Detection of SARS-CoV-2 in Nasopharyngeal Swabs by Using Multi-Spectral Screening System
The Reliability of the Computer Aided Multi-Spectral Screening System In the Diagnosis of Covid-19
研究概览
详细说明
The primary purpose of this study is to test whether artificial intelligence (AI) will identify existing SARS-COV-2 in nasopharyngeal swab samples using multi-spectral screening technology..
Multi spectral screening testing device called AP-23 offers a non-invasive system for the diagnosis of SARS-COV-2 as a solution. This approach makes multi-spectral diagnostic methods suitable for use by any end user and allows the establishment of Internet of Things systems.
COVID-19 contagion, which began in the northern hemisphere, continues to affect human health and the world economy in tremendously. Early diagnosis of the disease and, accordingly, the breaking of the transmission chain through filiation studies is very important for public health until an effective and safe vaccine is found. PCR tests, which we currently use as the gold standard to prove the existence of the virus, have not been sufficient to prevent the pandemic for the following reasons;
- PCR tests should be performed in a hospital or clinical setting
- There is a need for a custom kit
- There is a need for trained individuals to perform the test
- Standardization is not ensured for the receipt of test sample
- With at least 30 minutes for receipt of the test results, it takes up to 3-day
- testing is expensive and is usually applied to people who have symptoms and thus asymptomatic carriers are missed.
The primary purpose of this study is to test whether SARS-COV-2 can be detected in nasopharyngeal swab samples using multi-spectral screening technology. Multi-spectral techniques are based on phenomena related to absorption, excitation and propagation of biomolecules. In short, by irradiating the sample with electromagnetic energy, some molecules absorb and re-emit less energetic radiation. This phenomenon is called radiation, and the radiated spectrum is a function of certain molecules that combine microorganisms / viruses.
At this point, FableCorp's AP-23 system uses data from a minimally invasive or non-invasive simple measurement based on multi-spectral screening technology to calculate the presence or concentration of the desired product in a biological liquid as the solution to be used.
The most unique approach of the FableCorp system, is to use the A.I. to detect the desired biochemical / cell / virus is to scan appropriate multi-spectral inputs. Data mining tools, where it simultaneously eliminates noise in raw data generated from various multi-spectral spectroscopy inputs, give very clear results. This approach makes multi-spectral technology suitable for use as point-of-care systems for any end user, and also leads to the realization of full automation (Internet of Things) systems.
A.I. the base solution for Pointer signal detection / processing eliminates biochemical additives (kits) and specialized personnel.
AI's evaluation of data takes place within 15-30 seconds. If the effectiveness of this application is proven in the diagnosis of SARS-COV-2, many more people will be able to be screened in a much faster time, much cheaper, and filiation will be applied to the necessary people much faster.
Study Design Stage 1: Nasopharyngeal samples taken from COVID-19 suspected individuals will be given to artificial intelligence to learn positive and negative cases, and the learning results will be calculated as the learning accuracy for negative and positive samples. (n=4000, 2000 positive and 2000 negative)
Stage 1, Output Parameters (Evaluation of Learning Performance of Artificial Intelligence):
PCR results and AP-23 results obtained as a result of comparison; PCR Accordance, PCR Accordance for Negative Samples, PCR Accordance for Positive Samples.
Stage 2: Based on the Artificial Intelligence Learning accuracy rate second stage of the study will be initiated. At this stage, Nasopharyngeal swab samples will be tested by PCR, AP-23, and the results will be tested and be compared and the sensitivity of detection compared with results obtained by (n=400, 200 positive and 200 negative)
Stage 2, Output Parameters (Evaluation of Test Performance of Artificial Intelligence):
Nasopharyngeal swab obtained by comparison with PCR and AP-23 results in samples; Sensitivity, Specificity, Negative Prediction Value, Positive Prediction Value.
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:Deniz Unver, MSc.
- 电话号码:+905380246808
- 邮箱:deniz.unver@fablecorp.com
研究联系人备份
- 姓名:Emre Günerken
- 电话号码:905360300713
- 邮箱:emre.gunerken@fablecorp.com
学习地点
-
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Istanbul
-
Sancaktepe、Istanbul、火鸡、34785
- 招聘中
- Sancaktepe Şehit Prof.Dr. İlhan Varank Training and Research Hospital
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接触:
- İsmail Tayfur, MD
-
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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:
All people who applied to hospitals with suspicion of COVID-19
Exclusion Criteria:
Person who cannot give nasopharyngeal samples
学习计划
研究是如何设计的?
设计细节
- 主要用途:诊断
- 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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其他:Nasopharyngeal swabs
Nasopharyngeal swabs samples of volunteers who is referred with suspicion of Covid19.
|
AP-23 newly developed point of care system as a well-automated combination of multi-spectral technology and a distributed cloud computing A.I. system which has been developed to detect COVID-19.
|
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 |
|---|---|---|
|
Assessment of AI's Learning Performance:
大体时间:2 MONTHS
|
Compared PCR results with AP-23 results; PCR Accordance, PCR Accordance for Negative Samples, PCR Accordance for Positive Samples.
|
2 MONTHS
|
|
Evaluation of Artificial Intelligence's Test Performance)
大体时间:1 MONTH
|
As a result of comparison with the PCR and AP-23 results in nasopharyngeal swab samples; Sensitiviy, Specificity, Negative Prediction Value, Positive Prediction Value
|
1 MONTH
|
合作者和调查者
调查人员
- 首席研究员:İsmail Tayfur, MD、Sancaktepe Şehit Prof.Dr. İlhan Varank Training and Research Hospital
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
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