Artificial intelligence (AI) could significantly speed up the detection of antibiotic-resistant bacteria, a new study from the University of Cambridge suggests.
The research, published in Nature Communications, demonstrates a machine learning model that can identify drug-resistant Salmonella typhimurium from microscope images, potentially reducing diagnosis time compared to traditional methods. Is. This is part of a growing push to use AI to detect disease and find new drugs to treat it.
“We believe there is enormous potential for better diagnostics to fundamentally change healthcare and pharma,” said Mary Beckwith, co-founder of Lindus Health, a company that was not involved in the Cambridge study. is focused on accelerating clinical trials, told PYMNTS.
“Traditionally, diagnostics have been overlooked by industry and investors, because it's less about detecting a disease than treating it with potentially long courses of medication,” Beckwith said. There is commercial compensation.” “This is changing as health care systems come under greater pressure to reduce costs and improve efficiency, and this is where better diagnostics can play a big role.”
Healthcare's focus on prevention is driving the diagnostic tech market. This change creates new revenue opportunities in the healthcare development industry.
Search for clues
Advances in AI-powered medical imaging technology are opening up new commercial opportunities in healthcare diagnostics and drug development. In the Oxford study, researchers used high-powered microscopes to examine samples of S. Typhimurium exposed to different concentrations of a common antibiotic, ciprofloxacin. They identified five key imaging features that distinguish between resistant and susceptible bacteria and then trained a machine learning algorithm using data from 16 samples.
A computer program can tell if bacteria will resist an antibiotic without actually using the drug. He did this after growing the bacteria for just 6 hours, much faster than typical tests, which take 24 hours.
“The beauty of the machine learning model is that it can identify resistant bacteria based on a few subtle features on microscopy images that the human eye can't detect,” said Tuan-Anh Tran, who worked on the research.
Commercial effects and industrial effects
Potential uses of AI technology could be significant for the healthcare and pharmaceutical industries. Rapid and accurate diagnosis of antibiotic-resistant infections may lead to more effective treatment strategies.
“This valuable time saved can be used to lock down infections before they spread,” Beckwith said. However, Beckwith also cautioned that further validation would be necessary: ”Each application will have to undergo specific trials to demonstrate a cost, accuracy or speed advantage over the current gold standard before it can be adopted.”
Drug-resistant bacteria are becoming a serious threat to global health. These “superbugs” have evolved to resist common antibiotics, making infections difficult to treat. The overuse and misuse of antibiotics in healthcare and agriculture has accelerated this process. As a result, simple infections that were once easily cured can now become life-threatening. Doctors are running out of effective treatment options for some bacterial infections.
Future direction
AI can help doctors diagnose diseases more quickly and accurately in many areas of medicine.
Tech companies and startups are racing to develop AI-powered tools that can transform healthcare. For example, Google's DeepMind has created an AI system that is able to detect breast cancer in mammograms with greater accuracy than human radiologists. In a study published in Nature, the system reduced false positives by 5.7 percent and false negatives by 9.4 percent compared to human experts.
In 2018, IDx Technologies received FDA approval for its AI-based system that detects diabetic retinopathy. The software analyzes retinal images and provides quick diagnosis, potentially increasing screening rates in underserved areas.
MIT researchers have developed an AI model that can detect Alzheimer's disease years before symptoms appear. By analyzing brain scans, the system identifies subtle patterns associated with early-stage Alzheimer's, potentially allowing for early intervention.
AI has also contributed to the fight against COVID-19. Infervision's AI software, deployed in Chinese hospitals, rapidly analyzes chest CT scans to detect symptoms of coronavirus pneumonia, helping to prioritize treatment cases.
The research team plans to study more types of bacteria and antibiotics. They want to create a system that can detect drug-resistant bacteria in samples such as blood, urine or saliva. This could help doctors better treat infections in the future.
Sushmita Sridhar, who started the project while a PhD student at Cambridge, is now able to take a complex sample and identify susceptibility and resistance from it, especially for a clinical context. What will really matter? postdoc at the University of New Mexico and Harvard School of Public Health, said in a news release. “It's a much more complex problem and one that hasn't really been resolved, even in clinical evaluation in the hospital.”