The pre-computational era of medicine has provided enough data regarding disease status, prognostic factors, and therapeutic approaches to pave the road for artificial intelligence (AI) to use these data to come up with better solutions. The past few decades have witnessed tremendous evolutions in computational medicine. Today, machines are able to go through databases, retrieve desired information, and recruit this information to predict outcomes and design therapeutic interventions for a given individual. As an industry-leading company pursuing the frontier of science, Creative Biolabs has been focusing on the application of AI in medicine for years. With experience accumulated from practice, we are capable of providing reliable AI-based tumor immunotherapy evaluation development services to global researchers.

Potential for AI in Immunotherapy

Since the first introduction of AI in 1956, it aimed to tackle problems with human intelligence, but with greater speed and accuracy. AI has been inspired a lot by natural swarm intelligence, such as those observed in groupings of animals and insects. Swarm intelligence not only is observed in our surroundings but also exists within the human body. For instance, the vertebrae immune system can be considered as a swarm intelligence in that it consists of independent, self-organized agents that interact with one another to form a higher intelligence. AI has profited from the immune system and the artificial immune system has been used for overcoming challenges such as intrusion detection, self-healing of robots, optimization, and anomaly detection. As much as immunology has served the AI, recently AI is being used in many immunological fields. Besides remarkable analytic abilities, antigen and phenotype detection, predicting prognosis and treatment outcomes, etc., are examples where AI is serving the immunologists in the field.

Machine learning methodologies frequently used in immunologyFig.1 Machine learning methodologies frequently used in immunology. (Jabbari, 2019)

Application of AI in Immunotherapy-Lung Cancer

The characterization of lung cancer as an immune responsive malignancy has led to the development of immunotherapies that show significant promise. However, several challenges remain in the use of immunotherapies. Patient selection and predicting treatment response are challenging. Research on predictive biomarkers is currently focused on genetic and histological markers from biopsies. However, machine learning (ML) and radiomics have shown promising results in improving patient selection and outcome prediction by providing unique insights into the tumor and its microenvironment in a non-invasive manner. For example, Charoentong et al. used ML algorithms on cancer anti-genomes from the Cancer Immunome Atlas to identify determinants of tumor immunogenicity and developed a scoring scheme for predicting response to checkpoint inhibitors' (CPI's), while Coroller et al. demonstrated predictive radiomic features for treatment response using Computed Tomography (CT) data.

Machine learning for screening cancer immunotherapy.Fig.2 Machine learning for screening cancer immunotherapy. (Zhou, 2020)

Services at Creative Biolabs

Committed to in vitro diagnostics (IVD) development over years, Creative Biolabs has step-by-step established our platform with advanced facilities, the latest technologies, and excellent scientific staff. With the comprehensive platform established from practice, we are confident in offering reliable AI-based tumor immunotherapy evaluation development services to meet global customers' requirements.

With all the advantages in various aspects, Creative Biolabs is eager to bring our clients top-rated technology services and customer experience. If you are interested in AI-based tumor immunotherapy evaluation development services or you have any other questions about our services, please don't hesitate to contact us for more information.

References

  1. Jabbari, P.; Rezaei, N. Artificial intelligence and immunotherapy. Expert Rev Clin Immunol. 2019, 15(7): 689-691.
  2. Zhou, X.; et al. Screening cancer immunotherapy: When engineering approaches meet artificial intelligence. Adv Sci (Weinh). 2020, 7(19): 2001447.

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