Collecting Respiratory Sound Samples From Corona Patients to Extend the Diagnostic Capability of VOQX Electronic Stethoscope to Diagnose COVID-19 Patients
试验速览
- 阶段
- 不适用
- 发起方
- 入组人数
- 200
- 试验地点
- 6
- 主要终点
- Performance outcome
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Interventional
- 分配方式
- Na
- 干预模型
- Single Group
- 主要目的
- Screening
- 盲法
- None
入排标准
- 年龄范围
- 18 Years 至 —(Adult, Older Adult)
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •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
- •Suggestive blood test - CBC
- •Pulse oximetry
排除标准
- •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
结局指标
主要结局
Performance outcome
时间窗: through study completion, an average of 1 year
Use machine learning technologies to identify the above sound patterns and corresponding pathologies
次要结局
未报告次要终点
