Machine learning and artificial intelligence bring many advantages to the development and efficacy of clinical diagnostics. Seegene, a molecular biotech company that specializes in diagnostics, has realized these capabilities and is leveraging an artificial intelligence-based big data system, as well as its own high multiplex PCR technologies, to shift the R&D paradigm for diagnostic tests away from manual R&D practices. The goal is to simplify and automate the R&D process so that companies can leverage their AI system to make diagnostics tests a reality and eliminate some of the challenges associated with traditional practices.
Limitations of traditional diagnostics practices
Currently, companies and research institutions are using their own R&D teams to develop assays. This analog approach requires researchers to amass dozens of scientific instruments to be used in each complex step of the R&D process—which can take years of hard work to perfect. In fact, the manual R&D process for a new diagnostic test can be separated into numerous discreet steps and require around 16 platforms and 77 different scientific assays to design and troubleshoot, to ultimately achieve clinical validation for the assay. To complete these steps requires numerous technicians with extensive training and years of experience to run each assay and use each instrument – all to ultimately develop a successful test. When a new diagnostic test is urgently needed, the team must work around the clock to get the job done.
However, as was experienced during the pandemic, we do not always have the luxury of time. The difference between developing a diagnostic test in a few days versus a few weeks can be the difference between life and death. For this reason, some manufacturers are leveraging newly created artificial intelligence-based assay development systems to develop molecular diagnostics assays in as few as four days while minimizing human error and constraints during the R&D process. Automated platforms can run tirelessly through the many steps of assay development, requiring oversight from only a few employees at any given time. Automation also enables users to consolidate the scientific instrumentation that go into R&D down to a single platform and completes all steps of R&D that otherwise would require dozens of separate instruments to run with a degree of precision and accuracy not easily achieved by human hands.
Optimizing data for diagnostics
Creating diagnostic tests ultimately depends on how fast a biotech firm can handle and process a vast amount of data that are related to virus patterns, illnesses and related treatments. The process can be made considerably more manageable with the help of machine learning and AI. By incorporating artificial intelligence, companies can speed up the development process of new diagnostic tests by automating the assay designing phase, ultimately making a positive impact on patient care.
The fundamental power of artificial intelligence lies within the ability to systematically amass, sort and categorize the vast amount of data the technology outputs.
The data to be used by AI is in constant flux. The fundamental power of artificial intelligence lies within the ability to systematically amass, sort and categorize the vast amount of data the technology outputs. In order for the machine to think and process on its own, users need to be able to systematically instill vast amount of information into the data file. This can be done by formulating strategies for platform, software and data and identifying frequent patterns. Meticulous data management plays a significant role in determining the success of artificial intelligence, so consistently building competence for data architecture, data governance and data management is critical.
Diagnostics accuracy
Artificial intelligence technology and machine learning can also be used to improve diagnostics accuracy. As the algorithm goes over multiple layers of genetic components already instilled in the system, and with each new technological advancement, artificial intelligence enables an increasingly accurate analysis of diagnostics. In contrast, without the AI, current diagnostics have to rely on a simpler interpretation of genetic information, thereby potentially reducing the level of accuracy.
In the molecular diagnostics field, automated systems are also used to accurately spot target genes and design oligonucleotide primers, both critical to the development of molecular diagnostic assays. With data compiled from assay development, diagnostics companies can artificially find the most efficient genetic target for developing a specific diagnostic assay.
Identifying the clinical advantages
Because many different pathogens produce overlapping symptoms, there are significant clinical advantages to diagnostic tests that can evaluate more than one disease at a time and ensure a timely diagnosis as well.
Seegene was the first company in the world to develop an artificial intelligence (AI) platform to conduct diagnostic assay R&D, and was able to use it to produce the first-ever syndromic test. Because the process was automated, the development of the test took only four days from start to finish. In contrast, a test developed manually would have taken more than a year.
As COVID-19 was first emerging in early 2020, Seegene leveraged its AI-based assay development platform and proprietary high-multiplex RT-PCR technology to produce one of the first tests kits for COVID-19 infection—and did so in just two weeks. Two weeks after submitting its COVID-19 test to the Korea Centers for Disease Control & Prevention, it received emergency approval. The availability of Seegene’s test kits, along with the government’s rapid response, have been credited in South Korea’s widely praised COVID-19 response.
Such technological advancement not only saves time and cost in testing for the novel coronavirus, but improves overall process efficiency by enabling massive testing, essential in fighting the COVID-19 pandemic.
Machine learning and AI will definitely reshape the world we live in by helping researchers to develop diagnostic tests faster and with heightened accuracy. When that happens, we will be able to prevent not only once-in-a-generation pandemics but also various diseases. By continuing to advance this cutting-edge technology, researchers can improve general clinical diagnostics and even patient treatment and care in the years to come.