Good news! And this is only the beginning!
"Researchers have developed an artificial-intelligence (AI) tool that can diagnose a range of infections and health conditions in one sweep, by screening immune-cell gene sequences in blood samples. In a study of nearly 600 people, the tool identified whether participants were healthy or had COVID-19, type 1 diabetes, HIV or the autoimmune disease lupus, as well as whether they had recently received a flu vaccine. The tool could one day help clinicians to deal with “conditions that today don’t have definitive tests” ..."
From the editor's summary and abstract:
"Editor’s summary
The repertoire of immune receptors expressed by B and T cells provides an overview of the history of an individual’s infections and vaccinations, as well as other immune-related diseases. ... developed a framework, Mal-ID (machine learning for immunological diagnosis), to interpret the variable sequences of B and T cell receptors (BCRs and TCRs) from human blood samples. During training, six representations of sequence features of BCRs and TCRs were compared between healthy and ill individuals to learn commonalities, and these features were combined in a single model to predict disease status. This approach was able to distinguish controls, individuals with distinct autoimmune diseases or viral infections, and those who had received an influenza vaccine. ...
Structured Abstract
INTRODUCTION
Conventional clinical diagnosis relies on physical examination, patient history, laboratory testing, and imaging, but makes little use of the receptors on B cells and T cells that reflect current and past exposures and responses.
Microbial pathogen detection underpins infectious disease diagnosis. Other conditions are more challenging: Autoimmune diseases can require a combination of imaging studies and testing for autoantibodies and other laboratory abnormalities in the blood that may not yield a definitive disease classification. This process can be lengthy and may be complicated by initial misdiagnoses and ambiguous or overlapping symptoms between conditions.
B cell receptors (BCRs) and T cell receptors (TCRs) allow these immune cells to recognize and respond to specific antigens on pathogens and sometimes the body’s own tissues. The genes encoding BCRs and TCRs are generated by random recombination of segments in the genome of individual cells during their development, and have potential as a diverse set of sequence biomarkers associated with immune system activity.
BCR and TCR populations change after exposure to pathogens, after vaccination, and in response to autoantigens in autoimmune conditions, reflecting clonal expansion and selection of B cells and T cells during immune responses. Sequencing and interpreting BCR and TCR genes could provide a single diagnostic test for simultaneous assessment of many diseases.
RATIONALE
We designed experimental protocols and a data analysis framework for identifying human BCR heavy chain and TCR beta chain features characteristic of infectious and immunological disorders or elicited by therapeutic or prophylactic interventions such as vaccination.
Our method, named MAchine Learning for Immunological Diagnosis (Mal-ID), combines traditional immunological analyses, such as shared sequence detection between individuals with the same condition, with more complex features derived from artificial intelligence (AI) models of protein sequences, called protein language models. Although AI systems can be difficult to interpret, we developed ways to understand how the model makes its diagnostic predictions.
RESULTS
We generated large datasets of both BCR heavy chain and TCR beta chain sequences from the same individuals, spanning six disease or immune response states, to train and evaluate the Mal-ID model.
Mal-ID accurately identified immune status from blood samples of 542 individuals with COVID-19, HIV, lupus, type 1 diabetes, recent flu vaccination, and healthy controls, achieving a multiclass area under the receiver operating characteristic curve (AUROC) of 0.986 on data not used for training.
Combining features from both B cell and T cell receptor data led to the highest classification performance, but even with only BCR sequences, we still achieved high classification performance (0.959 AUROC in an expanded cohort adding 51 individuals for whom only BCR data were available). ...
CONCLUSION
This pilot study demonstrates that immune receptor sequencing data can distinguish a range of disease states and extract biological insights without prior knowledge of antigen-specific receptor patterns. With further validation and extension, Mal-ID could lead to clinical tools that harness the vast information contained in immune receptor populations for medical diagnosis."
AI tool diagnoses diabetes, HIV and COVID from a blood sample "‘One-shot’ approach that uses machine learning to screen immune cells could help to detect conditions with overlapping symptoms."
Disease diagnostics using machine learning of B cell and T cell receptor sequences (no public access)
The AI analyses gene sequences linked to receptors on the surface of B cells (pictured)
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