Use of AI in the fight against blood cancer
Research Project | 3 Project Members
Cancer is one of the leading causes of morbidity and mortality worldwide, accounting for more than 10 million deaths per year, according to the World Health Organization (WHO). It is the second leading cause of death globally, after cardiovascular diseases. Genomic aberrations, such as mutations, deletions, fusions, and amplifications, play a critical role in the development and progression of cancer. They can alter the function of genes that control cell growth and division, leading to uncontrolled cell proliferation and tumor formation. They can also serve as important predictive biomarkers since their presence can guide the selection of targeted therapies. To define the mutational status of these genes, specialized laboratories utilize Next-Generation Sequencing (NGS) technology. This technology provides the genomic status of the interrogated gene but requires a significantly large amount of input material. Specialized laboratories can also perform NGS with fewer cells by developing their protocols, but data quality suffers. Notably, only a minor fraction of patients worldwide has access to NGS technology and its associated diagnostic output. This is due to the high investment and running costs of such laboratories and the dependence on qualified staff.
Computer vision, a subfield of artificial intelligence (AI), combines computer science, mathematics, and neuroscience methods to develop algorithms and models that enable machines to analyze, interpret, and understand visual information from the world.
Today, the diagnosis, classification, and prognostic stratification of hematological diseases requires incorporation of morphological characterization, immunophenotyping by flowcytometry and molecular genetics. What if we can can use AI not only to facilitate the interpretation of combined hugh data information but as well explore and then extend the current limitation of a human expert interpretation. We aim to push this limits in order to democratize medical knowledge and quality standards.
