A platform that can diagnose several diseases with a high degree of precision using metabolic markers found in patients’ blood has been developed by scientists at the University of Campinas (UNICAMP) in Brazil.
The method combines mass spectrometry, which can identify tens of thousands of molecules present in blood serum, with an artificial intelligence algorithm capable of finding patterns associated with diseases of viral, bacterial, fungal and even genetic origin.
“We used infection by Zika virus as a model to develop the platform and showed that in this case, diagnostic accuracy exceeded 95%. One of the main advantages is that the method doesn’t lose sensitivity even if the virus mutates,” said Melo’s supervisor Rodrigo Ramos Catharino, principal investigator for the project. Catharino is a professor at UNICAMP’s School of Pharmaceutical Sciences (FCF) and head of its Innovare Biomarker Laboratory.
Another strength of the platform, he added, is the capacity to identify positive cases of Zika even in blood serum analyzed 30 days after the start of infection, when the acute phase of the disease is over.
“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,” Catharino said.
Development and validation of the platform involved analysis of blood samples from 203 patients treated at UNICAMP’s general and teaching hospital. Of these, 82 were diagnosed with Zika by the method currently considered the gold standard in this field: real-time polymerase chain reaction (RT-PCR), which detects viral RNA in body fluids during the acute phase of the infection.
The other 121 patients were the control group. Approximately half had the same symptoms as the group that tested positive for Zika, such as fever, joint pain, conjunctivitis and rash, but had negative RT-PCR results for Zika. The rest had no symptoms and also tested negative or were diagnosed with dengue.
All collected samples were analyzed in a mass spectrometer, a device that acts as a kind of molecular weighing scale, sorting molecules according to their mass.
“We identified some 10,000 different molecules in the patients’ serum, including lipids, peptides, and fragments of DNA and RNA. Among these metabolites, there were particles produced both by Zika and by the patient’s immune system in response to the infection,” Catharino said.
All the data obtained in the spectrometry analysis of both the group that tested positive for Zika and the control group were then fed into a computer program running a random-forest machine learning algorithm. This type of artificial intelligence tool is capable of analyzing a large amount of data by specific statistical methods in search of patterns that can be used as a basis for classification, prediction, decision making, modeling and so on.
“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,” Catharino explained.
Each new set of patient data fed into the program enhances its learning capacity and makes it more sensitive, he went on. In the case of Zika, a panel of 42 biomarkers was established as a specific key to identifying the virus. Twelve of these were found by the algorithm to be highly prevalent in the blood of patients who tested positive for the disease.
“In this platform, it isn’t important to know a lot individually about each of the molecules that serve as markers of the infection. It’s the set that matters and that will tell us with a high level of accuracy whether we’re looking at Zika. Moreover, even if the virus mutates, the program adapts and changes too. It’s not a static methodology,” Catharino said.
The UNICAMP group is currently performing tests to evaluate the platform’s capacity to diagnose systemic diseases caused by fungi. They also plan to test how well it detects bacterial and genetic diseases. Anderson de Rezende Rocha, a professor at the same university’s Institute of Computing (IC-UNICAMP), is collaborating on the research.
In the cloud
In theory, any laboratory equipped with a mass spectrometer could use the new diagnostic platform developed at UNICAMP. Mass spectrometers are routinely used in procedures such as measuring vitamin D and screening blood spots from newborns to detect metabolic diseases via the heel prick test.
“Our proposal is to make the platform available in the cloud, so that it can be downloaded to any mass spectrometer anywhere in the world. Data analysis can be performed online. Whether it would be free or paid is yet to be defined,” Catharino said.
The Latest on: Artificial intelligence diagnostics
via Google News
The Latest on: Artificial intelligence diagnostics
- PathAI raises $60 million for AI pathology and diagnostic tools on April 17, 2019 at 9:24 am
PathAI, a startup that employs machine learning techniques to improve diagnostic accuracy ... pathology and the application of artificial intelligence there is an opportunity to increase ... […]
- Gestalt Diagnostics to Participate in Panel Discussion on Digital Pathology During the 2019 Executive War College on April 17, 2019 at 9:00 am
April 17, 2019 /PRNewswire/ -- Gestalt Diagnostics' COO and Chief Strategy Officer ... of dollars being invested in developing digital pathology systems and artificial intelligence products designed ... […]
- CENTOGENE AG: CENTOGENE Appoints AI Industry Leader Dr. Carsten Ullrich as Director of Artificial Intelligence on April 17, 2019 at 6:44 am
Research Fellow from German Research Center for Artificial Intelligence to Lead Team Driving Solutions ... The company's AI initiative is also instrumental in enhancing diagnostic effectiveness and ... […]
- Aidoc Raises $27 Million to Expand Its Life-saving Artificial Intelligence Solutions by 700% Across Medical Imaging on April 17, 2019 at 6:00 am
Aidoc's FDA-cleared and CE-marked solutions support and enhance the impact of radiologist diagnostic power, helping them expedite patient treatment and improve quality of care. Radiologists benefit ... […]
- Workshop explores the future of artificial intelligence in medical imaging on April 16, 2019 at 10:07 pm
to explore the future of artificial intelligence (AI) in medical imaging ... The organizers aimed to foster collaboration in applications for diagnostic medical imaging, identify knowledge gaps and ... […]
- General Atlantic Backs PathAI to Improve Diagnostics on April 16, 2019 at 9:34 pm
But meanwhile, they are turning to artificial intelligence-powered programs that can turbocharge the research and testing of new treatments. Venture investors are betting that PathAI Inc. will bring ... […]
- Artificial intelligence performs as well as experienced radiologists in detecting prostate cancer on April 16, 2019 at 6:09 am
The research suggests that an artificial intelligence system could save time and potentially provide diagnostic guidance to less-experienced radiologists. The study’s senior authors are Kyung Sung, ... […]
- Artificial Intelligence Powering Boom in Israel's Digital Health Sector on April 16, 2019 at 4:05 am
Of the $511M, over 50% ($285M) went to companies in decision support and diagnostics which rely heavily on data ... allowing startups an increased ability to train and test artificial intelligence ... […]
- Automotive Diagnostics Market To Reach USD 55.6 Billion By 2026| Reports And Data on April 15, 2019 at 9:06 am
Market Size – USD 39.1 Billion in 2018, Market Growth - CAGR of 4.4%, Market Trends –Increasing innovations in artificial intelligence NEW YORK, April 15, 2019 (GLOBE NEWSWIRE) -- The global ... […]
via Bing News