Artificial Intelligence (AI) Employed to Improve Rapid Diagnostic Test Result Interpretation

on June 9, 2022

Serological rapid diagnostic tests (RDTs) are widely used across pathologies due to their ability to provide users with a simple, binary result (positive or negative). This type of test has first been developed in the early 70s and has mostly been used to determine pregnancy status for more than three decades.

Over the years, the applications of RDTs have expanded considerably, and nowadays we have tests for a variety of pathogens and analytes, making them invaluable tools for diagnosis and health management. However, reading and processing test results manually used to come with some limitations, which are currently being overcome through the use of AI and machine learning.

What are serological rapid diagnostic tests and how do they work?

Serological rapid tests detect antibodies in human fluid samples, producing a binary test result (positive or negative) in a relatively short time (5 to 20 minutes). They can be used to diagnose an active or previous infection or to identify autoimmune disorders.

Rapid tests are diagnostic assays adapted for use in low-resource settings or designed for use at the point-of-care (PoC). They are stable at high temperatures, simple to operate and read, and inexpensive.

Rapid tests or immunoassays detect disease using the principle of antigen-antibody reaction and can determine both the presence and concentration of an analyte. In other words, specific antigens stimulate unique immune responses, and the proteins produced by the immune response, called antibodies, indicate the presence of a target compound in a sample.

The most common type of rapid tests is the lateral flow device, a paper-based platform which works by applying a fluid sample at the end of the test strip and waiting for the sample to flow due to capillary action to the other end of the strip or the absorbent pad. The liquid sample contains an analyte which binds to the capture reagents of conjugates and on the membrane. The conjugate release pad contains antibodies specific to the target analyte; the sample, together with the conjugated antibody bound to the target analyte, migrates along the strip into the detection zone. This area contains specific antibodies or antigens immobilized in lines and which react with the analyte bound to the conjugated antibody.

Recognition of the sample analyte produces a response on the test line, while the response of the control line indicates the liquid has flown properly through the strip.

The test line works differently in sandwich assays and competitive assays. In the first type of assay, described above, the darker the test line, the higher the concentration of the analyte. In competitive assays, the absence of the test line is an indicator of analyte presence.

Limitations that existed before in rapid tests

The simplicity of rapid diagnostic tests does not mean, however, that results are easy to read or reliable. This is happening for several reasons:

  • The aspect of the test lines can differ from one brand and/or test model to others;
  • Low antibody levels can produce test lines that are not easily distinguished, causing positive tests to be read as negative;
  • Subjectivity of the reader and vision limitations can also impact the reading of the test result;
  • PoC tests such as lateral flow immuno-assays can be performed with minimal training and equipment but are less sensitive compared to tests conducted in reference labs;
  • Data regarding test results could only be collected and processed manually, a task which is time-consuming and susceptible to human error.

In short, two major improvements were necessary: finding a way to eliminate subjectivity and human reader limitations from test result reading and collecting and processing test result data more effectively. The adoption of digital readers combined with artificial intelligence software has changed the way rapid diagnostic test results are being processed, leading to rapid and personalized responses to diagnosis and opening a new horizon for research and scientific discovery.

Improvements made to rapid diagnostic test result interpretation

The two improvement directions needed to maximize the value of RDTs have been developed by implementing the following advanced technologies:

Reader apps

Smartphone apps for reading rapid test results use machine learning to classify serological test results and reduce reading ambiguities. Such apps yield more than 99% precision, compared to reading by eye, which produces an accuracy of a little over 90%. Additionally, reading apps enable traceability to provide a clearer diagnosis. The use of reader apps increases the confidence of clinical staff and creates opportunities for more accurate patient self-testing.
Because the intensity of the test line depends on the quantity of antibodies present in the sample, smartphone apps combine the high-resolution imaging abilities of the smartphone’s camera with image treatment to read and interpret rapid diagnostic test results.

Reader devices

Lateral Flow readers like the ones produced by Qassay® are another effective, less costly solution for reading rapid test results correctly. For example our Multi-use (LFT) reader, supplied in kit form with multiple disposable test strips.

Qassay® (by P4Q) Rapid, Digital Lateral Flow Test Readers are based on High-Precision Multi-Spectral Sensor Technology, highly accessible, supporting medical diagnostics , enabling them to evaluate any lateral flow strip test with higher accuracy, in terms of quantitative and qualitative analysis.

Storing test results into the cloud

Whether test results are being read using a smartphone app or a dedicated reader device, the raw data is automatically synchronized and stored in the cloud. Cloud infrastructure provides secure storage and access to data, and the use of proprietary cloud platforms simplifies regulatory implications for users.

Qassay® uses the trust-test cloud platform- development of its own website ( – based on AWS., which offers test data classification and analyses with machine learning capabilities. Our in-house data security (security of data in transit and at rest) comes with multiple data management options, using the latest encryption methods to ensure optimum security and patient privacy at the highest standards. Additionally, this allows us to have third-party auditors assess the security and compliance of the services as part of various AWS compliance programs, such as SOC, PCI, FedRAMP, and HIPAA.
The use of different algorithms makes it possible to obtain precise and reliable diagnoses. Next, the platform offers test mapping capabilities to help with prevalence map analysis procedures, leading to quick reactions to epidemic threats. Local leaders are empowered to make decisions to keep the population safe, while sharing data securely across a global network of organizations.

Using AI to process test results

As seen in the previous section, the next logical step after collecting and storing data is to use it for analysis to make better decisions. The last years have brought an increased focus on the role of artificial intelligence and big data in diagnostics. The development of data management in the lateral flow test market has implications for human health but also for animal health and welfare and for agricultural and environmental applications.

Lateral flow test results can be collected anywhere, and the information is communicated to a central source for further processing and analysis using AI. One of the areas benefiting from AI in test result analysis is the connected farm. Cloud-based data management can allow real-time monitoring of disease outbreaks, making it possible to take preventative measures to restrict the spread of the outbreak. Combined with other factors such as temperature and humidity, test result information can even be used to create early warning mechanisms to prevent future outbreaks.

Through the use of cloud storage and AI data management, lateral flow diagnostic tests become more than standalone devices and are integrated within a data capture solution with higher applicability across the diagnostics market. Replacing traditional databases with cloud data storage makes data reporting procedures faster and more accurate, helping to prevent the spread of disease. Medical experts and anyone with a role in managing disease can have access to real-time data instead of waiting for data to come from different areas and be uploaded.

The use of AI in conjunction with rapid tests during the Covid-19 pandemic

The rapid evolution of the COVID-19 pandemic has been a factor in developing new rapid diagnostic tests for identifying SARS-CoV-2. As of June 2020, more than 176 SARS-CoV-2 serological RDTs had been developed. These rapid diagnostic tests have been used in health facilities and in drive-through testing centers, which has made it possible to test large populations with minimal training and to collect data to make shutdown and reopening procedures when necessary.
The utilization of a smartphone app (xRCovid) that uses machine learning to classify SARS-CoV-2 serological RDT results and reduce reading ambiguities has led to a 99.3% precision across 11 Covid-19 rapid diagnosis test models, also enabling traceability to ensure a clearer diagnosis.

How AI can help to identify HIV-positive tests more accurately

AI is also being used to make improvements in HIV testing around the world by reducing the risk of false positives and negatives. Self-testing increases the number of tests, but those interpreting test results can make errors due to vision limitations and subjectivity.

Academics from the London Centre for Nanotechnology at UCL and AHRI used deep learning (artificial intelligence/AI) algorithms to improve health workers’ ability to diagnose HIV using lateral flow tests in rural South Africa. Their findings, published in Nature Medicine, reflect the results of the first and largest study of field-acquired HIV test results, which have applied machine learning to help classify them as positive or negative.

An app was provided to participants in the test, and they were able to use it without training. The five participants had to record their interpretation of 40 HIV test results and provide a picture of the tests to be read by a machine learning classifier. It was observed that interpreting test results by eye yielded an accuracy of 92.1%, while the machine learning classifier had a superior performance, reading 98.9% of test results correctly. More accurate HIV self-testing can improve patients’ access to prevention and treatment measures.

Dr Kobus Herbst, AHRI’s Population Science Faculty lead, showed that the use of AI in HIV testing puts emphasis on local health priorities and needs: “A digital system that connects a test result and the person to healthcare, including linkage to antiretroviral therapy and pre-exposure prophylaxis, has the potential to decentralize HIV prevention and deliver on UNAIDS goals to eliminate HIV.”

Implications of using AI to improve test result interpretation

Rapid diagnostic technology combined with artificial intelligence and machine learning represent the start of a new era in diagnostics and outbreak management by ensuring the following benefits:

  • Eliminating human error and subjectivity from test results to ensure impartial test analysis;
  • Allowing for predictive values of each test as the test results are updated in real time;
  • Preventing supply shortages by using predictive analysis to determine the future need for critical equipment and reduce waste in the supply chain;
  • Producing live disease maps by offering access to test results to health authorities using anonymized location data;
  • Benefiting patients from low- and middle-income settings by providing affordable and accurate rapid diagnostic tests;
  • Making other types of tests, such as agglutination tests, more suitable for self-testing through the use of deep-learning algorithms that can read more accurately blood spots with and without apparent agglutination;
  • Managing more efficiently large amounts of patient information, streamlining workplace practices and sharing information safely and with unprecedented ease;
  • Accomplishing the de-identification of patient data in order to ensure the privacy. De-identification is the process of removing identifying information from patient data and is critical for sharing health information with third parties for research purposes and use in advanced analytics and machine learning models. Moreover, data can be safely re-identified when needed, allowing researchers to effectively recruit for public health programs and accelerate discovery.
  • Potentially making it possible to identify viruses in seconds.

Regarding to the last benefit, an Oxford university spin out company is currently developing tech to identify viruses in seconds. As of February 2022, OxDX had raised £2.6m in pre-seed funding for its AI powered diagnostic technology. The new method is expected to recognize and identify specific species and strains of viruses, bacteria and other pathogens in a sample within seconds. Initial partners of this project are IQ Capital and Ahren Innovation Capital, with participation from Science Creates Ventures to expand the team and attract additional development partners in the future.

Alex Batchelor, chief executive officer of OxDX, declared: “We’ll start with respiratory viruses and expand from there. In parallel, we’ll be simplifying the workflow to move the test from the lab to point of care, which will represent a step change in the availability and cost of infectious disease diagnostics worldwide.”

The use of new technologies designed to read and store the results of rapid diagnostic tests, such as reading devices, cloud storage, and AI-powered analysis maximizes the potential of lateral flow tests and other rapid, self-assessed tests. As a result, health experts and persons administering rapid tests enjoy advantages such as increased accuracy, predictive value, and more effective outbreak management.

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quassay-adminArtificial Intelligence (AI) Employed to Improve Rapid Diagnostic Test Result Interpretation

Lateral Flow Test Reader Benefits and Fields of Application

on March 30, 2022

The usage of lateral flow tests for various applications has increased significantly in the last years. This surge of interest has created a need for switching from qualitative test result interpretation by a human to quantitative reading that can generate workable data.

Automated lateral test readers represent a significant step ahead in processing test results optimally with the purpose of making better healthcare or environmental decisions.

What are lateral flow readers?

Lateral flow readers (LFRs) are devices for reading and interpreting the results of lateral flow tests or lateral flow assays (LFA)/immunoassays (LFIA). These tests are used to confirm or exclude the presence of a target analyte. The analyte can be a pathogen or a biomarker, either human or animal, or a contaminant.
Lateral flow readers were developed in the mid 1990’s in tandem with advances in digital photography. They became commercially available in the early 2000s and slowly paved the way for today’s small, handheld readers employed for individual point-of-use lateral flow tests.

Lateral flow assays (LFAs) have experienced a new rise in popularity since the Covid-19 pandemic. This type of test had been widely used before for pregnancy tests, which already have a history of more than three decades. Lately, the increase in lateral flow assay testing in various fields has generated a need for more accurate, quantitative reading techniques that integrate reliable data storage and transmission methods. For instance, manufacturers are joining forces to improve reading accuracy by introducing spectral sensors lateral flow readers to raise standards in the point of care industry (Qassay and OSRAM).

How does a lateral flow reader work?

Lateral Flow Reader technology is evolving at a rapid pace. Today’s LTRs are highly flexible, versatile and customizable, easy to read, and can use any lateral flow assay.

Lateral flow assays use three elements to produce results: a nitrocellulose membrane, colored nanoparticles, and antibodies.

When a sample is added to the test, it flows along the test device onto an absorbent pad, passing through a conjugate pad. Some pads contain a filter to ensure a controlled flow of the sample.
The conjugate pad includes conjugated labels and antibodies which bind to the target if present, while continuing to migrate along the test.

The intensity of the colored line indicating the test result depends on the quantity of the present target. A lateral flow assay can yield quantitative results when the test is combined with a reader, thus determining target concentration. A LFA without a reader will be able to offer qualitative results only (whether the agent is present or not); assessing the colored line intensity by a human reader carries a large degree of subjectivity.

In other words, lateral flow assays alone are intended to operate on a purely qualitative basis. Readers, on the other hand, enable the use of quantitative tests, offering additional features that enhance the results of the test, with the two most important being:

  • Determining how much analyte is present in the sample by measuring the intensity of the test line. Readers employ image processing algorithms that are specially designed for a particular test type and medium;
  • Collecting and sending test results data to third parties, where it can be processed and used to diagnose in a faster and more accurate manner.

What are the benefits of lateral flow tests?

Error control
LFAs include a control line to confirm the test is working properly and one or more test lines. The presence of the control line indicates that the result is reliable, thus eliminating errors.

Intuitive use
Lateral flow tests have intuitive and easy to use protocols, which means that anyone can operate them with minimal training. The reading is conducted visually by the person administering the test or with assistance from a reader technology device.

Can be used in various settings
LFAs can be performed by anyone from patients to health care professionals, which means they can be administered anywhere: in laboratories, in clinics, at home, on the field.

High accuracy
The results of LFA tests have a similar level of accuracy compared to PCR tests analyzed in lab settings. For instance, for rapid tests used to detect the Covid-19 infection, tests’ accuracy levels can exceed 90%. Lateral flow readers are accurate enough to identify low analyte detection levels.

More affordable than PCR tests
Lateral flow tests are an affordable alternative to PCR tests, guaranteeing a high level of sensitivity and specificity. They have been an essential tool in the fight against Covid-19, as their lower price made them suitable for mass testing or testing in underdeveloped countries.

Lateral flow reader benefits
Lateral flow assays have already been used for a long time. The possibility to enhance them by adding a reader has emerged in the last years bringing major advantages:

Fast reading time
Modern lateral flow readers like those manufactured by Qassay ® have very rapid reading times, as fast as 5 seconds. The use of LFAs for Covid-19 virus detection has been a driver for developing readers that are able to provide faster results. The reason behind this was that virus detection speed has been vital for slowing down the spread of Covid-19.

The latest lateral flow readers are able to collect, store, and communicate data to other sources such as a laboratory information system. Connecting LFR results enables result interpretation centralization, data monitoring, and simultaneous feedback.

Eliminating human error
Lateral flow readers have integrated optical readers which eliminate the risk of misreading the test result. Human readers can interpret results differently due to perception particularities, visual ability, and psychological factors. Machine learning and AI technologies reduce reading ambiguities significantly and provide sophisticated precision. Moreover, automated readers have the capability to process all tests precisely, including those with faint-color test lines.

Lateral flow readers eliminate the manual introduction of results which can be affected by typing errors. A complete traceability of every sample is ensured by storing the images and results of tests.

What types of lateral flow readers are there in the market?

Lateral flow readers come in different types, depending on their format and operating mode. What they all have in common is the use of digital photography and computer algorithms to provide precise and error-free test results:

  • Single use readers – they are disposed of after one use;
  • Multiple-use readers – they can be used multiple times before disposal;
  • Smartphone lateral flow readers – they use familiar technology like reader apps and are most suitable for consumer diagnostics and lifestyle assays;
  • Standalone bench top or portable lateral flow readers – ensure the highest level of control and security;
  • Readers with built-in connectivity (Bluetooth or Wi-Fi) and capacity for adding data management solutions;
  • Absorption/fluorescence models/high-sensitivity fluorescence models – depending on the type of technology used by the assays whose results the device is reading.
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IDELT participates in the full design of an innovative high-precision antigen test

on March 29, 2022

IDELT participates in the full design of an innovative high-precision antigen test

IDELT has been actively involved in all phases of design, prototyping and production of an innovative digital antigen test that is set to transform diagnostic processes using a test strip analysis system.

To develop this device, called QASSAY®, IDELT has collaborated with the company P4Q, a leading original design manufacturer (ODM) of photovoltaic solar tracking controllers and high precision digital diagnostic tests, in the conception of the project with a very definite goal: to conceive, design and develop the most attractive and functional proposal for the outer casing and inner parts of the system. The Basque company has remained in permanent contact with its technical partner to learn first-hand about its preferences and design needs, and has provided its specialized know-how in plastic products to complement the technical solution, accompanying it throughout the process. At

Specifically, IDELT has developed, prototyped, painted and typeset the customizable aesthetic parts, and made the injection molds for the structural interior parts, while P4Q has developed the algorithm, the system, the functionality, the electronics, the software, the app and the rest of the technical components of QASSAY®. All in record time.

The QASSAY® lateral flow reader is an innovative, highly accurate and fast diagnostic testing device that offers a complete test strip analysis system. This solution is capable of performing a complete analysis of data collected from diagnostic tests with machine learning capabilities, and hosting it on an internal secure cloud platform.

The QASSAY® diagnostic device is currently undergoing certification as a step prior to commercialization and distribution in the medical sector. This solution is fully customizable from an aesthetic point of view and can be configured according to the needs of the end customer.


IDELT’s participation in this project is in line with the innovative strategy promoted by the Provincial Council of Gipuzkoa and the Basque Government, which are committed to boosting and diversifying Basque industrial activity towards differential sectors with a future projection.

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quassay-adminIDELT participates in the full design of an innovative high-precision antigen test

P4Q chooses ams OSRAM spectral sensor in their reusable, white-label digitalized lateral flow tests for the diagnostic and point of care industry

on March 14, 2022

News Facts:

  • ams OSRAM’s spectral sensor and LEDs allow the Qassay-Lateral-Flow-Test (LFT)-reader to accurately and objectively evaluate a lateral flow test strip
  • The multi-use reader and smartphone app can be customized and branded for third-party manufacturers of medical equipment and personal healthcare devices
  • The filter technology of ams OSRAM spectral sensors supports digital health by enabling P4Q and their medical device customers to get to market faster with objective, reusable lateral flow test reader products

Premstaetten, Austria (14th March, 2022) – ams OSRAM (SIX: AMS), a global leader in optical solutions, today announced it has supported original design manufacturer (ODM) P4Q in the development of a new production-ready design for a reusable LFT-reader. P4Q’s new Qassay-LFT-reader will enable brand-name manufacturers of medical and diagnostics equipment to get to market faster with easy-to-read and objective LFT reader products based on advanced optical semiconductor technology.

The Qassay-LFT-reader uses an AS7341L spectral sensor from ams OSRAM to read test lines on a standard lateral flow assay or Europium-based fluorescence test. The test strip is illuminated by either a white LW Q38E LED or an SU EBLP51.VA UVA LED, both also from ams OSRAM. The filter technology of the spectral sensor and the LED help make the LFT readers more reliable and easier to objectively read.

“From fertility testing to measuring cholesterol levels, there is a wide range of applications for digital LFTs. P4Q’s Qassay LFT reading solution addresses these use cases, and enables customers to digitize their products using ams OSRAM technology,” says Frederic Valentin, Vice President Sales EMEA at ams OSRAM.

“We chose the AS7341L spectral sensor because of its high sensitivity and accuracy. By combining ams OSRAM optical filter technology with P4Q’s cloud infrastructure-based and artificial intelligence-enhanced reading system, we have produced a highly precise medical diagnostics solution,” says Aitor Alapont, CEO at P4Q Electronics.

The reusable Qassay-LFT-reader supports the latest automated digital healthcare processes. The multi-channel AS7341L optical sensor detects even faint test lines on an LFT strip under the light emitted by a white or UVA LED, producing very reliable test results. The reader can be used with strips for testing a wide variety of conditions and bio-markers, including Covid-19 infection, diabetes, cancer, and fertility status. A Bluetooth® radio in the Qassay device links to the user’s smartphone. The Qassay app controls the reader’s operation, and enables automatic uploading of test results to the cloud.

The Qassay-LFT-reader is supplied as a 130mm x 44mm x 15mm plastic cartridge. A white-label solution for use by third-party companies, the reader can be customized and branded to fit the requirements of individual medical device manufacturers. The smartphone app can also be configured and branded.

Optical filter technology enhances LFT operation

In lateral flow tests, a liquid such as blood or saliva is applied to a test strip, which then flows evenly on to it and causes a biochemical reaction after a short time. The ams OSRAM AS7341L, a highly sensitive spectral sensor, detects very small changes in the color of the test strip caused by the binding of analytes with reagents in the lateral flow test, making the test more objective and easier to read. The multi-channel sensor capability of the AS7341L enables the Qassay-LFT-reader to offer cost-effective detection of multiple analytes.

Press contact:
Helena Schauer
Tel.: +498962133158

The multi-use reader from Qassay with ams OSRAM’s spectral sensor and LEDs can be customized and branded for third-party manufacturers of medical equipment.
Image: Qassay is a P4Q registered brand name

About ams OSRAM

The ams OSRAM Group (SIX: AMS) is a global leader in optical solutions. By adding intelligence to light and passion to innovation, we enrich people’s lives. This is what we mean by Sensing is Life.

With over 110 years of combined history, our core is defined by imagination, deep engineering expertise and the ability to provide global industrial capacity in sensor and light technologies. We create exciting innovations that enable our customers in the consumer, automotive, healthcare and industrial sectors maintain their competitive edge and drive innovation that meaningfully improves the quality of life in terms of health, safety and convenience, while reducing impact on the environment.

Our around 24,000 employees worldwide focus on innovation across sensing, illumination and visualization to make journeys safer, medical diagnosis more accurate and daily moments in communication a richer experience. Our work creates technology for breakthrough applications, which is reflected in over 15,000 patents granted and applied. Headquartered in Premstaetten/Graz (Austria) with a co-headquarters in Munich (Germany), the group achieved over EUR 5 billion revenues in 2021 and is listed as ams-OSRAM AG on the SIX Swiss Exchange (ISIN: AT0000A18XM4).

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ams is a registered trademark of ams-OSRAM AG. In addition many of our products and services are registered or filed trademarks of ams OSRAM Group. All other company or product names mentioned herein may be trademarks or registered trademarks of their respective owners. 

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About P4Q

P4Q is a Spain based company with manufacturing factories in Bilbao (Spain), Albuquerque (USA) and Kunshan (China), leader in electronic systems development for renewable energies, automotive lighting and medical devices, P4Q is transforming energy, transport and health performance with smart technology, data monitoring and on cloud analysis services. For more information, please visit or  and follow us on Twitter and LinkedIn.

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quassay-adminP4Q chooses ams OSRAM spectral sensor in their reusable, white-label digitalized lateral flow tests for the diagnostic and point of care industry