The aim of this research is to use the Multi-Spectral method to measure the biomolecular fingerprint of the virus directly instead of the virus's RNA (RT-PCR) or the body's immune response (antibody) measurement methods to diagnose the SARS-COV-2 virus, thereby determining whether COVID-19 can be diagnosed faster, cheaper and without requiring a medical laboratory environment.

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;

1. PCR tests should be performed in a hospital or clinical setting
2. There is a need for a custom kit
3. There is a need for trained individuals to perform the test
4. Standardization is not ensured for the receipt of test sample
5. With at least 30 minutes for receipt of the test results, it takes up to 3-day
6. 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.

Inclusion Criteria:

All people who applied to hospitals with suspicion of COVID-19

Exclusion Criteria:

Person who cannot give nasopharyngeal samples