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
調査の概要
詳細な説明
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.
研究の種類
入学 (予想される)
段階
- 適用できない
連絡先と場所
研究連絡先
- 名前:Deniz Unver, MSc.
- 電話番号:+905380246808
- メール:deniz.unver@fablecorp.com
研究連絡先のバックアップ
- 名前:Emre Günerken
- 電話番号:905360300713
- メール:emre.gunerken@fablecorp.com
研究場所
-
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Istanbul
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Sancaktepe、Istanbul、七面鳥、34785
- 募集
- Sancaktepe Şehit Prof.Dr. İlhan Varank Training and Research Hospital
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コンタクト:
- İsmail Tayfur, MD
-
-
参加基準
適格基準
就学可能な年齢
健康ボランティアの受け入れ
受講資格のある性別
説明
Inclusion Criteria:
All people who applied to hospitals with suspicion of COVID-19
Exclusion Criteria:
Person who cannot give nasopharyngeal samples
研究計画
研究はどのように設計されていますか?
デザインの詳細
- 主な目的:診断
- 割り当て:なし
- 介入モデル:単一グループの割り当て
- マスキング:なし(オープンラベル)
武器と介入
参加者グループ / アーム |
介入・治療 |
|---|---|
|
他の: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.
|
この研究は何を測定していますか?
主要な結果の測定
結果測定 |
メジャーの説明 |
時間枠 |
|---|---|---|
|
Assessment of AI's Learning Performance:
時間枠:2 MONTHS
|
Compared PCR results with AP-23 results; PCR Accordance, PCR Accordance for Negative Samples, PCR Accordance for Positive Samples.
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2 MONTHS
|
|
Evaluation of Artificial Intelligence's Test Performance)
時間枠: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
|
協力者と研究者
捜査官
- 主任研究者:İsmail Tayfur, MD、Sancaktepe Şehit Prof.Dr. İlhan Varank Training and Research Hospital
研究記録日
主要日程の研究
研究開始 (実際)
一次修了 (予想される)
研究の完了 (予想される)
試験登録日
最初に提出
QC基準を満たした最初の提出物
最初の投稿 (実際)
学習記録の更新
投稿された最後の更新 (実際)
QC基準を満たした最後の更新が送信されました
最終確認日
詳しくは
本研究に関する用語
追加の関連 MeSH 用語
その他の研究ID番号
- SNCKTP_AP-23
医薬品およびデバイス情報、研究文書
米国FDA規制医薬品の研究
米国FDA規制機器製品の研究
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