Artificial Intelligence-assisted Chest X-ray in TB Screening: Effectiveness of Enhancing the Care Cascades in Chinese Primary-care Settings (ACCESS-CARE)
Phase: NA
Conditions: Tuberculosis (TB)
Sponsor: Xuelin Yang

 
 
2 Arrow Street, Cambridge, MA 02138 (T) 617-979-8000 (F) 617-576-0973 www.chinamedicalboard.org 
October 28, 2024 
 
Mr. WU Peixin 
Chairman 
Peking Union Medical College Education Foundation 
9 Dong Dan San Tiao 
Dongcheng District 
Beijing 100730 
P. R. China 
 
 
Re: Grant Notification Letter: Grant #24-564 
 
Dear Chairman Wu, 
 
I am pleased to inform you that the China Medical Board (CMB) has approved a grant of 
US$100,000 to Peking Union Medical College Education Foundation (“you”) for supporting a 
research project on artificial intelligence-assisted chest X-ray in TB screening. This grant is 
being made in response to the proposal “Artificial intelligence-assisted chest X-ray in TB 
screening: effectiveness of enhancing the care cascades in Chinese primary-care settings 
(ACCESS-CARE)” (the “Proposal”) submitted by Dr. WANG Ye at School of Population 
Medicine and Public Health, China Academy of Medical Sciences & Peking Union Medical 
College. 
 
The following terms apply to the grant: 
 
a) Grant funds will be available for expenditure during a three-year period beginning on 
January 1, 2025. 
 
b) Grant funds will be paid in lump sum. The payment will be made by CMB Beijing 
Representative Office in the form of Chinese Yuan (applying the currency exchange rate 
of the transaction time). The payment will be made upon receipt of the countersigned 
copy of this letter and your payment instructions. Please complete the payment 
instructions located in Attachment III. 
 
c) Grant funds and any income therefrom shall be used only for the purposes outlined in this 
letter and as described in the proposal. All expenditures must adhere to the relevant 
accounting and grant management policies and regulations of your institution. Prior 
written approval from CMB is required for any extension of the grant duration. Grant 
funds must be maintained in a separate fund dedicated to the purposes stated in this letter 
and described in the proposal. 
 
d) Within ninety [90] days after the close of the first year of the project and any subsequent 
year in which you have any unexpended grant funds remaining, you will provide CMB 
with a narrative and financial report, signed by an authorized officer, describing the 
manner in which grant funds were used, your compliance with the terms of this grant 
agreement, and the progress made in accomplishing the grant purpose. In addition, within 

 
 
2 Arrow Street, Cambridge, MA 02138 (T) 617-979-8000 (F) 617-576-0973 www.chinamedicalboard.org 
ninety [90] days after the completion of the project, you will submit a final narrative and 
financial report to CMB with respect to all expenditures under the grant and indicating 
your progress made toward accomplishing the grant purpose. Please refer to Attachment 
Il for specific reporting schedule. The reports should include a financial accounting of 
actual expenditures against the line items in the approved budget. Budget modification or 
time extensions of the grant terms may be requested at the time of annual reports. 
 
e) Under United States law, CMB grant funds, and any income therefrom, may be expended 
only for charitable, scientific, literary, or educational purposes. You agree not to use the 
grant funds, or any income therefrom (i) for any purpose other than charitable purposes 
consistent with the provisions of Section 170(c)(2)(B) of the Internal Revenue Code of 
1986, as amended (the “Code”); (ii) to carry on propaganda, or otherwise to attempt to 
influence legislation within the meaning of Section 4945(d)(1) of the Code; (iii) to 
influence the outcome of any specific public election, or to carry on, directly or 
indirectly, any voter registration drive within the meaning of Section 4945(d)(2) of the 
Code; or (iv) to make a grant to any individual for travel, study, or other similar 
purposes, or to make a grant to any organization, except in compliance with the 
provisions of Sections 4945(g) or (h) of the Code, as the case may be. 
 
f) 
You confirm your obligation to maintain separate records regarding the receipt and 
expenditure of the grant and to make its books and records available to CMB at 
reasonable times if requested. You will maintain these books and records for at least four 
years after expending the grant funds.  
 
g) Upon the expiration of this agreement, you will return to CMB any part of the grant that 
is not expended or committed for the grant purpose. If any part of the grant is not 
expended or committed for the grant purpose, you will notify CMB in writing, 
whereupon CMB will have the sole right to approve carry-forward of funds or a no cost 
time extension. Either party may terminate this agreement upon thirty [30] days written 
notice to the other party. In the event of early termination, you agree to return any funds 
not committed or expended in accordance with THE PURPOSE OF THE GRANT and 
provide an accounting as to the use of expended or committed funds up to the date of 
termination.  
 
h) Any publicity derived from this grant, including publications, presentations, and printed 
materials, should acknowledge CMB’s financial support of this project. 
 
i) 
CMB encourages all investigators to share data whenever feasible. If the original data 
have been collected by the investigator using CMB funding, the data set should be 
released after the scientist has had sufficient time and opportunity to analyze and publish 
results. 
 
To ensure CMB funds are expended in conformity with the agreed upon purposes, CMB may 
monitor the grant, including reviews or audits of financial and other records. 
 
 

 
 
2 Arrow Street, Cambridge, MA 02138 (T) 617-979-8000 (F) 617-576-0973 www.chinamedicalboard.org 
To indicate that your organization understands and accepts the terms of the grant and the 
attached budget, please countersign and send the enclosed copy of this letter by November 10, 
2024 to Program Manager of CMB Beijing Representative Office, Ms. Iris Zhu, 
(izhu@cmbfound.org). 
 
CMB is pleased to provide this grant. We wish you and your colleagues every success in 
achieving its stated purposes. If you have any questions, please contact Ms. Zhu. 
 
Sincerely yours, 
 
 
 
Roger I. Glass, MD, PhD 
President 
China Medical Board 
 
Attachments: I. Approved Budget; Il. Reporting Schedule; Ill. Payment Instructions 
 
 
ACCEPTED AND AGREED: 
 
By:  
WU Peixin 
Title:  Chairman, Peking Union Medical College Education Foundation  
(Printed name and title) 
 
 
 
Signature: __________________________ Date: ______________________ 
(Signature, date and institution’s official seal) 
 
 
 
Cc: LIU Jiuchang; WANG Ye; John Lichten; Wenkai Li; WANG Yu 
 
 
 
 
 
 

 
 
2 Arrow Street, Cambridge, MA 02138 (T) 617-979-8000 (F) 617-576-0973 www.chinamedicalboard.org 
Attachment I 
 
 
APPROVED BUDGET 
 
Grant Number 24-564 
 
 
January 1, 2025 – December 31, 2027 
 
Items 
Amount ($)
Personnel costs and consultants 
60,000
Equipment and materials 
15,000
Travel and conference  
15,000
Publication and dissemination 
5,000
Administrative cost 
5,000
TOTAL 
$100,000

 
 
2 Arrow Street, Cambridge, MA 02138 (T) 617-979-8000 (F) 617-576-0973 www.chinamedicalboard.org 
Attachment II 
 
 
REPORTING SCHEDULE 
 
Grant number 24-564 
 
Reports should be submitted electronically to the attention of Ms. Iris Zhu at 
izhu@cmbfound.org on the following schedule: 
 
 
Report Due Date 
Report Type 
 
March 31, 2026 
Annual Narrative and Financial report for 
expenditures between January and 
December 2025 
March 31, 2027 
Annual Narrative and Financial report for 
expenditures between January and 
December 2026 
March 31, 2028 
Final Narrative and Financial Report for 
expenditures between January 2025 and 
December 2027 
 

 
 
2 Arrow Street, Cambridge, MA 02138 (T) 617-979-8000 (F) 617-576-0973 www.chinamedicalboard.org 
Attachment III 
 
 
PAYMENT AND BANKING INSTRUCTIONS 
 
 
The CMB will use a wire transfer for payment of grant funds in Chinese Yuan. Please provide 
ALL banking information as required in Chinese.  
 
 
请将下⾯电汇所需信息以中⽂填写完整。 
 
 
a. Name of Bank Account Holder 收款机构账户名  
 
 
 
 
b. Bank Account Number 收款机构⼈⺠币账户号  
 
 
 
 
c. Bank Name 开户银⾏（具体到⽀⾏名称）  
 
 
 
 
d. Address of Bank 开户银⾏地址（具体到省市区） 
 
 
 
 
 
 
 

1 
 
Artificial intelligence-assisted chest X-ray in TB screening: effectiveness of enhancing the care 
cascades in Chinese primary-care settings (ACCESS-CARE) 
1. Cover Letter  
Dear CMB Grant reviewers: 
I am writing to introduce myself as an Assistant Professor at the School of Population Medicine and 
Public Health, Chinese Academy of Medical Sciences & Peking Union Medical College 
(CAMS&PUMC), and to express our keen interest in applying for a grant through the esteemed China 
Medical Board (CMB). Our institution, with its rich history and deep-rooted connection to the CMB, 
stands at the forefront of medical education and research in China. 
In the era of digital health, where technology has the potential to transform lives, our project is a 
timely response to the persistent challenge of tuberculosis (TB). The idea for this project was inspired 
by the need for innovative solutions in early TB detection, aligning with the CMB's vision of 
harnessing technology for health improvement.  
This pioneering project integrates artificial intelligence (AI) into chest X-ray (CXR) screening at 
primary healthcare facilities in China. By conducting a clustered controlled trial, we aim to evaluate 
the effectiveness of AI-assisted chest X-ray (CXR) screening in improving the TB care cascade in 
Chinese primary-care settings. The results of our project are expected to help developing a sustainable 
TB screening strategy that can be widely implemented, thereby enhancing the timeliness and accuracy 
of TB diagnosis. 
My educational background in Epidemiology and Biostatistics, coupled with my professional 
experience in digital health and TB control, has prepared me to lead this project. I am honored to 
collaborate with esteemed colleagues Wei Liu, Zhongjie Li, and Xiaoyou Su, all of whom share a 
passion for improving TB care and are integral to the success of our project. Regarding Wei Liu, our 
acquaintance began at the JF Intelligent Healthcare Medical Technology Academic Conference where 
we forged a strong professional relationship. Their expertise in TB will be invaluable in ensuring the 
project's scientific rigor.  
We appropriate it that the CMB plays a pivotal role in facilitating international collaboration. We 
believe that the CMB's network and resources can support the exchange of knowledge and best 
practices, ensuring that our project's findings are not only locally impactful but also contribute to the 
global fight against TB. 
Thank you for considering our application. I am available for further discussion and would be 
delighted to provide additional information if required. 
Sincerely, 
Wang Ye 
School of Population Medicine and Public Health, CAMS&PUMC 
 

2 
 
2. Cover Page  
 Project details: 
• Project Title: Artificial intelligence-assisted chest X-ray in TB screening: effectiveness of 
enhancing the care cascades in Chinese primary-care settings (ACCESS-CARE) 
• Proposed Total Budget: $100,000 
• Project Duration: 3 years (2025.01-2027.12) 
 PI Information: 
• Full name: Ye Wang 王也 
• Contact Information: +86-13552881677 Email: wangye_pumc@163.com 
• Professional Title: Assistant Professor 
• Institution: School of Population Medicine and Public Health, China Academy of Medical 
Sciences & Peking Union Medical College, Beijing, China 
• Institution Address: 31 Beijige Santiao, Dongcheng District, Beijing 
 Co-PI(s) Information: 
• Full name: Wei Liu, 刘伟 
• Contact Information: +86-13588000511 Email: whhzliuwei@163.com 
• Professional Title: Chief Medical Officer 
• Institution: JF Intelligent Healthcare Medical Technology Co. 
• Institution Address: 1 Dongsanhuan South Road, Chaoyang District, Beijing 
 
• Full name: Zhongjie Li 李中杰 
• Contact Information: +86-13671194343 Email: lizhongjie@sph.pumc.edu.cn 
• Professional Title: Professor 
• Institution: School of Population Medicine and Public Health, China Academy of Medical 
Sciences & Peking Union Medical College, Beijing, China 
• Institution Address: 31 Beijige Santiao, Dongcheng District, Beijing 
 
• Full name: Xiaoyou Su 苏小游 
• Contact Information: +86-13520029120 Email: suxiaoyou@pumc.edu.cn 
• Professional Title: Professor 
• Institution: School of Population Medicine and Public Health, China Academy of Medical 
Sciences & Peking Union Medical College, Beijing, China 
• Institution Address: 31 Beijige Santiao, Dongcheng District, Beijing 
 
 
 
 
 
 
 

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3. Project Summary Statement in English 
The global incidence rate and mortality of tuberculosis (TB) pose a challenge to achieving the goals 
set out in the tuberculosis eradication strategy and the SDGs by 2030. At present, timely and 
accessible early detection methods for tuberculosis are still a major obstacle. In this context, the 
emergence of artificial intelligence (AI), especially the AI-assisted chest X-ray (CXR) in the field of 
diagnostic imaging, has proved the potential to significantly improve the speed and accuracy of 
tuberculosis diagnosis. However, the extent to which these technologies can affect the broader 
tuberculosis care cascade, especially by reducing the diagnostic time in the population level, has not 
yet been explored. The proposed project plans to use the certified AI-assisted CXR system (JF CXR-
1) for tuberculosis screening, which aims not only to integrate AI into the diagnosis process, but also 
to critically assess its impact on the overall tuberculosis care cascade. The selected location for this 
project is Yichang City in western Hubei Province, China, which is facing a high TB burden. The city 
has established a strong city-wide health big data platform ten years ago, providing the basis for this 
project. The project will first optimize the AI-assisted CXR system through retrospective imaging to 
validate the accuracy of case screening (Stage Ⅰ). Secondly, the project will shift its focus to the real 
world, where cluster randomized controlled trials will be conducted in primary-care settings (Stage 
Ⅱ). In this stage, the effectiveness of the AI-assisted CXR system in reducing the diagnostic time of 
TB cases will be evaluated by comparing with those settings without using the tool. In stage Ⅲ, the 
qualitative and quantitative methods will be used to evaluate the generalization, practicality, and 
feasibility of extending the screening strategy in various community environments. If the AI-assisted 
screening strategy is proven accurate, effective, and sustainable, it may pave the way for its 
widespread adoption in primary healthcare institutions and other grassroots areas in China. This can 
not only improve the timeliness of tuberculosis diagnosis, but also help to allocate medical resources 
more effectively and significantly reduce tuberculosis-related incidence and mortality, bringing 
positive changes to global public health. In addition, the results of the project can also provide 
information for policy decisions and guide the formulation of strategies to prioritize the integration 
of AI into health care, which can not only fight against tuberculosis but also a series of other diseases. 

4 
 
4. Project Summary in Chinese 
项目名称： 
人工智能辅助胸片在结核病筛查中的应用：提高中国基层结核病照护级联的有效性 
PI： 
王也 助理研究员 
联系方式: 电话号码：13552881677; 邮箱：wangye_pumc@163.com 
中国医学科学院北京协和医学院群医学及公共卫生学院 
 
Co-PI： 
刘伟 首席医疗官 
联系方式：电话号码：13588000511 邮箱：whhzliuwei@163.com  
江西中科九峰智慧医疗科技有限公司 
 
李中杰 研究员 
联系方式：电话号码：13671194343；邮箱：lizhongjie@sph.pumc.edu.cn 
中国医学科学院北京协和医学院群医学及公共卫生学院 
 
苏小游 研究员 
联系方式：电话号码：13671194343；邮箱：suxiaoyou@pumc.edu.cn 
中国医学科学院北京协和医学院群医学及公共卫生学院 
 
 
项目摘要： 
全球结核病发病率和死亡率对实现2030 年终止结核病战略和可持续发展目标构成了严峻挑战。当前，
及时且易于获取的结核病早期诊断方法依旧是一个重要的难题。在此背景下，人工智能（AI）技术的兴起，
尤其是在诊断成像领域中AI 辅助的胸部X 射线（CXR）技术，已经展现出其在显著提升结核病诊断速度
和准确性方面的潜力。然而，这些技术在人群层面对结核病照护级联的影响，尤其是它们在缩短诊断时间
方面的具体作用，尚未得到充分探索。 
本项目拟采用AI 辅助CXR 系统（JF CXR-1）进行结核病筛查，旨在将人工智能技术整合到诊断流程
中，并对其在整个结核病照护级联中的影响进行深入评估。项目将在湖北省宜昌市开展，该市结核病疾病
负担较重，并且在十年前已建立了一个功能强大的全市健康大数据平台，这为本项目顺利开展提供了良好
基础。 
项目将分为三个阶段进行：第一阶段，通过对大数据平台影像资料的回顾性分析，对AI 辅助CXR 系
统进行验证和优化，以验证其病例筛查的准确性；第二阶段，项目将转向现实世界，在基层医疗机构开展
整群随机对照试验，通过与未使用该工具的对照组相比较，评估AI 辅助CXR 系统在缩短结核病诊断时间
上的有效性；第三阶段，将形成AI 辅助结核病筛查的策略，运用定性和定量研究方法，评估该策略在不同
社区环境中的普适性、实用性和可行性。 
若AI 辅助筛查策略被证实准确、有效、可持续，它将有望为在基层医疗卫生机构中推广该技术铺平
道路，从而在结核病的早期诊断和治疗中发挥重要作用。 

5 
 
5. Project Proposal 
 Background and Rationale： 
Tuberculosis (TB) remains a formidable global health challenge (1, 2), with persistently high 
incidence and mortality rates threatening the achievement of the End TB strategy and the 2030 
Sustainable Development Goals (3). Timely detection is crucial for improving patient outcomes and 
stemming disease transmission (4). 
Digital radiography (DR) devices are widely used for disease diagnosis in multi-level healthcare 
facilities especially in the primary healthcare in low-resource settings. While in these settings, 
shortage of qualified and experienced radiologists is a big problem, which limits the early detection 
and diagnosis of TB by chest radiographs. Addressing these limitations, there is a growing focus on 
AI-based software for tuberculosis detection to improve diagnostic accuracy while reducing costs(5).  
Positioning AI as a revolutionary tool within healthcare systems, its applications have extended to 
detecting extrapulmonary TB (6), and some studies suggest AI may outperform pulmonologists in 
interpreting pulmonary function tests (7). Advanced AI models developed from medical imaging and 
genetic data specifically aim to detect TB, differentiate infection from other pulmonary diseases, and 
identify TB drug resistance (8).  
Currently, five AI algorithms have been certified for TB detection on chest radiographs. A study in 
Bangladesh evaluated the five AI algorithms for TB diagnosis using chest radiographs, comparing 
them with each other and with radiologists(9). All chest films were read independently by three 
radiologists and five AI algorithms, and all five AI algorithms performed significantly better than 
radiologists(10). 
One of the five AI algorithms, JF CRX-1, is the intellectual technology to be implemented and 
evaluated in this proposed project. JF CXR-1 is a simultaneous CXR detection CAD software based 
on CNNs that detects multiple thorax diseases including TB. The software has achieved an AUC of 
0.94, a sensitivity of 0.91, and a specificity of 0.81, which meet the WHO's criteria for the target 
product profile(11), and has been externally validated(12). The JF CXR-1 has already been officially 
approved by the National Medical Products Administration as the Class III medical device 
registration certificate, which is the first Class III AI certificate in the field of TB in China. 
Considering the fact that the JF CXR-1 is developed based on the clinical-diagnostic TB cases, it is 
needed to be validated and optimized using the etiologically positive cases, which is going to be 
conducted in this project. 
Until now, most studies have examined the accuracy of AI algorithms than traditional methods, while 
few have tested the effectiveness of AI-assisted CXR in reducing the disease burden of TB when it is 
implemented in mass screening in the real world. In this project, we aim to not only validate and 
optimize the AI algorithms in diagnosing etiologically positive TB cases but also evaluate how the 
AI-assisted system can help in early detection and diagnosis at the population level.  
AI-driven CXR screening represents the potential in supporting medical decision-making, expanding 
screening capabilities, enhancing medical training, and promoting healthcare equity (13, 14), we wish 
the results of our research can help to format guidelines or strategies in TB screening that can be 
implemented in the domestic and global primary care settings. 

6 
 
 Aim and Objectives： 
The overarching aim of this project is to evaluate the effectiveness of AI-assisted CXR screening in 
improving the TB care cascade by reducing the time of detection and diagnosis among etiologically 
positive TB cases in Chinese primary-care settings. The specific objectives are: 
Objective 1: Using the retrospective chest radiography and case reports, to validate and optimize the 
AI-assisted CXR system and optimize its accuracy for the detection of etiologically positive TB cases. 
Objective 2: Using a prospective cluster randomized controlled trial design, to evaluate the 
effectiveness of the AI-assisted CXR system in reducing the time of detection and diagnosis of 
etiologically positive TB cases when it is implemented in real-world primary healthcare settings. 
Objective 3: Using the mixed methods research (MMR) approach based on the theory of the 
Technology Acceptance Model (TAM), to investigate the generalization, practicality, and feasibility 
of extending the AI-enhanced screening strategy in various community environments. 
 Project Design： 
The study will be structured into three continuous stages. In the first stage, a retrospective study will 
be employed to validate and optimize the AI-assisted CXR system. In the second stage, the 
effectiveness of the optimized system in the real world will be evaluated in a prospective stratified 
clustered randomized controlled trial. In the third stage, mixed-method research will be conducted to 
evaluate the generalizability, practicality, and feasibility of the AI-assisted CXR system. 
 
 
 Stage Ⅰ Validate and optimize the AI-assisted CXR system 
(1) Objective 
By validating and optimizing the AI-assisted CXR system with retrospective chest 
radiography driven from the big-data platform in Yichang, the project aims to improve the 
system's ability to accurately identify etiologically positive TB cases. 
(2) Setting 
The proposed study will be conducted in Yichang, a city located in the western part of Hubei 
Province, China. Yichang has faced a high TB burden in the past years, the incidence was up 
to over 100/100,000 in some townships, which is much higher than the average level in China. 
The disease burden calls for innovation in the field of early detection and diagnosis. This 
region is also notable for its well-established city-wide health big data platform, operational 
for over a decade, which provides a robust foundation for leveraging advanced technological 
tools such as AI in healthcare. 
(3) Project Design 

7 
 
In this stage, the study is designed to extract all the confirmed etiologically positive TB cases 
recorded in the city-wide health big data platform in the past five years when this program 
launched. Those with diseases that can be misdiagnosed as TB (e.g., lung cancer, pneumonia) 
will be extracted as the matched controls. Chest X-ray images will be collected and analyzed 
using the AI-assisted CXR system. Etiologically positive will be considered the gold standard 
for diagnosing TB patients. 
Upon establishing the robust diagnostic performance of the AI-assisted CXR system, the 
study intends to further refine the existing algorithm by incorporating predictive variables 
associated with TB diagnosis, including Body Mass Index (BMI), immune status, underlying 
medical conditions, etc. 
 
(4) Study Population 
Registered patients in city-wide health big data platform in Yichang City diagnosed with TB, as 
well as those with lung cancer, and pneumonia. 
(5) Inclusion and Exclusion Criteria 
Inclusion criteria: Etiologically positive TB cases; Etiologically negative lung cancer and 
pneumonia patients. 
Exclusion criteria: People without complete chest X-ray results or pathogenic results. 
(6) Study Indicators 
Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area 
under ROC curve (AUC), and Youden index of the AI-assisted CXR system. 
(7) Sample Size 

8 
 
The sample size can be calculated using the formula: 
 
θ0 is the ROC area of the null hypothesis (0.865). θ1 is our expected ROC (0.950). V is the 
variance. Zα and Zβ are the Z values corresponding to the values of α and β. 
A two-group (positive/negative or with condition/without) design with continuous response 
data will be used to test the area under the ROC curve against the null value of 0.865. The 
comparison will be made using a two-sided Z-test with a Type I error rate (α) of 0.05. The 
area under the curve will be computed across the entire X-axis (false positive rate) range of 0 
to 1. To detect an area under the curve of 0.95 with 95% power, the number of subjects needed 
will be 83 in the positive group and 83 in the negative group.  
In practice, we design to include all the cases in the database, with enough sample size to 
meet the need. 
(8) Statistical Analysis 
Using the diagnosis of the etiologically positive TB as the gold standard, the diagnostic 
performance of the AI-assisted CXR system will be evaluated by calculating sensitivity, 
specificity, PPV, NPV, the AUC along with the 95% confidence interval, and Youden index. 
as detailed in Table 1. 
Table 1 Diagnostic Test Contingency Table 
the AI-assisted CXR system 
Etiological results 
Total 
Positive 
Negative 
Positive 
True Positive A 
False Positive B 
A+B 
Negative 
False Negative C 
True Negative D 
C+D 
Total 
A+C 
B+D 
N 
Note: sensitivity =A/(A+C)×100％; specificity =D/(B+D) ×100％; PPV: A/(A+B) ×100％; 
NPV: D/(C+D) ×100％; Youden index: (sensitivity+ specificity)-1 
 
 Stage Ⅱ Evaluation of the effectiveness of the AI-assisted CXR system in reducing the time 
of detection and diagnosis of TB cases 
(1) Aim and Objectives 
To test whether the AI-assisted CXR system can reduce the time of detection and diagnosis of 
etiologically positive TB cases when it is implemented in the real-world setting, by carrying out a 
cluster-randomized controlled trial. 
(2) Study Design 
A cluster-randomized controlled trial will be conducted at the township level in Yichang. The 
stratification will be grounded before the randomization based on the average reported TB 
incidence rates from the past three years among the 86 townships in Yichang. These rates will be 

9 
 
classified into lower tier (below 100/100,000) and higher tier (more than 100/100,000). Then the 
towns in each tier will be randomly allocated into the intervention or control group with the 
corresponding TB screening strategy. All TB cases screened in the following 12 months will be 
included in the study. 
 
(3) Intervention and Control Group 
Intervention group: The intervention involves utilizing the AI-assisted CXR system. This system 
analyzes chest radiographs to identify potential signs of TB. Positive results flagged by the AI 
require further assessment by a physician or specialist to confirm the diagnosis. Following 
confirmation, detailed diagnostic tests such as sputum smear microscopy, culture tests, or 
molecular biology diagnostics are conducted. 
Patients diagnosed with TB will receive a personalized treatment plan tailored to their specific 
condition, which might include medication therapy, regular follow-ups, and lifestyle adjustments. 
Information on TB, guidance on treatment options, and advice on lifestyle changes will be 
provided. 
Control group: The control group will use conventional methods for TB screening which do not 
involve the AI-assisted CXR system. These methods include standard chest X-ray analysis and 
physical examination. 
(4) Outcomes Evaluation 
Primary outcome: The difference in the average number of days from patient visit to diagnosis 
between the intervention and the control group. 
Secondary outcome: The exploratory outcomes include the difference between the intervention 
and the control group in the number of TB cases, and the above changes in the intervention group 
after the implementation of the AI-assisted TB screening strategy. 

10 
 
Economic evaluation: The cost-effectiveness of utilizing the AI-assisted TB screening strategy 
will be assessed. 
(5) Sample Size 
The sample size calculation based on primary outcome will consider differences between mean 
number of days from patient visit to diagnosis between different groups, using the following 
formula: 
2mk=2N×[1+ρ(𝑚ഥ-1)] 
k is the sample size of the cluster, 𝑚ഥ is the average cluster size, N is the sample size required for 
each group of the corresponding individual randomized trial, and ρ is the intra-cluster correlation 
coefficient (ICC). In this study, the 𝑚ഥ=40, k=2, ρ is presumed to be 0.2, N can be calculated from: 
 
in which, Zα/2 is 1.96, Zβ is 1.28, μ2-μ1 is presumed to be 3, σ is presumed to be 2. The N will be 
10. As the result, in the cluster-randomized controlled design, the total number of cases need to be 
included will be 176. It is expected that over 3,000 TB cases will be included during our study, 
which can meet the required sample size. 
(6) Statistical analysis 
All TB cases screened during the study period will be included in the study. Data analysis will be 
mainly performed according to the intention-to-treat approach.  
For primary outcome evaluation, statistical comparisons between intervention and control groups 
for the average diagnostic time and other continuous variables of confirmed TB cases will be made 
using regression analysis with robust SEs accounting for clustering. Subgroup analysis will be 
conducted according to different purposes.  
For secondary outcome evaluation, all TB-related cases detected will be included in data analysis, 
and difference test between intervention and control groups will be performed in the numbers of 
TB-related cases detected and confirmed TB cases. The same method will be used to analyze the 
changes before and after the implementation of the AI-assisted TB screening strategy within the 
intervention group. 
In addition, a suitable cost-effectiveness analysis model will be established to assess the cost and 
benefits of utilizing the AI-assisted TB screening strategy in these experimental towns in Yichang. 
 Stage Ⅲ Acceptance of the AI-assisted CXR system in various populations 
(1) Objectives 
This stage aims to assess the generalizability, practicality, and feasibility of the AI-assisted CXR 
system based on TAM theory, ultimately integrating the evidence from stage Ⅰ and stage Ⅱ to 
develop practical guidelines for TB screening using the AI-assisted CXR system. 
(2) Study Design 

11 
 
This is a cross-sectional study utilizing a mixed methods research (MMR) approach based on the 
theory of the Technology Acceptance Model (TAM). TAM is a theoretical model that includes 4 
influencing factors of system usage: perceived usefulness, perceived ease of use, attitude toward 
using the system, and behavioral intention. A TAM-based quantitative survey will be conducted to 
analyze the influencing factors of the willingness to use the AI-assisted CXR system among 
healthcare workers, TB patients, and relevant local government officials. Additionally, TAM-based 
offline focus group interviews will be conducted to explore the attitudes, acceptance levels, and 
influencing factors concerning the AI-assisted CXR system among healthcare workers, TB patients, 
and government officials, and their political suggestions for the system. 
(3) Research indicators 
Outcome: Willingness to use the AI-assisted CXR system. 
Factors: External variables (such as demographic data), perceived ease of use, perceived 
usefulness, and attitude towards the AI-assisted CXR system. 
(4) Setting 
The research setting will be the primary healthcare institutions in Yichang involved in this project. 
The data will be collected using convenience sampling.  
 
(5) Eligibility criteria 
Inclusion criteria: (1) Over 18 years old; (2) Healthcare workers with professional qualifications 
and have ever used the AI-assisted CXR system; TB patients diagnosed using the AI-assisted CXR 
system; relevant government officials; (4) Willing to participate and sign an informed consent form.  
Exclusion criteria: For quantitative research: Not proficient in Chinese. 

12 
 
For qualitative research: (1) Those showing significant discomfort, hesitation, or avoidance 
regarding privacy issues that prevent continuation of interviews; (2) Those who provide short, 
irrelevant responses during interviews.  
(6) Sample size 
For the quantitative research, the sample size will be at least ten times the number of scale items 
based on the 10 Events Per Variable (10 EPV) principle. Assuming an average of 3 items would 
be assigned to each dimension of the TAM, 120 samples will be required. Considering an effective 
recovery rate of 80%, at least 150 participants will be required. 
For the qualitative research, the actual sample sizes will be determined based on the data saturation. 
(7) Instruments 
A survey questionnaire and an interview guide will be developed following an iteration process 
based on the TAM model, expert suggestions, and a pilot study. The questionnaire and interview 
guide will include demographic information and items based on TAM with 4 dimensions: 
perceived ease of use, perceived usefulness, attitude towards using the AI-assisted CXR system, 
and willingness to use the AI-assisted CXR system. 
(8) Statistical Analysis 
For quantitative research, statistical descriptions will be used to describe perceived ease of use, 
perceived usefulness, attitude towards using the AI-assisted CXR system, and willingness to use. 
Linear regression and structural equation modeling will be used to identify factors associated with 
the willingness to use the system. For qualitative research, “Colaizzi's phenomenological approach” 
will be used for analysis.  
 Limitations： 
One limitation is that we evaluate AI+DR in this study, as DR is currently mainly used at the township 
level in rural areas in China. In many urban healthcare facilities, CT but not DR is more commonly 
used. Therefore, in the future, it is necessary to further explore the strategy of AI+CT for TB screening 
in various settings. Secondly, this project will last for only three years, and some more crucial 
indicators such as mortality and morbidity may not be evaluated in such a short term. 
 Pilot study： 
A pilot study will be conducted in all continuous three stages. In the first stage, we will involve 50 
TB cases and 50 controls to initially assess the AI-assisted CXR system. In the second stage, two 
towns in the intervention group and two towns in the control group will be included to test the whole 
process. In the last stage, five people from each category will be invited. This will provide preliminary 
insights into the performance and potential adjustments needed for the AI-assisted CXR system 
before a full-scale trial. 
 Outcomes, Dissemination, and Sustainability： 
The project anticipates the development of a refined AI-assisted CXR system and a sustainable TB 
screening strategy. It expects to generate a more precise AI-assisted CXR system and a feasible 
strategy for TB screening in Chinese primary-care settings. With the implementation of the AI-
assisted strategy, we expect to see enhanced TB care cascades especially reduced time of detection 

13 
 
and diagnosis. Outcomes will be disseminated through academic publications, conferences, and 
policy briefings. Sustainability is ensured by integrating the AI system into existing healthcare 
infrastructure and training local staff. 
 Feasibility and Risk Assessment： 
The project's feasibility is supported by the established health big data platform in Yichang and the 
expertise of the multidisciplinary team. Risks, such as technological failures or adoption resistance, 
will be mitigated through continuous system updates, stakeholder engagement, and iterative training 
programs. Each team member offers knowledge, skills, and networks that can contribute to ensuring 
the project’s feasibility and successful completion within a 36-month timeframe. 
 Monitoring and Evaluation： 
Project progress will be monitored using key performance indicators (KPIs) aligned with project 
objectives. Evaluation will involve both quantitative analysis of care cascade metrics and qualitative 
assessment of system usability and impact. 
Monitoring of the research process 
Ye Wang, the PI, will oversee the study design and implementation throughout the process, to ensure 
research activities meet the milestones. 
Major monitoring activities will be conducted, to ensure: 
 Achieving the objectives of all the study phases. 
 All quantitative research tools are properly designed, pilot-tested, and revised. 
 All data collectors are properly trained and understand the procedure and content. 
 All local partners understand the significance, requirements, and anticipated outcomes so 
that the study is implemented smoothly and completely.  
 Data entry, cleaning, coding, and analysis are completed on time, and the method of data 
analysis is appropriate. 
 Progress to be systematically tracked and annually reviewed both internally and with CMB. 

14 
 
 Timeline and Milestones： 
Activities and Milestones 
YEAR 1 
YEAR 2 
YEAR 3 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
1 
2 
3 
4 
5 
6 
7 
8 
9 
10 
11 
12 
Ethic review 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
PILOT STUDY 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Pilot study preparation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Pilot data extraction 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Pilot interview 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Program revision 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
STAGE Ⅰ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Retrospective collection of images 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Cases inclusion and exclusion 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
AI-assisted CXR system validation 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
AI-assisted CXR system optimization 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
STAGE Ⅱ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Pre-intervention survey 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Design of the intervention program 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Implementation of the AI-assisted 
screening  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data management and analysis 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
STAGE Ⅲ 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Design of questionnaire 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Interview with participants 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Data analysis 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
FULL PROJECT 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Report and publication 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Formation TB screening strategy and 
raise policy proposals 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

15 
 
6. References: 
1. Phillips JA. Global Tuberculosis. Workplace Health Saf. 2015;63(10):476. 
2. Orcau À, Caylà JA, Martínez JA. Present epidemiology of tuberculosis. Prevention and control 
programs. Enferm Infecc Microbiol Clin. 2011;29 Suppl 1:2-7. 
3. Fitchett JR, MacPherson P, Corbett EL. Implementing the End TB Strategy and the intersection 
with the Sustainable Development Goals, 2016-2030. Trans R Soc Trop Med Hyg. 2016;110(3):145-
7. 
4. Lönnroth K, Jaramillo E, Williams BG, Dye C, Raviglione M. Drivers of tuberculosis epidemics: 
the role of risk factors and social determinants. Soc Sci Med. 2009;68(12):2240-6. 
5. Du J, Su Y, Qiao J, Gao S, Dong E, Wang R, et al. Application of artificial intelligence in 
diagnosis of pulmonary tuberculosis. Chinese medical journal. 2024;137(5):559-61. 
6. Sharma A, Sharma A, Malhotra R, Singh P, Chakrabortty RK, Mahajan S, et al. An accurate 
artificial intelligence system for the detection of pulmonary and extra pulmonary Tuberculosis. 
Tuberculosis (Edinb). 2021;131:102143. 
7. Topalovic M, Das N, Burgel PR, Daenen M, Derom E, Haenebalcke C, et al. Artificial 
intelligence outperforms pulmonologists in the interpretation of pulmonary function tests. Eur Respir 
J. 2019;53(4). 
8. Liang S, Ma J, Wang G, Shao J, Li J, Deng H, et al. The Application of Artificial Intelligence in 
the Diagnosis and Drug Resistance Prediction of Pulmonary Tuberculosis. Front Med (Lausanne). 
2022;9:935080. 
9. Santosh KC, Allu S, Rajaraman S, Antani S. Advances in Deep Learning for Tuberculosis 
Screening using Chest X-rays: The Last 5 Years Review. Journal of medical systems. 2022;46(11):82. 
10. Qin ZZ, Ahmed S, Sarker MS, Paul K, Adel ASS, Naheyan T, et al. Tuberculosis detection from 
chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial 
intelligence algorithms. The Lancet Digital health. 2021;3(9):e543-e54. 
11. Yang Y, Xia L, Liu P, Yang F, Wu Y, Pan H, et al. A prospective multicenter clinical research 
study validating the effectiveness and safety of a chest X-ray-based pulmonary tuberculosis screening 
software JF CXR-1 built on a convolutional neural network algorithm. Frontiers in medicine. 
2023;10:1195451. 
12. Qin ZZ, Van der Walt M, Moyo S, Ismail F, Maribe P, Denkinger CM, et al. Computer-aided 
detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: 
external validation and modelled impacts of commercially available artificial intelligence software. 
The Lancet Digital health. 2024. 
13. Sachedina D, Hooda R, Fawaz B. Practical applications of artificial intelligence in dermatology 
residency training. Clin Exp Dermatol. 2024;49(8):925-6. 
14. Al-Naser Y. How medical radiation technologists can foster equity, diversity, and inclusion 
through artificial intelligence in radiology. J Med Imaging Radiat Sci. 2024:101436. 
 

16 
 
7. Budget 
 Budget Table 
 
 Narrative Explanation of Budgetary Item 
Personnel and Consultants: 
Year 1: $25,000 - Initial setup and optimization of AI-assisted CXR system; 
Year 2: $20,000 - Implementation and monitoring of TB screening strategy; 
Year 3: $15,000 - Data analysis and formulation of sustainable TB screening strategy. 
Equipment, Materials, and Supplies: 
Year 1: $5,000 - Initial purchase for AI-assisted CXR system setup; 
Year 2: $5,000 - Scale-up with additional hardware and software for expanded screening; 
Year 3: $5,000 - Equipment maintenance and supplies for project completion. 
Travel: 
Year 1: $5,000 - Travel for initial setup, training, and international conference; 
Year 2: $3,000 - Travel for monitoring and evaluation; 
Year 3: $2,000 - Travel for final evaluation and dissemination. 
Conferences: 
Year 1: $3,000 - Fees and travel for presenting preliminary findings; 
Year 2: $1,000 - Participation in local and regional conferences; 
Year 3: $1,000 - Dissemination of final results at conferences. 
Publication and Dissemination: 
Year 1: $0 - No planned publications or major dissemination activities; 
Year 2: $2,500 - Publication fees, conference fees, and dissemination materials; 
Year 3: $2,500 - Publication fees, conference fees, and dissemination materials. 
Category 
Year 1 
Year 2 
Year 3 
Total 
Personnel and Consultants 
25,000 
20,000 
15,000 
60,000 
Equipment, Materials, and Supplies 
5,000 
5,000 
5,000 
15,000 
Travel 
5,000 
3,000 
2,000 
10,000 
Conferences 
3,000 
1,000 
1,000 
5,000 
Publication and Dissemination 
0 
2,500 
2,500 
5,000 
Administrative Costs 
2,000 
1,500 
1,500 
5,000 
Total 
40,000 
33,000 
27,000 
100,000 

17 
 
Administrative Costs: 
Year 1: $2,000 - General administrative expenses during initial setup; 
Year 2: $1,500 - Reduced administrative support as the project progresses; 
Year 3: $1,500 - Administrative costs for project completion and integration. 
 Other Monetary Support (if applicable)  
There’s no other monetary support. 
 
 
 
 

18 
 
8. Concise CVs of PI and Co-PIs  
 
Ye Wang, PhD 
No. 31, Beijige Santiao, Dongcheng District, Beijing, 100730 
Mobile: +86-13552881677 
Email: wangye_pumc@163.com 
Date of birth: September 28, 1993 
Educational Background 
2018-2021 
PhD 
Epidemiology and Biostatistics, Peking Union Medical College, 
Beijing, China 
Thesis title: Association of Obesity with Environmental Factors and 
MC4R Gene polymorphism in Yi People in Liangshan Prefecture, 
Sichuan Province 
Supervisor: Guangliang Shan 
2016-2018 
Master 
Epidemiology and Biostatistics, Peking Union Medical College, 
Beijing, China 
2011-2016 
Bachelor 
Preventive Medicine, Soochow University, Suzhou, China 
Professional Experience 
2021.8-present 
Assistant professor, School of Population Medicine and Public Health, 
Peking Union Medical College, Chinese Academy of Medical Sciences 
2023.11-present 
Deputy director, Office of Discipline Development, School of 
Population Medicine and Public Health, Peking Union Medical College, 
Chinese Academy of Medical Sciences  
FIELDS OF INTEREST:  Digital health, TB, Respiratory disease, Prediction model, 
Epidemiologic methods, Real-world study 
Grants 
Early accurate assessment and comprehensive intervention of major chronic respiratory diseases, 
CAMS Innovation Fund for Medical Sciences, 2021-2025 (Sub-project: Development and validation 
prediction model for chronic respiratory disease based on fine classification, PI) 
Research & Project Experience 
 
Life-course cohort study and digitalization construction in Chinese Academy of Medical Sciences, 
CAMS Innovation Fund for Medical Sciences, 2020-2021 
General population cohort study in Beijing-Tianjin-Hebei, National Key Research and Development 
Plan-Key Project of Precision Medicine Research, 2016-2021 
Longitudinal comparison and prediction of cardiovascular and metabolic risk in Yi migrants, National 
Natural Science Foundation of China, 2012-2015 

19 
 
China national health survey and basic physiological parameters (Phase II), Science and Technology 
Basic Work Project, 2013-2017 
Awards and Honors 
APSR Young Investigators Travel Award, 27th Congress of the Asian Pacific Society of 
Respirology, Singapore, November 2023. 
Outstanding Graduates Award, Peking Union Medical College, June 2021 
Outstanding Presentation Award, 8th National Conference of Epidemiology, China, July 2019  
Outstanding Graduates Award, Medical College, Soochow University, June 2016 
Selected Publications 
Published 
1. Wang Y, He R, Ren X, Huang K, Lei J, Niu H, Li W, Dong F, Li B, Yang T, Wang C. Developing 
and validating prediction models for severe exacerbations and readmissions in patients 
hospitalised for COPD exacerbation (SERCO) in China: a prospective observational study. BMJ 
Open Respir Res. 2024 May 7;11(1):e001881. 
2. Wang Y, Pan L, He H, Li Z, Cui S, Yang A, Li W, Jia G, Han X, Wang X, Shan G. Prevalence, 
associated factors, and gene polymorphisms of obesity in Tibetan adults in Qinghai, China. BMC 
Public Health. 2024 Jan 26;24(1):305. 
3. Wang Y, He R, Dong F, Liu D, Ren X, Yang T, Wang C. Re-exacerbation within 30 days of 
discharge is associated with poor prognosis in the following year among patients hospitalised with 
exacerbation of chronic obstructive pulmonary disease: a clinical cohort study. BMJ Open Respir 
Res. 2023 Aug;10(1):e001759.  
4. He R, Ren X, Huang K, Lei J, Niu H, Li W, Dong F, Li B, Wang Y#, Yang T#, Wang C. Influenza 
and pneumococcal vaccination coverage and associated factors in patients hospitalized with acute 
exacerbations of COPD in China: Findings from the real-world data. Chin Med J (Engl). 2023 Jul 
24. (Co-corresponding author) 
5. Ren X*, Wang Y*, He R, Dong F, Liu D, Yang T, Wang C. Mortality and readmission risk for 
hospitalised patients with acute exacerbation of COPD with and without spirometric obstruction: 
a longitudinal observational study in China. BMJ Open. 2023 Jun 5;13(6):e071560. (Co-first 
author) 
6. Wang Y, Pan L, Wan S, Yihuo W, Yang F, Li Z, Yong Z, Shan G. Body fat and muscle were 
associated with metabolically unhealthy phenotypes in normal weight and overweight/obesity in 
Yi people: A cross-sectional study in Southwest China. Front Public Health. 2022 Oct 
6;10:1020457.  
7. Wang Y, Pan L, Wan S, Yihuo W, Yang F, He H, Li Z, Yong Z, Shan G. MC4R Gene 
Polymorphisms Interact With the Urbanized Living Environment on Obesity: Results From the Yi 
Migrant Study. Front Genet. 2022 Apr 14;13:849138.  
8. Wang Y, Pan L, Wan S, Yi H, Yang F, He H, Li Z, Yong Z, Shan G. Association of Socioeconomic 
Status and Overweight/Obesity in Rural-to-Urban Migrants: Different Effects by Age at Arrival. 
Front Public Health. 2020 Dec 17;8:622941.  

20 
 
9. Wang Y, Pan L, Wan SP, Yi HW, Yang F, He HJ, Li Z, Zhang J, Yong ZP, Shan GL. Association 
between age at arrival, duration of migration, and overweight/obesity in Chinese rural-to-urban 
migrants: the Yi migrant study. Chin Med J (Engl). 2020 Aug 20;134(1):60-67.  
10. Wang Y, Pan L, Wan S, Yi H, Yang F, He H, Li Z, Zhang J, Wang X, Yong Z, Shan G. Increasing 
prevalence of overweight and obesity in Yi farmers and migrants from 2007 to 2015 in China: the 
Yi migrant study. BMC Public Health. 2018 May 24;18(1):659. 
11. He R, Wang Y, Ren X, Huang K, Lei J, Niu H, Li W, Dong F, Li B, Yang T, Wang C. Associations 
of medication regimen complexity with medication adherence and clinical outcomes in patients 
with chronic obstructive pulmonary disease: a prospective study. Ther Adv Respir Dis. 2023 Jan-
Dec;17:17534666231206249. 
Under Review 
1. Effect of Glucocorticoid Administration Routes for Patients with Acute Exacerbations of Chronic 
Obstructive Pulmonary Disease in China: A Propensity Score-Matched Longitudinal Analysis 
2. Understanding Psychological Symptoms Among Chinese College Students During the COVID-19 
Omicron Pandemic: Findings from a National Cross-Sectional Survey in 2023 
3. Prognostic Risk Prediction Models for Acute Exacerbation of Chronic Obstructive Pulmonary 
Disease: A Systematic Review 
Digital Application 
 
All-Health mini-app system, software copyright, 2023SR0749095, 2023 
All-Health backstage management system, software copyright, 2023SR0749096, 2023 
Previous Participation in CMB Activities 
None. 
 

21 
 
Wei Liu, Ph.D 
No. 1 Dongsanhuan South Road, Chaoyang District, Beijing, 100020 
Mobile: +86-13588000511 
Email: whhzliuwei@163.com 
 
Brief Introduction 
The Chief Medical Officer of JF Healthcare Co., Ltd., General Manager of the Tuberculosis and 
Infection Disease Control Division，and Executive Director of the Artificial Intelligence Network 
Laboratory of the National Medical Center for Infectious Disease. 
At present, mainly engaged in the cross-disciplinary research and medical application of artificial 
intelligence and infectious diseases, the development of diagnostic products of infectious diseases.  
He has published many SCI papers and undertaken many national and provincial major scientific 
research projects. He has rich work experience in many fields of medicine and health. 
Doctoral supervisor of Shanghai Jiaotong University, external graduate supervisor of the School of 
Biomedical Engineering and Instrumentation Science of Zhejiang University.  
 
Education  
2011-2016 
Postdoc 
Infectious Diseases, Department of Infectious Diseases, Huashan 
Hospital, Fudan University, Shanghai, China 
2005-2011 
PhD 
Microbiology, School of Life Science Fudan University, Shanghai, 
China 
1997-2001 
Bachelor 
Microbiology, Central China Agricultural University, Beijing, 
China 
 
Work experience: 
2023.3-present 
JF Healthcare Co., Ltd. As the Chief Medical Officer, General Manager of the 
Tuberculosis and Infection Disease Control Division, Executive Director of the 
Artificial Intelligence Network Laboratory of the National Medical Center for 
Infectious Disease. 
2018.7-2023.2 
Joinstar Biotechnology Co., Ltd. As the Executive Deputy Director of the 
Shanghai Jiaotong University- Joinstar Medical Diagnostic Technology Joint 
Laboratory, the President of the Zhejiang Joinstar In Vitro Diagnostic High-tech 
Enterprise Research Institute. 
2010.8-2018.7 
Hangzhou Center for Disease Control and Prevention，Tuberculosis Prevention 
and Control Institute, as the head of the Reference Laboratory for 
Tuberculosis, Engaged in the field of molecular epidemiology, rapid diagnosis, 
drug resistance mechanism research, and quality control of tuberculosis 
laboratories in the city. 
2001.8-2005.8 
East China Medicine Group Co., Ltd., Gene Technology Research Institute)，
Engaged in the research and development of antibiotic drugs and genetically 
engineered drugs. 
 

22 
 
Research projects: 
1. Twelfth Five-Year National Science and Technology Major Project: Research on the Clinical 
Treatment of Multi-Drug Resistant Tuberculosis, Sub-Project Leader (Project No. 2013ZX10003008-
003)  
2. Zhejiang Province's Key Project: Project Leader of Research and Development of Common 
Consumables and Reagents for Biomedical Laboratories (Project No. 2022C03002),  
3. Zhejiang Province's Key Project: project leader of High-sensitivity Rapid Detection Technology 
and Products for COVID-19 Variants (Project No. 2022C03047),  
4. Zhejiang Province's Key Project: Project leader of Disruptive Quick and Highly Sensitive COVID-
19 Detection reagent (Project No. 2022C03187), 
5.The first (set) key equipment engineering project in Zhejiang Province: Project leader of Fully 
automatic flow fluorescence chemiluminescence integrated analysis system and supporting core 
coding microspheres. 
 
Major Industrialization Achievements: 
1. The "Computer-Aided-Design X-ray Image for Tuberculosis Software" (JF CXR-1) (Registration 
Certificate No.: NMPA 20223211374), is the first Class III certificate in the field of infectious diseases 
and X-ray chest radiography, and has been Listed in STOP-TB partnership  (which belong to the 
World Health Organization's）product list recommended. 
  
Main Article: 
Liu W, Chen J, Shen Y, et al., Phenotypic and genotypic characterization of pyrazinamide resistance 
among multidrug-resistant Mycobacterium tuberculosis clinical isolates in Hangzhou, China, Clinical 
Microbiology and Infection (2018),doi: 10.1016/j.cmi.2017.12.012. IF=6.11  
2. Longxiang Xie, Wei Liu (co-first author), Qiming Li, et al. First Succinyl-Proteome Profiling of 
Extensively Drug-Resistant Mycobacterium tuberculosis Revealed Involvement of Succinylation in 
Cellular Physiology. Journal of Proteome Research, 2014,14(1):107-119,IF=4.51  
3. Jing Wu, Wei Liu (co-first author), Lei He et al. Sputum Microbiota Associated with New, 
Recurrent and Treatment Failure Tuberculosis. PLoS ONE 2013,8(12)：e83445IF=3.73  
 
Main Honors and Practical experience : 
As the head of the reference laboratory for tuberculosis in Hangzhou, he led the city's tuberculosis 
quality control work, made beneficial contributions to the creation of a national demonstration area 
for the diagnosis and treatment of multi-drug resistant tuberculosis, and promoted the implementation 
of the Gates Foundation and Global Fund multi-drug resistant tuberculosis project in Zhejiang 
Province. He was awarded the National Science and Technology Major Special Project: Clinical 
Treatment Research on Multi-drug Resistant Tuberculosis.  
 
The "Computer-Aided-Design X-ray Image for Tuberculosis Software" (JF CXR-1) (Registration 
Certificate No.: NMPA 20223211374), is the first Class III certificate in the field of infectious diseases 
and X-ray chest radiography, and has been Listed in STOP-TB partnership (which belong to the World 
Health Organization's）product list recommended. 
 

23 
 
Main academic positions: 
1. Member of the Clinical Laboratory Committee of the Tuberculosis Branch of the Chinese Medical 
Association 
2. Member of the Laboratory Medicine Expert Committee of the National Health Industry 
Association 
3. Member of the Biological Diagnostic Technology Branch of the China Pharmaceutical 
Biotechnology Association 
4. Member of the Expert Committee of the Zhejiang Provincial Quality Control Center for the 
Diagnosis and Treatment of Tuberculosis 
 
Main Reports on national academic conferences  
1. New technology of artificial intelligence screening for tuberculosis in lung imaging. The 34th 
National Academic Conference of the Chinese Anti-Tuberculosis Association. April 26, 2023. 
Xiamen, Fujian Province 
2. New technology of AI screening for tuberculosis based on DR imaging data. The 2nd National 
Forum on Innovation in Tuberculosis, April 20, 2023. Haikou, Hainan 
3. Utilizing artificial intelligence imaging screening technology to enhance the comprehensive 
prevention and control of tuberculosis on campus. The 4th National Academic Forum on the 
Prevention and Control of Tuberculosis and Common Infectious Diseases in Schools. June 16, 2023. 
Nanning, Guangxi 
4. Application of AI imaging technology in active screening of tuberculosis. The 2nd Oriental 
International Infectious Diseases Academic Week and the National Infectious Diseases Medical 
Center (Shanghai) Cooperation Forum.  
5. Adopting CAD imaging technology to improve the application of active screening for tuberculosis. 
The 3rd National Forum on Innovation in Tuberculosis and the 4th International Forum on Research 
and Development Cooperation in Tuberculosis in China.  
6. Application of AI in the Active Detection of Tuberculosis, 6th Academic Conference of the 
Southwest Tuberculosis Hospital Alliance and the 2024 Annual Meeting. 

24 
 
Zhongjie Li, Ph.D 
No. 31, Beijige Santiao, Dongcheng District, Beijing, 100730 
Mobile: +86-13671194343 
Email: lizhongjiecdc@163.com 
Date of birth: August 15, 1977 
Brief Introduction 
LI Zhongjie’s main research field primarily focuses on the epidemiology of infectious diseases. His 
research interests include infectious disease transmission dynamics, disease surveillance and early 
alert, disease burden and evaluation of the effectiveness of interventions. He has been engaged in 
technical support and policy research for infectious disease surveillance and prevention and control 
in national disease control departments and research institutions for nearly twenty years. As the 
project leader, he has presided over several China key research and development plans, science and 
technology support plans, and National Nature Fund projects. 
In the past five years, as the first author or corresponding author, he has published more than 20 
academic papers on infectious disease epidemiology in international academic journals such as 
Lancet series and Nature series, and the research results have provided support for the formulation of 
national infectious disease prevention and control programs and policies.  
 
Educational Background 
2013-2016 
PhD 
Epidemiology and health statistics, Chinese Center for Disease 
Control and Prevention, Beijing, China 
2001-2004 
Master 
Epidemiology and health statistics, Chinese Academy of 
Preventive Medicine, Beijing, China 
1996-2001 
Bachelor 
Preventive Medicine ，School of Public Health, West China 
University of Medical Sciences，Sichuan, China 
Professional Experience 
2023.9-present 
Assistant dean, School of Population Medicine and Public Health, Peking 
Union Medical College, Chinese Academy of Medical Sciences, engaged in the 
overall coordination of scientific research, teaching and management. 
2022.8-2023.9 
Section chief, the National Bureau of Disease Control and Prevention, the 
Department of Infectious Disease Prevention and Control, Epidemiological 
Investigation and Public health Laboratory Division 
2021.4-2022.8 
Section chief, National Health Commission, Tuberculosis Prevention and 
Control Division of Disease Control Bureau 
2014.12-2015.2 
China provided assistance to the public health expert team on Ebola prevention 
and control in Sierra Leone, serving as the deputy head of the team 

25 
 
2012.9-2021.4 
Section chief, Department of Infectious Disease Prevention and Control, 
Chinese Center for Disease Control and Prevention 
Research & Project Experience 
National Key Research and Development Plan, research and application demonstration of intelligent 
methods for social data-based management of major infectious diseases, Project leader (Project No. 
2023YFC2308700)，2023-2026 
National Science and Technology Major project, "Belt and Road" important infectious disease 
epidemic law and early warning and response technology research, Sub-Project Leader (Project No. 
2018ZX10101-002)，2018-2020 
National Natural Science Foundation of China, Epidemiological Research on Asymptomatic 
Infection of Novel Coronavirus -- Central China (Hubei), Sub-Project Leader (Project No. 82041029)，
2020-2021 
National key research and development Plan, key measures for the prevention and control of 
infectious diseases and emergency plan system research, Sub-Project Leader (Project No. 
2021YFF0306002)，2021-2022 
Major Industrialization Achievements 
Invention patent "Method for Monitoring and Warning of Infectious Disease Symptoms with 
Multivariate Data in Large scale Activities" 
Selected Publications 
1.Li ZJ, Chen Q, Feng L, et al Active case finding with case management: the key to tackling the 
COVID-19 pandemic. Lancet. 2020 Jul 4;396(10243):63-70. IF:168.9 
2. Li ZJ, Zhang HY, Ren LL, et al.Etiological and epidemiological features of acute respiratory 
infections in China. Nature Communications .2021 Aug 18;12 (1):5026.IF:16.6 
3.Li ZJ, G.F. Gao, et al. Infectious disease trends in China since the SARS outbreak.The Lancet 
Infectious diseases .2017. 17(11): p. 1113-1115. IF:56.3 
4. Li ZJ, Liu F,CuiJ,et al.Comprehensive large-scale nucleic acid-testing strategies support China's 
sustained containment of COVID-19. Nature Medicine .2021May;27(5):740-742. IF:82.9 
5. Li ZJ, Yu LJ, Zhang HY, et al.Broad Impacts of Coronavirus Disease 2019 (COVID-19) 
Pandemic on Acute Respiratory Infections in China: An Observational Study. Clinical Infectious 
Diseases .2022 Aug 24;75(1): e1054 -e1062.IF:21.0 
Main Honors and Practical experience  
From April 2021 to August 2022, he served as the director of the Tuberculosis Prevention and Control 
Department of the Disease Prevention and Control Bureau of the National Health Commission, and 
has been involved in the formulation of national TB control planning, norms and technical guidelines 
for many times. He won the honor of "exemplary individual in the National Science and Technology 
System to Fight COVID-19" issued by the Ministry of Science and Technology. He served as an 
expert of the Disease Control Expert Advisory Committee of the National Health Commission, and 
the leader of the COVID-19 Emergency Response Prevention and Control Technical Team of the 
China Center for Disease Control and Prevention, and submitted more than 20 prevention and control 
policy recommendations and professional research and judgment reports to the country based on 
scientific research achievements. 
In 2020, the research team led by him won the title of "National Advanced Collective in Fighting 
COVID-19". In the future, we will continue to focus on the research of response strategies and 

26 
 
technologies for potential infectious diseases during pandemics, with a particular emphasis on 
emerging technologies such as big data and artificial intelligence. We will accelerate their 
technological innovation in the field of infectious disease monitoring, early warning, and prevention, 
effectively promote their transformation and application, and significantly enhance China's ability to 
prevent and control sudden new infectious diseases. 
He has rich experience in scientific research and prevention and control practices in national level 
institutions, understands the major strategic needs in the field of national infectious disease prevention 
and control, deeply participates in the formulation of national prevention and control policies and 
measures, and has sensitive judgment on scientific and technical issues that are prioritized for 
research. The recommender can effectively promote practical scientific and technological innovation 
research, guide and influence more research teams across the country to carry out urgently needed 
scientific research work, promote the comprehensive improvement of China's scientific research 
capabilities in the field of monitoring, early warning, and prevention and control of sudden and new 
infectious diseases, and cultivate a group of scientific and technological talents in the field of 
infectious diseases who can solve practical and complex problems for the country. 
Main academic positions 
1. The applicant's research field is infectious disease epidemiology, with nearly 20 years of experience 
working on the front line in national level disease control departments and research institutions. They 
have strong identification ability for major scientific and key technical issues in national infectious 
disease prevention and control.  
2. Led the team to successfully organize and carry out a number of national large-scale scientific 
research tasks such as the national pathogenic spectrum of respiratory infection, serological 
investigation of COVID-19 infection, research, transformation and application of monitoring and 
early warning technology system, and has high academic influence and professional leadership in this 
field. Published over 10 research papers as the first author and corresponding author in top 
international journals such as Lancet and its sub journals, Nature Medicine, etc;  
3. As a core expert, he participated in the organization and formulation of the National COVID-19 
Prevention and Control Plan (Version 1-10), and many research achievements were adopted by the 
national prevention and control technical plan. 
4. He is the leader of the infectious disease discipline at the School of Group Medicine and Public 
Health, Peking Union Medical College, Chinese Academy of Medical Sciences. Based on domestic 
research and teaching bases, the applicant is conducting innovative research on the monitoring, early 
warning, and prevention technology system for sudden and new respiratory infectious diseases. The 
applicant will further improve the construction of the college's academic system, strengthen the 
interdisciplinary integration model of "group medicine+", effectively promote the development of 
disciplines, cultivate a group of high-level scientific research talents who can play a decisive role in 
responding to public health emergencies, and make positive contributions to the construction of 
"Double First Class" disciplines and high-level public health colleges in the School of Group 
Medicine and Public Health. 

27 
 
XiaoYou Su 
No. 31, Beijige Santiao, Dongcheng District, Beijing, 100730 
Mobile: +86-13520029120 
Email: suxiaoyou@pumc.edu.cn 
Educational Background 
2005-2010 
PhD 
Social Medicine, The Chinese University of Hong Kong, Hong 
Kong, China 
1995-1998 
Master 
Environmental Health, Shandong Medical University, Shandong, 
China 
1990-1995 
Bachelor 
Preventive Medicine, Shandong Medical University, Shandong, 
China 
 
Professional Experience 
2020.7-present 
Professor, Department of Global Health, School of Population Medicine and 
Public Health, Peking Union Medical College 
2016.4-2020.7 
Associate Professor, Department of Epidemiology and Statistics, School of 
Public Health, Peking Union Medical College 
2016.4-2017.12 
Research Associate, Department of Epidemiology and Biostatistics, University 
of California, San Francisco  
2012.7-2016.4 
Project Officer, Department of International Cooperation, Chinese Academy 
of Medical Sciences & Peking Union Medical College  
2005.6-2009.9 
Research Coordinator, Department of Epidemiology and Statistics, School of 
Public Health and Primary Care, The Chinese University of Hong Kong  
2009.11-2010.12 Research Coordinator, Department of Surgery, The Chinese University of 
Hong Kong  
2005.6-2009.11 
Research Coordinator, Department of Epidemiology and Statistics, School of 
Public Health and Primary Care, The Chinese University of Hong Kong 
2003.11-2005.7 
AIDS Program Officer, CIPRA Program (sponsored by NIH), China CDC 
2000.8-2001.8 
Research Fellow, Muhimbili National Hospital, Tanzania  
1998.9-2003.11 
Research Fellow-AIDS Department, China Academy of Traditional Medicine 
TEACHING 
Postgraduate Program in Global Health Essentials, course convener, School of Public Health and 
School of Population Medicine and Public Health, Peking Union Medical College, 2014-present. The 

28 
 
course was rated as one of the top-quality postgraduate courses of PUMC in 2022 
Postgraduate Program in Introduction to Diplomacy, course convener, School of Population 
Medicine and Public Health, Peking Union Medical College, 2021-present 
Postgraduate Program in Global Health Security, School of Population Medicine and Public Health, 
Peking Union Medical College, 2021-present 
Postgraduate Program in Ethics of Public Health, School of Population Medicine and Public Health, 
Peking Union Medical College, 2023-present 
Undergraduate Program in Social Medicine, School of Nursing, Peking Union Medical College, 
2021-present 
Postgraduate Program in Epidemiology and Biostatistics, School of Public Health and Primary Care, 
Chinese University of Hong Kong, Hong Kong, 2009 – 2010 
Supervision of 11 graduate students currently, Outstanding Teacher of Peking Union Medical 
College in 2023 
 
GRANTS 
Social environment survey among people living with HIV in China, sponsored by UNAIDS, ongoing 
Technical and strategic study on eradication of cervical cancer in China, sponsored by Chinese 
Academy of Medical Sciences, ongoing 
EEG and mental health screening among various population, sponsored by BrainCo, ongoing 
School Bullying and mental health among boarding school students in China, sponsored by National 
Center for Mental Health, China, ongoing  
 
SOCIAL SERVICES 
Member, Global Health Branch, Chinese Preventive Medicine Association 
Member, Cancer Prevention and Control Branch, Chinese Preventive Medicine Association 
Member, Chinese Association for Student Nutrition & Health Promotion 
Member, International Medical Services Branch, Chinese Hospital Association 
Member, Mental Health Branch, National Health and Promotion Expert Database 
Member, Chronic Diseases Branch, National Terminology Committee of Public Health and 
Preventive Medicine 
 
BOOKS 
Chen Wang, Xiaoyou Su, Hongchi Wei. The Oxford Handbook of Global Health Politics. Peking 
Union Medical College Press, 2024, ISBN 9787567922075 (Chief Translator) 
Chen Wang, Weizhong Yang. Population Medicine. Peking Union Medical College Press, 2022, 
ISBN  9787567915503 (Translator) 
Chen Wang, Weizhong Yang. How to practise Population Medicine. Peking Union Medical College 
Press, 2022, ISBN 9787567919136 (Translator) 
Xiaohui Liang, Zongfu Mao. Global Health Case Studies. China Science Publishing & Median LTD. 
2019, ISBN 9787030597847 (Editor) 
Minghui Ren, Shenglan Tang, Yuanli Liu. An Introduction to Global Health. People’s Medical 

29 
 
Publishing House Co. LTD, 2016, ISBN 9787117220606 (Editor) 
 
SELECTED PUBLICATIONS  
1. Liu X, Wu YJ, Su XY§, et al. COVID-19 Vaccine Hesitancy Among People Living with HIV: A 
Systematic Review and Meta-Analysis. AIDS and Behavior. 2024. 
2. Dai ZW, Wu YJ, Su XY§, et al. Characteristics and Influencing Factors of Anticipated HIV 
Stigma among HIV-negative/unknown MSM in China: a Regression Mixture Model. Brain and 
Behavior 2024;14(4):e3472. 
3. Wu YJ, Dai ZW, Su XY§, et al. Prevalence and influencing factors of PTSD symptoms among 
healthcare workers: A multicenter cross-sectional study during the surge period of the COVID-19 
pandemic since December 2022 in the Chinese mainland. Journal of Affective Disorders 
2024;348:70-77. 
4. Xiao WJ, Liu XY, Su XY§, et al. Prevalence and risk for symptoms of PTSD among survivors of 
a COVID-19 infection. Psychiatry Research 2023;326:115304. 
5. Huang YM, Zhang L, Su XY§, et al. COVID-19 Vaccine Hesitancy Among Patients Recovered 
From COVID-19 Infection in Wuhan, China: Cross-Sectional Questionnaire Study. JMIR Public 
Health and Surveillance 2023;9:e42958. 
6. Zhang L, Wu YJ, Su XY§, et al. The second dose of COVID-19 vaccine booster hesitancy among 
health care workers in China: A multicenter cross-sectional study. American Journal of Infection 
Control 2023;52(5):525-532. 
7. Chen X, Dai ZW, Su XY§, et al. Suicidal ideation and associated risk factors among COVID-19 
patients who recovered from the first wave of the pandemic in Wuhan, China. Qjm-an 
International Journal of Medicine 2023;hcad083. 
8. Dai ZW, Si MY, Su XY§, et al. Depressive symptoms, perceived social support, and anticipated 
HIV stigma among HIV-negative/unknown men who have sex with men in China during the 
COVID-19 pandemic: A multicenter online cross-sectional study. Brain and Behavior 
2023;13(4):e2946. 
9. Jing S, Su XY§, Wang C et al. Prevalence and influencing factors of depressive and anxiety 
symptoms among hospital-based healthcare workers during the surge period of the COVID-19 
pandemic in the Chinese mainland: a multicenter cross-sectional study. QJM-An International 
Journal of Medicine 2023;116(11):911-922. 
10. Fu JQ, Chen X, Su XY§, et al. HIV-related stigma, depression and suicidal ideation among HIV-
positive MSM in China: a moderated mediation model. BMC Public Health 2023;23(1):2117. 
11. Liu XY, Xiao WJ, Su XY§, et al. Prevalence and influencing factors of PTSD symptoms among 
the general population during the surge period of COVID-19 pandemic in mainland China. 
General Hospital Psychiatry 2023. 
12. Xiao WJ, Liu XY, Su XY§, et al. Mediating role of resilience in the relationship between COVID-
19 related stigma and mental health among COVID-19 survivors: a cross-sectional study. 
Infectious Diseases of Poverty 2023;12(1):27. 
13. Dai ZW, Si MY, Su XY§, et al. Willingness to human papillomavirus (HPV) vaccination and 
influencing factors among male and female university students in China. Journal of medical 
virology 2022;94:2776-2786. 
 

30 
 
9. Team Capability 
 Narrative Description of Project Team 
This project will be executed by a multidisciplinary team including public health, epidemiology, IT, and clinical medicine. This project is proposed 
by Dr. Ye Wang, who is a young and vigorous investigator in PUMC. He gained a PhD degree in epidemiology in 2021 and has been engaging in 
respiratory diseases for the past four years. The project is co-supervised by Prof. Zhongjie Li, who is an outstanding practitioner in disease control 
and public health. We also work with Yichang CDC, which provides the field for this project. The AI-assisted CXR system will be provided by JF 
Healthcare Co., Ltd. The project team is also composed of staff from the CDC, primary healthcare institutions, and students. 
 Information of Key Members 
 
Name 
Age 
Highest 
Degree 
Institution 
Professional 
Title 
Specialty 
Role and 
Responsibility in the 
Project 
% of Work 
Time on this 
Project 
Signature 
PI 
Ye Wang 
31 
Doctoral 
degree 
PUMC 
Assistant 
Professor 
Epidemiology 
Conceptualize the study, 
fieldwork, data analysis, 
and report 
50% 
 
Co-PI 
Wei Liu 
46 
Doctoral 
degree 
JF Healthcare 
Co.Ltd 
Chief Medical 
Officer 
Pathogenic 
Microbiology 
Conceptualize the study, 
fieldwork, data analysis, 
and report 
40% 
 
Co-PI 
Zhongjie Li 
47 
Doctoral 
degree 
PUMC 
Professor 
Infectious Diseases 
Control 
Conceptualize the study, 
fieldwork, data analysis, 
and report 
40% 
 
Co-PI 
Xiaoyou Su 
50 
Doctoral 
degree 
PUMC 
Professor 
Social Medicine 
Conceptualize the study, 
fieldwork, data analysis, 
and report 
40% 
 
Senior 
Consultant Wenbiao Hu 
60 
Doctoral 
degree 
The University of 
Queensland 
Professor 
Infectious Diseases 
Study design and 
consulting 
10% 
 
Senior 
Consultant Xiaolin Wei 
50 
Doctoral 
degree 
University of 
Toronto 
Professor 
Implementation 
Science 
Study design and 
consulting 
10% 
 

31 
 
Team 
member 
Jianhua Liu 
42 
Doctoral 
degree 
Yi Chang CDC 
Professor 
Disease Control 
Fieldwork, data 
analysis, and report 
30% 
 
Team 
member 
Li Zhang 
34 
Doctoral 
degree 
PUMC 
Assistant 
Professor 
Epidemiology 
Fieldwork, data 
analysis, and report 
30% 
 
Team 
member 
Jian Li 
41 
Doctoral 
degree 
JF Healthcare 
Co.Ltd 
— 
Artificial Intelligence 
Fieldwork, data 
analysis, and report  
30% 
 
Team 
member Zhengwei Dai 
25 
Master's 
degree 
Peking University 
Sixth Hospital 
— 
Public Health 
Fieldwork, data 
analysis, and report 
20% 
 
Team 
member 
You Xin 
23 
Bachelor 
degree 
PUMC 
— 
Public Health 
Fieldwork, data 
analysis, and report 
20% 
 
Team 
member 
Wenjie Jiang 
23 
Bachelor 
degree 
PUMC 
— 
Public Health 
Fieldwork, data 
analysis, and report 
20% 
 
Team 
member 
Xuya Yu 
32 
Master's 
degree 
PUMC 
— 
Public Health 
Fieldwork, data 
analysis, and report 
20% 
 
Team 
member 
Ling Xin 
23 
Bachelor 
degree 
PUMC 
— 
Population Medicine 
Fieldwork, data 
analysis, and report 
20%