With the implementation of artificial intelligence (AI), scholars have lately turned to establish models using AI algorithms to realize in vitro diagnostic (IVD) in different fields. As an industry-leading company pursuing the frontier of science, Creative Biolabs has never stopped making progress and has thrown our sight into the application of AI in medicine. After years of effort and practice, we are now capable of offering high-quality AI-based IVD development services to global researchers.

AI-based Tumor Immunotherapy Evaluation

Over the past decade, the role of the immune system in controlling tumorigenesis and tumor progression has been well established. To better fulfill the purpose of screening models as predictive or discovery platforms, clinically useful and commercially feasible engineering approaches must be compatible with high-content or high-throughput settings. Data acquisition and interpretation are challenging, however, significant advancements in AI can support the growing need for big data analytics. Armed with the unique data derived from an array of screening approaches, AI can play an integral role in the pipeline of screening cancer immunotherapies, including predicting targetable epitopes for cancer vaccinations, pairing appropriate immunotherapeutics to responsive patients, and identifying adverse reactions prior to administration.

AI-based Tumor Neoantigens Screening

Neoantigen represents any peptide whose generation is a direct consequence of somatically acquired genetic changes in tumor cells. These changes include single-nucleotide variants, insertions and deletions (indels) that lead to frameshifts, and structural variants. In addition, for virally associated cancers, any expressed open reading frames in the viral genome may also be considered potential sources of neoantigens. The ability of neoantigen therapeutic vaccines to promote tumor-specific T-cell responses has been verified in a number of pre-clinical models. The development of a machine-learning algorithm to predict the immunogenicity of neoantigen peptides (i.e., variant peptides predicted to bind MHC) would be valuable for screening predicted neoantigens for clinical application.

AI-based Biomarker Detection

Deep learning models have recently been applied to automated biomarker detection in immunohistochemically stained tumor images. For example, in the breast tumor, they have been used for automatic scoring of immunohistochemistry (IHC) HER2 and automated estrogen receptor α (ER) scoring. Deep learning for assessment of tumor-associated stroma and the diagnostic importance as a biomarker was recently shown. In addition, deep learning methods have been used to quantify immune cell infiltration, so-called tumor-infiltrating lymphocytes, in H&E stained breast tumor images.

AI-based POCT Development

Point-of-care testing (POCT) is defined as laboratory testing that takes place at or near the site where the patient is located. POCT is a quality-assured pathology service using analytical tools such as blood gas, critical care analyzers, and meters for glucose, urinalysis, and other metabolites. POCT can be particularly useful for noncommunicable disease states requiring continuous monitoring such as diabetes mellitus, hypertension, congestive heart failure, and stroke prevention. POCT gives health care providers faster results, which allows for a shorter time frame for therapeutic intervention. Nowadays, medicinal and therapeutic options are becoming more complex and personalized. To improve patient outcomes, leveraging the best medical care options, and augmenting physicians' knowledge processing, while reducing overall healthcare costs, digital disease management platforms has got more and more attention. As such, there are numerous opportunities for AI/machine learning (ML)-based solutions to enhance and personalize medical practice.

The developmental pathway for engineering mobile point-of-care diagnostics.Fig.1 The developmental pathway for engineering mobile point-of-care diagnostics. (Malekjahani, 2019)

AI and Liquid Biopsy for Disease Research

Liquid biopsy refers to the analysis of rare circulating cells, microvesicles, nucleic acids (in particular, circulating tumor DNA (ctDNA) and exosomes), proteins, and metabolites released in the peripheral blood from the primary tumor and/or metastatic deposits. A liquid biopsy or blood sample can provide the genetic landscape and epigenetic characteristics of all cancerous lesions and offer the opportunity to track genomic evolution systematically. Rapid technical innovation in the field of liquid biopsy is producing microchip-based technologies that are increasingly sensitive and specific, are able to measure increasing numbers of biomarkers, and are miniaturized and clinically deployable. Machine learning encompasses a set of computational techniques widely applied in many fields to reduce large numbers of measurements into lower-dimensional outputs that are more useful. Thus, machine learning applications are important to the development of the emerging liquid biopsy-based microchip technology that extracts increasingly large quantities of molecular data.

AI and Digital Diagnostics Services

The integration of AI will be one of the biggest transformations for medicine in the next decade and histopathology is right at the center of this revolution. Histopathology was highlighted as being ripe for innovation and where modern tools should allow digital images to replace the manual approach based on microscopy. In addition, Histopathology creates opportunities for AI-based algorithms that could provide grading of tumors and prognostic insights that are not currently available through the conventional methodology. Many researchers are aiming to realize the benefits of AI in pathology diagnostics by speeding up diagnosis, improving outcomes, providing better value for money, and allowing clinicians to spend time on other tasks.

AI and IoT in Healthcare

Internet of Things (IoT) is the network of physical objects that contain embedded technology to communicate and sense or interact with their internal states or the external environment. The things in IoT can be anything in the world, not just mobile or wearable devices. In recent years, IoT has evolved as technology including AI. The biomedical signal is acquired from a sensor emitted by digital devices, such as a wearable device. Basically, the signal is digital biomarkers. AI analyzes digital signals in real-time, processes them into meaningful data, and automatically provides a feedback service to patients or medical staff. The application of IoT in the medical field is closely linked to AI and digital biomarkers. Thus, AI and digital biomarker are components that are closely connected with medical IoT. AI can be used to learn and analyze vast amounts of data generated by real-time monitoring.

The concept of the Internet of Things (IoT) system is very similar to that of the human nervous system.Fig.2 The concept of the Internet of Things (IoT) system is very similar to that of the human nervous system. (Nam, 2019)

Services at Creative Biolabs

Committed to AI-based medicine research over years, Creative Biolabs has step-by-step equipped our technology platform with advanced facilities, excellent scientists, and mature technicians. With strong foundations and rich experience, we are capable of providing high-quality AI-based IVD development services to meet global researchers' requirements.

With advantages in various aspects, Creative Biolabs aspires to become the best scientific-technical service provider. We have never stopped making progress to bring our clients better and better services. If you are interested in AI-based IVD development services or you have any other questions about our services, please feel free to contact us for more information.

References

  1. Malekjahani, A.; et al. Engineering steps for mobile point-of-care diagnostic devices. Acc Chem Res. 2019, 52(9): 2406-2414.
  2. Nam, K. H.; et al. Internet of things, digital biomarker, and artificial intelligence in spine: Current and future perspectives. Neurospine. 2019, 16(4): 705-711.

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