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注册号: Registration number: |
ChiCTR1900021601 |
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最近更新日期: Date of Last Refreshed on: |
2019-02-28 23:05:16 |
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注册时间: Date of Registration: |
2019-02-28 00:00:00 |
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注册号状态: |
预注册 |
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Registration Status: |
Prospective registration |
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注册题目: |
基于深度学习快速识别慢性鼻窦炎伴鼻息肉病理分型的预测模型 |
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Public title: |
A pathological prediction model of chronic sinusitis with nasal polyps based on deep learning |
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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: |
基于深度学习快速识别慢性鼻窦炎伴鼻息肉病理分型的预测模型 |
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Scientific title: |
A pathological prediction model of chronic sinusitis with nasal polyps based on deep learning |
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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: |
邓慧仪 |
研究负责人: |
邓慧仪 |
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Applicant: |
Deng Huiyi |
Study leader: |
Deng Huiyi |
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申请注册联系人电话: Applicant telephone: |
+86 15521127735 |
研究负责人电话:
Study leader's |
+86 15521127735 |
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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: |
deng.huiyi@foxmail.com |
研究负责人电子邮件: Study leader's E-mail: |
deng.huiyi@foxmail.co |
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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: |
广东省广州市天河区天河路600号 |
研究负责人通讯地址: |
广东省广州市天河区天河路600号 |
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Applicant address: |
600 Tianhe Road, Guangzhou, Guangdong, China |
Study leader's address: |
600 Tianhe Road, Guangzhou, Guangdong, 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: |
中山大学附属第三医院 |
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Applicant's institution: |
The Third Affiliated Hospital, Sun Yat-sen University |
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研究负责人所在单位: |
中山大学附属第三医院 |
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Affiliation of the Leader: |
The Third Affiliated Hospital, Sun Yat-sen University |
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是否获伦理委员会批准: |
否 |
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Approved by ethic committee: |
No |
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伦理委员会批件文号: Approved No. of ethic committee: |
伦理委员会批件附件: Approved file of Ethical Committee: |
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批准本研究的伦理委员会名称: |
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Name of the ethic committee: |
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伦理委员会批准日期: Date of approved by ethic committee: |
2013-08-26 00:00:00 | ||
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伦理委员会联系人: |
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Contact Name of the ethic committee: |
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伦理委员会联系地址: |
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Contact Address of the ethic committee: |
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伦理委员会联系人电话: Contact phone of the ethic committee: |
伦理委员会联系人邮箱: Contact email of the ethic committee: |
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研究实施负责(组长)单位: |
中山大学附属第三医院 |
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Primary sponsor: |
The Third Affiliated Hospital, Sun Yat-sen University |
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研究实施负责(组长)单位地址: |
广东省广州市天河区天河路600号 |
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Primary sponsor's address: |
600 Tianhe Road, Guangzhou, Guangdong, 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: |
中山大学附属第三医院临床医学研究专项基金 |
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Source(s) of funding: |
Special fund for clinical medical research of the Third Affiliated Hospital of Sun Yat-sen University |
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研究疾病: |
慢性鼻窦炎伴鼻息肉 |
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Target disease: |
chronic rhinosinusitis with nasal polyps |
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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: |
诊断试验 |
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Study type: |
Diagnostic test |
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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: |
连续入组 |
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Study design: |
Sequential |
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研究目的: |
根据文献报道和我们的前期研究,因为机器学习辅助病理诊断联合鼻息肉精准治疗具有诸多优势,也符合“精准医学”的潮流,我们提出一种基于深度学习开发一种快速识别慢性鼻窦炎伴鼻息肉病理分型的预测模型,能快速、准确地识别慢性鼻窦炎伴鼻息肉的病理分型,为鼻息肉的临床诊断、治疗和预后分析提供实用的鉴别手段,方便各个等级的医院更好地为患者提供精准的治疗方案,保证了针对eCRSwNP的“激素治疗”和neCRSwNP的“大环内酯类抗生素治疗”能在大范围内开展,从而改善鼻息肉的治疗效果、降低手术复发率,又可减少伴发疾病的漏诊率,实现治疗效果最大化,还能降低诊疗成本,有效推广到基层医院,实现医疗水平一体化的成效。本研究若能成功研发,对改善鼻息肉患者的疗效、降低复发率提供一种科学且简便的手段,无论是患者还是医师都能得从中获益,对于eCRSwNP术后诊断、治疗和预后有重要的指导意义。 |
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Objectives of Study: |
According to the literature and our prospective study, there are lots of advantages together with pathological machine-aided learning and individual treatment of nasal polyps, meanwhile, according with the trend of the "Precision Medicine", we put forward a pathological?prediction?model?of chronic sinusitis with nasal polyps based on deep learning which can identify the pathological types of chronic sinusitis and nasal polyp quickly and accurately. It?could?provide?objective?and?reliable?data for diagnosing, treatment and?prognosis, and then provide accurate treatment for patients all around the hospitals.Therefore we could make sure?that?hormone?treatment for eCRSwNP but macrolide?antibiotics treatment for neCRSwNP can be?carried out?widely in hospitals, so as to improve the curative effect, to reduce the postoperative recurrent rate, and maybe to reduce the missed diagnosis rate of concomitant diseases. Besides, it can also maximize?the?therapy?effect, reduce the cost of diagnosis and treatment, promote to the primary hospitals, and achieve medical level integration. If it is successful, it will provide a scientific and simple means to improve the efficacy and reduce the recurrence rate of nasal polyps, and both patients and physicians can benefit from it, which?will be?of guiding?significance?to the?postoperative diagnosis, treatment and prognosis of eCRSwNP. |
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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: |
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Inclusion criteria |
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排除标准: |
3个月内使用过任何形式的抗生素或糖皮质激素、未接受过免疫治疗;后鼻孔息肉、囊性纤维化、原发性纤毛不动症、真菌性鼻炎、变应性鼻炎、鼻内翻性乳头状瘤等其他鼻腔疾病;并发上呼吸道感染、哮喘或其他严重的系统性疾病。 |
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Exclusion criteria: |
Have used any form of antibiotics or glucocorticoids within 3 months, and have not received immunotherapy; Other nasal diseases such as posterior nostril polyp, cystic fibrosis, primary ciliary immobile disease, fungal rhinitis, allergic rhinitis, inverted papilloma, etc.;Complicated with upper respiratory tract infection, asthma or other serious systemic diseases. |
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研究实施时间: Study execute time: |
从 From 2019-05-01 00:00:00至 To 2021-05-01 00:00:00 |
征募观察对象时间: Recruiting time: |
从 From 2019-05-01 00:00:00 至 To 2021-05-01 00:00:00 |
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诊断试验: Diagnostic Tests: |
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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: |
正在进行 Recruiting |
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): |
该研究不是随机临床研究。 |
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Randomization Procedure (please state who generates the random number sequence and by what method): |
The study is not the randomized clinical trail. |
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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: |
公开/Public |
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盲法: |
N/A |
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Blinding: |
N/A |
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试验完成后的统计结果(上传文件): |
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Calculated Results after
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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): |
暂时未定,待试验完成后再确定。 |
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The way of sharing IPD”(include metadata and protocol, If use web-based public database, please provide the url): |
unavailable |
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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: |
我们每个病人均有一份病例记录表(Case Record Form, CRF),并且有专人整理成Excel表。 |
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Data collection and Management (A standard data collection and management system include a CRF and an electronic data capture: |
There is a Case Record Form (CRF) for each patient, and special one compile it into Excel. |
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数据与安全监察委员会: Data and Safety Monitoring Committee: |
暂未确定/Not yet |