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审核状态: Project audit state: |
通过审核 Successful |
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注册号: Registration number: |
ChiCTR2600130940 |
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最近更新日期: Date of Last Refreshed on: |
2026-08-26 16:02:02 |
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注册时间: Date of Registration: |
2026-08-26 00:00:00 |
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注册号状态: |
补注册 |
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Registration Status: |
Retrospective registration |
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注册题目: |
基于人工智能的CBCT鼻中隔偏曲自动识别系统 |
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Public title: |
Automated Assessment of Nasal Septum Deviation Using CBCT Images Based on Artificial Intelligence |
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注册题目简写: |
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English Acronym: |
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研究课题的正式科学名称: |
基于人工智能的CBCT鼻中隔偏曲自动识别系统 |
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Scientific title: |
Automated Assessment of Nasal Septum Deviation Using CBCT Images Based on Artificial Intelligence |
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研究课题代号(代码): Study subject ID: |
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在二级注册机构或其它机构的注册号: The registration number of the Partner Registry or other register: |
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申请注册联系人: |
刘超 |
研究负责人: |
刘超 |
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Applicant: |
Liu Chao |
Study leader: |
Liu Chao |
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申请注册联系人电话: Applicant telephone: |
+86 21 23271699 |
研究负责人电话:
Study leader's |
+86 21 23271699 |
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申请注册联系人传真 : Applicant Fax: |
研究负责人传真: Study leader's fax: |
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申请注册联系人电子邮件: Applicant E-mail: |
79668635@qq.com |
研究负责人电子邮件: Study leader's E-mail: |
79668635@qq.com |
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申请单位网址(自愿提供): Applicant website(voluntary supply): |
研究负责人网址(自愿提供): Study leader's website(voluntary supply): |
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申请注册联系人通讯地址: |
中国上海市黄浦区制造局路639号 |
研究负责人通讯地址: |
中国上海市黄浦区制造局路639号 |
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Applicant address: |
639 Zhizaoju Road, Huangpu District, Shanghai, China |
Study leader's address: |
639 Zhizaoju Road, Huangpu District, Shanghai, China |
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申请注册联系人邮政编码: Applicant postcode: |
研究负责人邮政编码: Study leader's postcode: |
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申请人所在单位: |
上海交通大学医学院附属第九人民医院 |
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Applicant's institution: |
Shanghai Ninth People’s Hospital, Shanghai Jiao Tong Unive |
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研究负责人所在单位: |
上海交通大学医学院附属第九人民医院 |
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Affiliation of the Leader: |
Shanghai Ninth Peoples Hospital,Shanghai JiaoTong University School of Medicine |
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是否获伦理委员会批准: |
是 |
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Approved by ethic committee: |
Yes |
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伦理委员会批件文号: Approved No. of ethic committee: |
SH9H-2025-T102-2 |
伦理委员会批件附件: Approved file of Ethical Committee: |
查看附件View |
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批准本研究的伦理委员会名称: |
上海交通大学医学院附属第九人民医院研究者发起的临床研究伦理审查专委会 |
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Name of the ethic committee: |
Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine Ethics Committee |
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伦理委员会批准日期: Date of approved by ethic committee: |
2025-04-11 00:00:00 | ||
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伦理委员会联系人: |
甄红 |
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Contact Name of the ethic committee: |
Zhen Hong |
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伦理委员会联系地址: |
中国上海市黄浦区制造局路639号 |
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Contact Address of the ethic committee: |
639 Zhizaoju Road, Huangpu District, Shanghai, China |
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伦理委员会联系人电话: Contact phone of the ethic committee: |
+86 21 23271699 |
伦理委员会联系人邮箱: Contact email of the ethic committee: |
shjyiec@126.com |
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研究实施负责(组长)单位: |
上海交通大学医学院附属第九人民医院 |
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Primary sponsor: |
Shanghai Ninth Peoples Hospital,Shanghai JiaoTong University School of Medicine |
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研究实施负责(组长)单位地址: |
中国上海市黄浦区制造局路639号 |
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Primary sponsor's address: |
639 Zhizaoju Road, Huangpu District, Shanghai, China |
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试验主办单位(项目批准或申办者): Secondary sponsor: |
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经费或物资来源: |
自筹 |
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Source(s) of funding: |
Self-funded |
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研究疾病: |
鼻中隔偏曲 |
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Target disease: |
Nasal Septal Deviation |
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研究疾病代码: |
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Target disease code: |
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研究类型: |
观察性研究 |
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Study type: |
Observational study |
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研究所处阶段: |
探索性研究/预试验 | ||||||||||||||||||||||
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Study phase: |
0 |
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研究设计: |
横断面 |
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Study design: |
Cross-sectional |
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研究目的: |
1. 开发一个深度学习模型,用于分析CBCT中有无NSD的筛查; 2. 测试现有的目标检测算法,实现最精确的NSD-ROIs(region of interest)提取; 3. 测试现有图像分类模型,在兼顾准确性、灵敏度和特异性的同时尽可能降低算力,以开发适合于临床环境中的实际部署的智能筛查系统。 |
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Objectives of Study: |
Develop a deep?learning model for screening the presence or absence of NSD in CBCT images.Test existing object?detection algorithms to achieve the most accurate extraction of NSD?ROIs (regions of interest).Evaluate existing image?classification models to balance accuracy, sensitivity and specificity while minimizing computational overhead, for developing an intelligent screening system suitable for real?world clinical deployment. |
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药物成份或治疗方案详述: |
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Description for medicine or protocol of treatment in detail: |
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纳入标准: |
1. 男女不限,年龄不限; 2. 有全头颅CBCT影像资料。 |
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Inclusion criteria |
1. No restriction on gender or age; 2. Having complete cranial CBCT image data. |
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排除标准: |
1. 鼻外伤史或颌面部外伤史; 2. 颌面部先天发育畸形; 3. 腺样体肥大或扁桃体肥大且未行手术者; 4. 孕妇。 |
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Exclusion criteria: |
1. History of nasal trauma or maxillofacial trauma; 2. Congenital maxillofacial developmental deformities; 3. Adenoid hypertrophy or tonsillar hypertrophy without surgical intervention; 4. Pregnant women. |
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研究实施时间: Study execute time: |
从 From 2025-02-25 00:00:00至 To 2026-02-25 00:00:00 |
征募观察对象时间: Recruiting time: |
从 From 2025-02-25 00:00:00 至 To 2026-02-25 00:00:00 |
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干预措施: Interventions: |
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研究实施地点: Countries of recruitment and research settings: |
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测量指标: Outcomes: |
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采集人体标本:
Collecting sample(s)
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征募研究对象情况: Recruiting status: |
结束 /Completed |
年龄范围: Participant age: |
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性别: |
男女均可 |
Gender: |
Both |
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随机方法(请说明由何人用什么方法产生随机序列): |
无 |
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Randomization Procedure (please state who generates the random number sequence and by what method): |
None |
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是否公开试验完成后的统计结果: Calculated Results after the Study Completed public access: |
不公开/Private |
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盲法: |
无 |
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Blinding: |
None |
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是否共享原始数据: IPD sharing |
是Yes |
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共享原始数据的方式(说明:请填入公开原始数据日期和方式,如采用网络平台,需填该网络平台名称和网址): |
研究结束后半年;国家生物信息中心(https://www.cncb.ac.cn/) |
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The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
Six months after the completion of the research; China National Center for Bioinformation (https://www.cncb.ac.cn/) |
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数据采集和管理(说明:数据采集和管理由两部分组成,一为病例记录表(Case Record Form, CRF),二为电子采集和管理系统(Electronic Data Capture, EDC),如ResMan即为一种基于互联网的EDC: |
1. 数据采集 病例来源:2020 年 9 月 —2023 年 11 月上海九院正畸科门诊就诊患者,依据纳入、排除标准筛选 330 例受试者,收集全头颅 CBCT 影像 DICOM 文件及病历基线信息。 影像标注:专业人员在 CBCT 图像上人工标注鼻中隔感兴趣区(ROI),构建带注释数据集用于 YOLOv11、CNN 模型训练测试。 信息录入:采用电子信息收集表采集受试者资料;监察员核对电子病历原始文件,核对数据一致性。 2. 数据管理 人员分工:设置研究者、监查员、数据管理员并统一培训;数据管理员搭建专用电子数据库,设置逻辑校验程序,自动核查数据缺失、异常、逻辑矛盾,存在疑问发放数据质疑表复核修正。 保密与存储:受试者病历、影像资料院内归档保存;电子数据库由专人管控,仅研究者、伦理委员会、监查、稽查、药监部门可查阅,研究发表不披露受试者身份信息,严格保护患者隐私。 监察上报:每年完成一次项目数据监察报告并提交伦理委员会。 |
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Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
Data CollectionSubjects were screened from orthodontic outpatients of Shanghai Ninth People’s Hospital between September 2020 and November 2023 according to inclusion and exclusion criteria, with a total of 330 participants enrolled. Full-skull CBCT DICOM images and baseline medical records were collected. Professional annotators manually delineated nasal septum ROIs on CBCT images to build annotated datasets for training and testing YOLOv11 and CNN models. Subject information was collected via electronic case report forms, and monitors verified data against original electronic medical records.Data ManagementResearchers, monitors and data managers were assigned and trained. Data managers established a dedicated electronic database with logical validation programs to detect missing, abnormal or inconsistent data; data queries were issued to researchers for correction when discrepancies existed.All medical records and imaging data were archived in the hospital. The electronic database was managed exclusively by designated staff, accessible only to researchers, ethics committee members, monitors, auditors and drug regulatory authorities. No personal identifiable information of subjects will be disclosed in published research results to protect patient privacy. A data monitoring report shall be submitted to the ethics committee annually. |
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数据与安全监察委员会: Data and Safety Monitoring Committee: |
有/Yes |