrane transplantation 
错误!未找到引用源。, 错误!未找到引用源。. Although efforts have been made to prevent recurrence after 
surgery , most current research results show that the adjuvant treatment after 
intrauterine adhesion separation is not effective . In view of this, a new medical silicone 
rubber material was processed into a uterine cavity-shaped stent , which is specifically 
used for the postoperative adjuvant treatment of intrauterine adhesions. After nearly 10 
years of effort, we have successfully designed and developed a new type of silicone 
device (called an intrauterine stent) that can theoretically prevent the recurrence of 
intrauterine adhesions through the physical barrier effect, and applied for patent 
protection 
错误!未找到引用源。- 错误!未找到引用源。. It is made of medical silicone rubber, which is 
non-irritating to human tissues, has no toxic side effects, is non-carcinogenic, has good 
biocompatibility, is corrosion resistant, will not swell, soften, or deteriorate due to water 
absorption, is easy to process and manufacture, is convenient to use, and has good 
thermal stability. The uterine support mainly consists of a reinforcing ring, a membrane, 
a tail-shaped structure, and a placement device. The placement device comprises a 
placement cannula, a positioning block, and a push rod. It is made of medical-grade 
silicone rubber. The reinforcing ring is elastic, and its shape and size conform to the 
periphery of the uterine wall. Three drainage grooves are located at the lower end of 
the reinforcing ring, which facilitates the drainage of intrauterine fluid and reduces the 
chance of intrauterine infection due to poor drainage. The membrane completely 
separates the anterior and posterior walls of the uterine cavity, physically preventing 
adhesions between them. Furthermore, because the membrane is very thin, it exerts 
almost no pressure on the endometrium it contacts. The tail-shaped structure is 
primarily responsible for preventing adhesion recurrence. This longitudinal, solid 
structure connects to the midpoint of the lower segment of the ring and communicates 

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with the uterine cavity. Its length can be trimmed according to the patient's specific 
situation, effectively preventing adhesions in the lower segment of the uterine cavity 
and the internal cervical os, while maximizing material savings, reducing 
manufacturing costs, and providing better drainage of intrauterine fluid. 
1.4 Famous doctors and prescriptions as adjunctive treatment for intrauterine 
adhesions 
Endometrial damage is the main cause of uterine secondary infertility, and 
how to repair the damaged endometrium has always been a research bottleneck. 
Intrauterine adhesions can lead to sparse endometrium, low cell proliferation, 
insufficient response to estrogen, and inability to fully proliferate and repair [26] . An 
immunohistochemical study to detect the expression of estrogen receptors in the 
endometrium found that the expression of estrogen receptors in the proliferative and 
secretory phase thin endometrial stromal cells was significantly lower than that in 
normal endometrium [27-29] . Therefore, high doses of estrogen have limited effect on 
promoting endometrial growth [30] . From the perspective of traditional Chinese 
medicine, the proliferation of endometrium in intrauterine adhesions is closely related 
to the strength and weakness of kidney essence and kidney qi. Modern research has 
confirmed that kidney-tonifying Chinese medicine can significantly increase uterine 
blood supply by promoting blood circulation in the internal reproductive organs, 
thereby improving the receptivity of the endometrium, increasing the affinity of target 
tissue estrogen receptors, promoting endometrial proliferation, and improving secretory 
function [31] . In addition, kidney-tonifying Chinese medicine increases the weight of 
the uterus in experimental animals, significantly thickens the endometrium, increases 
the number of endometrial gland openings, shows a secretory tendency, and increases 
the content of estrogen and progesterone receptors in the endometrium. Kidney-
tonifying Chinese medicine has an endocrine hormone-like effect, which can improve 
the low estrogen environment in the uterine cavity [32,33] . Professor You Zhaoling 
believes that kidney deficiency and blood stasis are the main causes, and uses kidney-
tonifying and blood-activating Chinese medicine to nourish the endometrium, thereby 
improving the overall environment of the endometrium [57] . Modern medical research 

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shows that the application of Chinese medicine to tonify the kidney can promote blood 
circulation in the internal reproductive organs, increase blood supply to the uterus, 
significantly improve endometrial receptivity, and increase the affinity of target tissue 
estrogen receptors, causing endometrial proliferation and transformation to the 
secretory phase [60] . 
1.5 Post-IUA infertility seriously affects my country's fertility rate 
A study published in *The Lancet* indicates a continued decline in global fertility rates, 
with an average infertility rate of 10%-16%, reaching as high as 18% in China. In China, 
intrauterine adhesions (IUA) account for 22.0% of female infertility cases, making it a 
"characteristic disease" of the country. IUA involves endometrial damage and fibrosis. 
Even after adhesion separation surgery, endometrial repair remains poor, and re-
adhesion is common . The lack of early detection and prevention leads to an infertility 
rate as high as 40%-60% (including infertility and miscarriage treated with Western 
medicine). Given the national commitment to improving fertility, preventing infertility 
after IUA surgery is of great significance. Therefore, it is urgent to develop accurate 
risk prediction models to identify high-risk groups and provide prevention and control 
guidance. Our team has achieved near-adhesion-free uterine cavity after the last IUA 
surgery through surgical technique innovation and the clinical application of a patented 
intrauterine stent . However, postoperative estrogen therapy to promote endometrial 
recovery is ineffective, resulting in poor endometrial receptivity and a high rate of re- 
adhesion, leading to a high infertility rate. Currently, scoring criteria for predicting 
pregnancy outcomes after intrauterine adhesions (IUA) are based on indicators such as 
the morphology, extent, and type of adhesions observed under hysteroscopy, resulting 
in poor predictive efficacy. Furthermore, there is a lack of research on predicting post-
IUA infertility risk using Traditional Chinese Medicine (TCM). Professor You Zhaoling, 
a nationally renowned TCM practitioner, guided a research group to use transvaginal 
three -dimensional ultrasound to analyze endometrial receptivity after IUA and 
developed a TCM-based treatment plan for IUA and a post-operative TCM 
management system, which can effectively improve the success rate of conceiving after 
IUA and provide insights for the prediction and prevention of post-IUA infertility. 

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However, the characteristics of post-IUA three-dimensional ultrasound images are 
highly variable and easily affected by machine noise and physician diagnostic 
experience; how to accurately extract image features is a bottleneck in its application. 
The information from the four diagnostic methods of TCM is complex, and how to 
simplify complex information is a challenge for its use in prediction. The integration 
and enhancement of multidimensional TCM and Western medicine risk factors is a key 
challenge in establishing a post- IUA infertility risk prediction model. Machine learning 
technology holds promise for solving these bottlenecks. 
 
2. Research Objectives 
2.1 To reveal the intrinsic relationship between three-dimensional ultrasound 
image characteristics and diagnosis and prognosis in the IUA population; 
2.2 Multicenter clinical studies have confirmed the clinical efficacy of the cold 
knife plowing technique and uterine stent in preventing adhesions; 
2.3 Standardize treatment plans for renowned doctors and prescriptions to promote 
endometrial growth and maintain pregnancy and assist in conception, and confirm their 
clinical efficacy; 
2.4 Construct and validate a multidimensional infertility risk factor prediction 
model based on integrated traditional Chinese and Western medicine after IUA, and 
visualize it for easy application; 
 
3. Research Design 
3.1 Establish four-dimensional ultrasound diagnostic evaluation standards 
A super-resolution model for enhancing 3D ultrasound images was trained using 
transfer learning across multiple tasks, and its parameters were fine-tuned to obtain a 
super-resolution model capable of enhancing IUA 3D ultrasound image features. 
Lesions in the reproductive terminal chain (ovary, follicle, uterus, endometrium, 
myometrium-mesenchymal junction, and fallopian tubes) were annotated on the 
enhanced 3D ultrasound images , resulting in a large-scale, precisely annotated IUA 3D 
ultrasound image dataset. Using this dataset and object detection algorithms in artificial 

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intelligence, automatic and accurate detection of lesions in the reproductive terminal 
chain can be achieved, thus enabling accurate diagnosis of IUA 3D ultrasound. 
Furthermore, a scoring standard was constructed based on the characteristic indicators 
of endometrial receptivity assessed by 4D ultrasound. 
3.2 Standardize IUA diagnosis and treatment data and build a large database and 
data queue 
To strengthen the standardization and normalization of integrated traditional 
Chinese and Western medicine (TCM) diagnosis and treatment, and addressing the 
current lack of unified standards for IUA (Intrauterine Adnexation) medical data , this 
project pioneered the development of the "Expert Consensus on Integrated TCM and 
Western Medicine Diagnosis and Treatment of IUA." This consensus standardizes 
preoperative diagnosis, preoperative TCM conditioning, surgical procedures, 
postoperative TCM treatment, and pregnancy guidance for IUA, and has been 
disseminated and promoted nationwide. A national-level big data platform has been 
established, relying on TCM clinical research bases and national regional obstetrics and 
gynecology medical centers. The database is constructed in two parts: based on 
established multi-dimensional data standards , previous IUA data (including images) is 
collected and cleaned, and clinical information data extraction and storage methods are 
improved to establish a systematic, structured, and standardized integrated TCM and 
Western medicine IUA big data database across multiple centers ; simultaneously, 
regional medical centers connect the information systems of hospitals within the 
network region to the medical center through the data platform, providing real-time 
monitoring and follow-up , and contributing to the establishment of an IUA cohort 
dataset. 
3.3 Evaluate the clinical efficacy of the new integrated traditional Chinese and 
Western medicine treatment model for IUA through real-world research. 
This project conducts prospective and retrospective studies by systematically 
collecting real-world data and utilizing practical clinical trials. Based on a large 
database of integrated traditional Chinese and Western medicine treatments for IUA 
(Intrauterine Adhesion) patients, it compares the clinical efficacy of the new treatment 

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model with the traditional model. The "New Model for IUA Whole-Process 
Management and Treatment" includes: preoperative 4D ultrasound diagnosis and 
assessment; intraoperative "immediate diagnosis and treatment " cold-knife plowing- 
style adhesion separation using an integrated hysteroscope; postoperative application 
of a patented uterine stent to prevent re-adhesion; postoperative adjuvant therapy with 
renowned doctors and prescriptions to promote endometrial repair; postoperative 
follow-up using 4D ultrasound assessment instead of multiple hysteroscopy 
examinations; and combining intraoperative hysteroscopic AI counting of endometrial 
gland openings and gland density, multimodal data fusion to predict and assess 
pregnancy prognosis after IUA treatment. 
3.4 Constructing a Predictive Model for Post-IUA Infertility Risk Based on 
Machine Learning Integration and Enhancement of Multidimensional Risk Factors 
from Traditional Chinese and Western Medicine 
Potential risk factors were extracted from the post-IUA cohort in the second part 
of the study and standardized. A predictive factor system was constructed by combining 
four-dimensional image feature variables and TCM syndrome element variables, and 
pregnancy outcomes were analyzed after two years of follow-up following IUA . Two 
machine learning algorithms, Logistic Regression and XGBoost , were used to establish 
the predictive model . The Logistic Regression model used stepwise regression to select 
variables, and the final predictive factors were determined by comprehensively 
considering the minimum Akaike information criterion, Bayesian information criterion, 
and clinical significance. The XGBoost model used the Boruta algorithm to select the 
final predictive factors. After model establishment, internal and external validations 
were performed. The predictive performance of the models was compared and 
evaluated in terms of discrimination and calibration. Finally, the models were visualized 
and applied using nomograms, an app, or a webpage. 
 
4. Research Content 
4.1 Collect IUA diagnosis and treatment data and build a large database 
and data queue 

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Under the leadership of Professors Xu Dabao and You Zhaoling , a consensus 
among experts in both traditional Chinese medicine and Western medicine with 
extensive experience in the diagnosis and treatment of IUA ( Intrauterine Adnexation) 
was jointly developed , outlining the "Expert Consensus on the Integrated Traditional 
Chinese and Western Medicine Treatment of IUA." This consensus standardizes IUA 
diagnostic methods and criteria, surgical procedures , traditional Chinese medicine 
treatment plans, and pregnancy guidance protocols , and is being promoted through 
clinical training demonstration bases and academic conferences . Relying on the 
platform of the Traditional Chinese Medicine Clinical Research Base and the National 
Regional Medical Center for Obstetrics and Gynecology , a big data platform for 
integrated traditional Chinese and Western medicine diagnosis and treatment has been 
established. This platform collects IUA diagnosis and treatment data (including imaging 
data) from IUA-specific alliance units, with Xiangya Third Hospital of Central South 
University as the chair unit . The data is then recorded and stored in a standardized and 
structured manner , serving as modeling indicators for the integrated traditional Chinese 
and Western medicine diagnosis and treatment decision- making system for IUA . By 
collecting image data from examinations, textual data from Western medicine surgeries 
and TCM treatment cases, and numerical data from laboratory tests in IUA's integrated 
traditional Chinese and Western medicine treatments through a big data platform , we 
first retrospectively collected treatment data from each alliance unit from 2017 to 2022, 
performed structured storage and cleaning, and completed follow-up to establish a 
training dataset. Then, we continuously completed high-quality prospective data 
collection, established the IUA Integrated Traditional Chinese and Western Medicine 
Treatment Center's official WeChat account, and enabled patient follow-up, real-time 
storage, export and analysis , real-time efficacy assessment and fertility measurement 
and evaluation, and established a test dataset for the decision-making model. We 
continuously expand the sample size to collect treatment data more comprehensively. 
The big data platform of the Traditional Chinese Medicine Clinical Research 
Base and the National Regional Medical Center for Obstetrics and Gynecology 
encompasses health data from over 50 gynecological specialty alliance units nationwide, 

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including Xiangya Third Hospital of Central South University, the First Affiliated 
Hospital of Hunan University of Traditional Chinese Medicine, Xiangya Hospital of 
Central South University, Xiangya Second Hospital of Central South University, Hunan 
Provincial Maternal and Child Health Hospital, and Hunan Provincial People's Hospital. 
Based on the existing IUA case database, IUA patients from the past two years were 
retrospectively screened. Most of these patients underwent transvaginal 3D ultrasound 
follow-up on days 20-24 of their menstrual cycle one month after surgery. More than 
5,000 IUA patients with relatively complete information were identified, sufficient for 
subsequent machine learning modeling needs. Furthermore, patients within the past two 
years could clearly recall their disease, making the retrospective investigation and 
supplementary data reliable. Data was collected on individual patient information, 
family history, lifestyle, past medical history, marital and reproductive history, clinical 
symptoms, clinical diagnosis, laboratory test results, treatment methods, postoperative 
3D ultrasound images, male partner fertility-related information, and TCM syndromes. 
(This project involves the collection and analysis of clinical medical records of IUA 
patients, which has been approved by the Ethics Committee of the chair unit, 
Xiangya Third Hospital of Central South University, and filed with the alliance 
units .) Data underwent standardization, cleaning, and outlier/ missing value handling . 
Simultaneously, unified follow-up standards and evaluation mechanisms were 
established to strengthen follow-up examinations and monitoring of the cohort 
population, forming a comprehensive monitoring and follow-up system. Follow-up 
efforts were intensified, with reproductive follow-ups conducted every 3 months, 
utilizing multiple channels such as WeChat, telephone, and Douyin (TikTok) to improve 
timeliness. Pregnancy outcomes were followed up two years after the start of 
conception . Multi-center clinical diagnosis and prognostic information collection was 
completed, constructing a multi-center IUA cohort for Chinese women. 
4.2 Evaluate the clinical efficacy of the new integrated traditional Chinese and 
Western medicine treatment model for IUA through real-world research. 
Real-world research involves systematically collecting real-world data and 
employing appropriate design and analytical methods to conduct prospective or 

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retrospective studies. This project, based on the IUA's integrated traditional Chinese 
and Western medicine treatment database, compares the clinical efficacy of new and 
traditional treatment models through practical clinical trials (PCTs) . PCTs, also known 
as practical clinical trials or efficacy clinical trials, are clinical trials that closely 
resemble real-world clinical practice. They are a type of research between traditional 
randomized controlled trials (RCTs) and observational studies, and fall under the 
category of interventional research. 
IUA ( Intrauterine Adhesion) treatment model utilizes the CTM platform , 
where nine teams sequentially manage six key stages: pre - TCRA ( Transcervical 
Resection of Adhesion ) procedures, TCRA procedures, post- TCRA procedures, 
pre - pregnancy period , pregnancy , and perinatal period, achieving integrated 
traditional Chinese and Western medicine management of intrauterine adhesions 
throughout the entire course of the disease. Pre-TCRA : For examination, the 
ultrasound imaging department uses the American GE-E10 4D color Doppler 
ultrasound to assess the uterus, ensuring clear and intuitive visualization while 
minimizing intrauterine manipulation and damage. For treatment, the gynecology 
department, based on a detailed assessment and prediction of the disease and 
complications, decides on the treatment / surgical plan. For conditioning, pre-
operatively, patients take Professor You Zhaoling's exclusive traditional Chinese 
medicine formula " Gongzhan No. 1 " to promote blood circulation, remove blood stasis, 
and condition the endometrium, creating favorable conditions for intrauterine adhesion 
separation. TCRA Procedure : The first surgery for intrauterine adhesions is 
particularly important. Because during the first surgery, the patient's uterine anatomy is 
natural; if the surgical anatomical layers are incorrect, even by only a few millimeters, 
it is very difficult to correct in later surgeries. Professor Xu Dabao, the project leader, 
performed a " cold knife " precision adhesiolysis procedure to separate intrauterine 
adhesions, avoiding damage to the endometrium while separating them. Post - 
operatively, a patented uterine stent was placed in the uterine cavity to reduce the 
incidence of re-adhesion, achieving both treatment and protection. Post- TCRA 
surgery : During the golden recovery period, a combination of traditional Chinese and 

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Western medicine treatments continued. The gynecology department provided precise 
medication based on the cause and individual differences; the traditional Chinese 
medicine department used the You family's secret formula " Gongzhan No. 1 " to 
promote endometrial repair, reduce scar fibrosis, lower the re-adhesion rate , and 
provide sufficient nutrition for uterine repair. During the pre-conception period: The 
genetics and laboratory departments conducted necessary medical genetic testing and 
prenatal diagnosis to improve pregnancy and birth rates, ensuring the embryo " grows 
well " after conception ; the andrology department screened for male diseases that might 
be caused by embryonic factors leading to miscarriage or infertility, addressing 
conditions such as asthenospermia, teratospermia , oligospermia, and delayed or non-
liquefied semen; the infertility department provided precise intervention and scientific 
guidance for infertility, helping patients achieve pregnancy as soon as possible . 
Pregnancy and perinatal period: Integrating personalized treatment based on 
syndrome differentiation, we conduct scientific and precise full-course management of 
pregnancy maintenance . The team proposes a differentiated and step-by-step approach, 
followed by a two-step approach to pregnancy maintenance, which effectively 
improves embryo implantation rate and clinical pregnancy success rate. Through 
meticulous and comprehensive diagnosis and treatment, we address the underlying 
causes of maternal problems, regulate maternal hormone levels, improve maternal 
physical condition, and help the fetus develop healthily. 
4.3 Based on machine learning, a multidimensional risk factor prediction model 
for post-IUA infertility was constructed and validated by integrating traditional 
Chinese and Western medicine. 
By reviewing domestic and international literature on risk factors for infertility 
after intrauterine device (IUA) surgery, and combining the characteristics of traditional 
Chinese medicine (TCM) and Western medicine clinical risk factors for IUA infertility 
in Chinese patients, this study, based on the IUA integrated TCM and Western medicine 
cohort data constructed in Part I, standardized the data and established a multi-
dimensional candidate variable dataset that may be related to IUA infertility. This 
dataset included multi-dimensional TCM and Western medicine diagnostic and 

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treatment data such as age, lifestyle, family history, laboratory tests, surgical details, 
male partner's semen and sexual activity, 3D ultrasound image feature combinations, 
and TCM syndrome combination variables. Follow-up results for the corresponding 
population, i.e., pregnancy outcomes two years after surgery, were also collected . This 
study used two machine learning methods to construct IUA infertility risk prediction 
models: traditional logistic regression and XGBoost . The logistic regression model 
used stepwise regression to screen variables, combining the least Akaike information 
criterion, Bayesian information criterion, and clinical significance to determine the final 
predictive factors included in the model. After determining the predictive factors, a 
logistic regression equation was used to establish a predictive model to predict the 
probability of IUA infertility. The effect of the predictive factors was reflected by the 
regression coefficient or OR value and its 95% CI. After model establishment, 
multicollinearity tests were performed. Multicollinearity is assessed using the 
correlation coefficient method/variance inflation variable. Strong collinearity among 
variables can severely impact the model's generalization ability. XGBoost , short for 
"Extreme Gradient Ascent," is an ensemble machine learning algorithm based on 
decision trees and using a gradient boosting framework. It employs the Boruta 
algorithm to select the final predictive factors for inclusion in the model. The 
importance of predictive factors is reflected by the SHAP (Shapley Additive 
Explanation) value; a higher SHAP value indicates a more important predictive factor. 
The resulting model combines a Logistic Regression model and an XGBoost model to 
predict infertility risk after IUA (Intrauterine Access) surgery. 
5. Key Evaluation Indicators  
This study employed both internal and external validation methods for model 
evaluation. Internal validation used a validation set derived from the same source as the 
training set to assess model reproducibility. This study used a random split validation 
approach, randomly dividing the dataset into training and validation sets in a 7:3 ratio. 
The training set data was used to build the model, and the validation set data was used 
to evaluate model performance. External validation used a prospective dataset collected 
based on the IUA data platform. The predictive performance of both the Logistic 
Regression and XGBoost models for predicting post-IUA infertility risk was compared 

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and evaluated in terms of discriminability and calibration. The Area Under the Receiver 
Operating Characteristic (AUC) was used for evaluation; a higher AUC value indicates 
higher discriminability. The DeLong 's test was used to compare the AUC values of the 
two models to determine statistical significance. The Brier score was used to plot the 
calibration curve, and the Hosmer- Lemeshow goodness-of-fit test was used to assess 
model calibration and plot the calibration curve. Finally, to promote the application of 
predictive models, the Logistic regression model for predicting infertility risk after IUA 
is visualized using a nomogram, and the Youden index is calculated using the ROC 
curve method. The optimal cutoff value for the total score of the nomogram is then 
derived based on the Youden index. The XGBoost model for predicting infertility risk 
after IUA is presented in an app/webpage format. Two different predictive models are 
provided for selection in both traditional Chinese medicine and Western medicine 
clinical practice. 
 
6. Safety considerations 
Surgical risks, anesthesia risks, etc. 
 
7. Statistical Analysis Plan (SAP) 
A super-resolution IUA ultrasound image enhancement algorithm based on transfer 
learning was developed. Lesions in the reproductive terminal chain (ovary, follicle, 
uterus, endometrium, myometrium- mesenchymal junction, and fallopian tubes) were 
annotated on the enhanced 3D ultrasound images. Based on the enhanced 3D ultrasound 
image features of the IUA reproductive terminal chain from the second part of the study, 
and the multimodal large dataset from the first part of the study, deep learning and 
natural language processing technologies were combined to achieve multimodal data 
feature extraction and fusion, enabling automatic detection of IUA clinical subtyping, 
thus constructing an AI-based IUA classification and grading algorithm. Two machine 
learning methods were used to construct a predictive model for post-IUA infertility risk: 
traditional logistic regression and XGBoost . The logistic regression model used 
stepwise regression to screen variables, combining the minimum Akaike information 
criterion, Bayesian information criterion, and clinical significance to determine the final 

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predictive factors included in the model. After determining the predictive factors, a 
predictive model was established using the logistic regression equation to predict the 
probability of post-IUA infertility. The effect of the predictive factors was reflected by 
the regression coefficient or OR value and its 95% CI. After model establishment, 
multicollinearity tests were performed. Multicollinearity is assessed using the 
correlation coefficient method/variance inflation variable. Strong collinearity among 
variables can severely impact the model's generalization ability. XGBoost , short for 
"Extreme Gradient Ascent," is an ensemble machine learning algorithm based on 
decision trees and using a gradient boosting framework. It employs the Boruta 
algorithm to select the final predictive factors for inclusion in the model. The 
importance of predictive factors is reflected by the SHAP (Shapley Additive 
Explanation) value; a higher SHAP value indicates a more important predictive factor. 
The resulting model combines a Logistic Regression model and an XGBoost model to 
predict infertility risk after IUA (Intrauterine Access) surgery. 
 
8. Data integrity and quality assurance 
Researchers should ensure that data is authentic, accurate, complete, and traceable, 
and should ensure the integrity of basic clinical research documents during retention, 
avoiding intentional or unintentional alteration or loss. 
 
 
9. Ethical Statement 
The clinical research will adhere to the relevant regulations of the World Medical 
Association's Declaration of Helsinki and the Ethical Review Guidelines for 
Biomedical Research Involving Human Subjects. The research protocol will be 
approved by the ethics committee before the clinical research can commence. The 
privacy of research participants and the confidentiality of data will be protected 
throughout the research process. 
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