Receive our newsletter – data, insights and analysis delivered to you
  1. News
June 15, 2018

Brazilian researchers devise AI-based approach to detect pathogens

A research team from the University of Campinas (UNICAMP) in Brazil has developed a new platform based on artificial intelligence (AI) for the diagnosis of various pathogenic diseases.

A research team from the University of Campinas (UNICAMP) in Brazil has developed a new platform based on artificial intelligence (AI) for the diagnosis of various pathogenic diseases.

The approach uses a combination of AI algorithm and mass spectrometry to accurately identify metabolic markers in patient blood samples.

Mass spectrometry can detect numerous molecules present in blood serum, while the algorithm can pin-point patterns related to viral, fungal, bacterial and genetic diseases.

“It is expected that the algorithm can examine large data volumes for specific patterns that can help in classification, prediction, decision making and modelling.”

The team built the platform using Zika virus infection as a model and observed more than 95% accuracy for diagnosis of this condition.

According to the researchers, the technique retains sensitivity even in cases of virus mutations, with the capability to detect positive Zika cases even in samples examined after completion of acute infection phase (30 days post onset).

UNICAMP Innovare Biomarker Laboratory head Rodrigo Ramos Catharino said: “None of the currently available diagnostic kits has the sensitivity to detect infection by Zika after the end of the acute phase. The method we developed could be useful to analyze transfusion blood bags, for example.”

Content from our partners
“Everything is custom”: Behind the scenes of medical wire solutions
The added value of Qarad’s multilingual freephone service to their eIFU solution
Small and simple: how medical device manufacturers select materials

The researchers trained the AI tool using data obtained from analysis of 203 blood samples, of which 82 patients were diagnosed with Zika using real-time polymerase chain reaction (RT-PCR) method.

It is expected that the algorithm can examine large data volumes for specific patterns that can help in classification, prediction, decision making and modelling.

Catharino noted: “The algorithm separates samples randomly, determines which one will be the training group and the blind group, and then carries out testing and validation.

“At the end, it tells us whether with that number of samples it was possible to obtain a set of metabolic markers capable of identifying patients infected by Zika.”

Currently, the team is assessing the platform for the diagnosis of fungal systemic diseases, with plans to extend the evaluation to bacterial and genetic conditions.

Related Companies

NEWSLETTER Sign up Tick the boxes of the newsletters you would like to receive. The top stories of the day delivered to you every weekday. A weekly roundup of the latest news and analysis, sent every Friday. The medical device industry's most comprehensive news and information delivered every month.
I consent to GlobalData UK Limited collecting my details provided via this form in accordance with the Privacy Policy
SUBSCRIBED

THANK YOU