Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT04786197 |
| Other study ID # |
Kaïssa Covid |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
August 10, 2021 |
| Est. completion date |
December 31, 2023 |
Study information
| Verified date |
April 2023 |
| Source |
Groupe Hospitalier Paris Saint Joseph |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
SARS-CoV-2 infection was identified as responsible for several cases of pneumonia and acute
respiratory distress syndromes described in Wuhan, Hubei Province, China in December 2019. A
global epidemic has spread since and the Director General of the World Health Organization
(WHO) declared in March 2020 the state of a global pandemic.
As the spread of the virus accelerates, several countries are implementing containment
strategies to stem the epidemic.
The context of an influx of patients and congestion in healthcare establishments requires
rapid and reliable diagnostic solutions for SARS-CoV-2 infection in order to enable patients
to be properly referred. These solutions will represent fundamental tools in the management
of new epidemic waves, both in terms of health and economics.
Description:
Spectroscopy is the discipline of studying the interactions between light and matter, in
order to perform analyzes unmatched in terms of the speed of data acquisition. Depending on
the spectral ranges used by the sensors, it is possible to carry out molecular (molecular and
vibrational spectroscopy) or elementary (atomic spectroscopy) analyzes.
As part of this project, GreenTropism has selected Surface Enhanced Raman Scattering (SERS)
technology as a spectral technique. The scientific literature reports several cases of use of
SERS technology for virus analysis, under variable conditions: variable viral loads, after
amplification, use of substrates enriched in antigens.
The SERS allows an analysis of a sample deposited on a substrate on average (from fifteen
seconds to 10 minutes depending on the devices and the presence of complementary imaging).
Already proven for the identification of viruses on strains pathogenic for humans and
animals, its deployment is slowed down by the complexity of the data to be processed.
These spectra acquisition technologies require the joint use of statistical tools and
multivariate analyzes to allow sample discrimination (classification) and / or
quantification. Until recently, the capacity and performance of statistical tools were
limited by the available computational capacities. The lifting of this technological lock
allowed the advent and democratization of Artificial Intelligence (AI) techniques theorized
in the 1960s and applied today.
GreenTropism's Kaïssa, AI tool, in addition to processing big data, has been designed and
trained specifically for processing spectral data and automating all of the algorithmic
chains needed to go from spectrum support to its interpretation, and the presentation of the
final answer.
The analysis of chemometric data, for the purpose of classification, implements several types
of algorithms that Kaïssa uses, combining them automatically to obtain the best possible
analyzes of these data. These algorithms are divided into two large groups: mathematical
preprocessing and classification models.
The combination of photonic technologies (here SERS) and AI allows real-time analyzes of
multiple substrates, without a priori knowledge of the user on the sample and without prior
expertise. These characteristics make it a valuable tool for diagnosing SARS-CoV-2 infection
in the context of Point Of Care.
In a work carried out between the months of March and June 2020, several models showed, on
test databases not integrated in the learning, performance of discrimination between positive
and negative patients for SARS-CoV-2 according to RT-PCR equivalent to Youden indices of 0.6
to 0.92. On the other hand, these models have highlighted a variability in the use of samples
which results in a drop in performance during tests on statistically independent databases
requiring additional spectral acquisitions, leading today to the presentation of this report
project.
