利用超声波传感器原理研制乳腺检查辅助工具
利用超声波传感器原理开发乳房检查辅助工具:开发与验证研究
The accuracy of breast examinations and ultrasonography performed clinically to detect breast mass varies greatly depending on the physician's skill level, and the accuracy of breast examinations by non-experts is particularly low. In this study, we aimed to validate whether the concurrent use of ultrasound sensor technology is an efficient strategy for the purpose of improving the sensitivity of detecting breast masses through breast examination.
研究概览
详细说明
[Background] This research team would like to conduct this study based on the idea that the sensitivity of breast palpation can be improved by moving away from traditional breast palpation, which is simply performed by hand, and using auxiliary examination equipment based on ultrasonic sensor technology. In particular, our research team focused on the waveform of the ultrasound itself rather than the visual images obtained through the ultrasound device. In the existing breast ultrasound, the medical staff reads images created through ultrasound from multiple sensors to confirm the possibility of breast cancer, and this is read based on the medical staff's very subjective opinions. However, ultrasonic waveforms acquired through ultrasound can store information about the waveform as data and thus be implemented as objective values.
[Study design] Prospective, multi-institutional
[Study protocol]
① Preoperative ultrasound sensor-based diagnostic equipment was applied to 200 patients with breast mass among patients admitted to the breast surgery department, and prospectively obtained ultrasound echo signal data generated by the mass.
② For this purpose, the researcher uses equipment containing a single ultrasound sensor to manually scan the mass lesion area and no evidence disease area.
③ Diagnostic performance (judgment for presence or absence of a tumor) of diagnostic tool based on ultrasound sensor technology through an artificial intelligence algorithm designed based on ultrasound wavelength and frequency optimized for mass detection.
[Objectives]
- Primary endpoint Sensitivity/specificity/predictive value/accuracy/positive & negative predictive of diagnostic performance
- Secondary endpoint Artificial intelligence algorithm efficacy
研究类型
注册 (实际的)
联系人和位置
学习地点
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Seoul
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Seoul、Seoul、韩国、03080
- Seoul National University Hospital
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参与标准
资格标准
适合学习的年龄
接受健康志愿者
有资格学习的性别
取样方法
研究人群
scheduled for surgery after a tumor has been confirmed on breast ultrasound examination
描述
Inclusion Criteria:
- female patients between 18 and 80 years of age who are scheduled for surgery after a tumor has been confirmed on breast ultrasound examination
Exclusion Criteria:
- Patients diagnosed with breast cancer after biopsy with non-mass enhancement or calcification
- Inflammatory breast cancer
- Patients whose cancer has invaded the skin and broken through
- Patients with skin diseases
- Women who refused to participate in the study
学习计划
研究是如何设计的?
设计细节
- 观测模型:COHORT
- 时间观点:PROSPECTIVE
队列和干预
团体/队列 |
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超声波组
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研究衡量的是什么?
主要结果指标
结果测量 |
措施说明 |
大体时间 |
|---|---|---|
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Device performance
大体时间:Within 1 year after the study participant registration deadline
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Sensitivity/specificity/predictive value/accuracy/positive predictive value/ negative predictive value of diagnostic performance |
Within 1 year after the study participant registration deadline
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次要结果测量
结果测量 |
大体时间 |
|---|---|
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人工智能算法功效
大体时间:研究参与者注册截止日期后 2 年内
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研究参与者注册截止日期后 2 年内
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合作者和调查者
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (实际的)
研究完成 (实际的)
研究注册日期
首次提交
首先提交符合 QC 标准的
首次发布 (实际的)
研究记录更新
最后更新发布 (实际的)
上次提交的符合 QC 标准的更新
最后验证
更多信息
此信息直接从 clinicaltrials.gov 网站检索,没有任何更改。如果您有任何更改、删除或更新研究详细信息的请求,请联系 register@clinicaltrials.gov. clinicaltrials.gov 上实施更改,我们的网站上也会自动更新.