Collecting Respiratory Sound Samples From Corona Patients to Extend the Diagnostic Capability of VOQX Electronic Stethoscope to Diagnose COVID-19 Patients
Technological developments in the recent decades has enabled the integration of electronic and digital components in the stethoscope design, in an attempt to improve auditory performance and, moreover, to assist in improving user's diagnostic accuracy by incorporating computerized, digital technologies, artificial intelligence capabilities and deep-learning-based algorithms enhancing these devices.
We believe that these technologies can be used to significantly improve the diagnostic performance in the primary care phase, by means of a sophisticated stethoscope that enables auscultation to sounds and signals typically found in the sub-sound frequency level. Their transformation into the sound range, and the use of artificial intelligence and machine learning techniques to characterize sound patterns that correspond to specific problems or diseases can substantially enhance the physician's or other care giver's performance to the benefit of the patients.
At this stage, the software in development does not purport to make diagnostic decisions, but only to provide information that will enhance decision and diagnosis making process, therefore enable a more accurate and definitive diagnostic decision and perhaps decrease the number of additional diagnostic tests requested.
研究概览
详细说明
Up to 200 patients will participate in an open, prospective and multi-center study.
Patients diagnosed as positive to COVID-19 will be referred to a VOQX examination. All patients will receive detailed explanation about the purpose of the examination, its impact and will provide their consent prior to the examination. The VOQX device output will have no influence on the decision-making process of the physicians and care givers. The VOQX Stethoscope membrane will be put on the patient's chest area in predefined anterior and posterior points. The data collected in the form of breath sound signals in particular infra-sound will be transferred to an external computer and processed by machine learning algorithm developed by the company. The algorithm will seek patterns typical for the diagnosed disease for each corresponding case diagnosed.
研究类型
注册 (预期的)
阶段
- 不适用
联系人和位置
学习联系方式
- 姓名:Hadas Sapir
- 电话号码:972 54 7826543
- 邮箱:shadas@gsap.co.il
学习地点
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Ashkelon、以色列、7830604
- 招聘中
- Barzilai Medical Center
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Hadera、以色列、38100
- 招聘中
- Hille Yaffe Medical Center
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Zrifin、以色列、703000
- 招聘中
- Shamir Medical Center (Assaf Harofah)
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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
描述
Inclusion Criteria:
- Patients over the age of 18 years
- RT-PCR positive for COVID 19
Patients diagnosed with the following pulmonary pathology:
- Pneumonia
- Pulmonary edema
- Bronchitis
- Acute asthmatic attack
- Emphysema
- Or Normal (e.g. asymptomatic patients)
The diagnosis is confirmed if possible, by:
- Anamnesis
- Physical examination
- X-ray
- Suggestive blood test - CBC
- Pulse oximetry
Exclusion Criteria:
- Pregnant women
- Chest malformation
- Unconsciousness
- Subject that need a guardian
- Weigh above 150 Kg.
- Patients with current shortness of breath
- Patients currently assisted by breathing machine such as CPAP or other
学习计划
研究是如何设计的?
设计细节
- 主要用途:放映
- 分配:不适用
- 介入模型:单组作业
- 屏蔽:无(打开标签)
武器和干预
参与者组/臂 |
干预/治疗 |
|---|---|
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实验性的:Open Label
Up to 200 patients will participate in this open study. Before each examination with the study device, data from each patient (Current medical condition, medical history and demographic data) will be inserted to a computer and added to the database of the study for further processing in conjunction with the study device results. The study device electronic stethoscope membrane will be put on the patient's chest area in predefined anterior and posterior points. At the end of each examination the data will be transferred to a computer and stored in the patient's file. Each patient will be requested to attend the examination once. |
Electronic stethoscope
其他名称:
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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 |
|---|---|---|
|
Performance outcome
大体时间:Through study completion, an average of 1 year
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Detection and identification of pulmonary sound signals ranging from infra-sound to auditory sound which are typical to specific pathologies of COVID-19
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Through study completion, an average of 1 year
|
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Performance outcome
大体时间:through study completion, an average of 1 year
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Use machine learning technologies to identify the above sound patterns and corresponding pathologies
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through study completion, an average of 1 year
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合作者和调查者
赞助
调查人员
- 研究主任:David Linhard、Sanolla
研究记录日期
研究主要日期
学习开始 (实际的)
初级完成 (预期的)
研究完成 (预期的)
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.