Bengaluru: For several years, researchers from the Indian Institute of Science (IISc), led by Siva Umapathy, have been working on combining Raman spectroscopy with AI (artificial intelligence) for applications in diagnostics and healthcare. They have previously shown how the combination can be used to detect Covid-19 biomarkers in blood samples — an accomplishment that won the Challenger award at the NASSCOM AI Game Changer Awards programme last year.
In a new study, Siva Umapathy and Deepak Kumar Saini’s teams have demonstrated another application for this combination: Quickly identifying bacterial pathogens in different types of clinical samples. “Rapidly identifying the causative bacterium can help clinicians treat and manage the symptoms of infectious diseases more efficiently. Current gold-standard tests are either time-consuming or work only for a specific type of sample nucleic acid. Amplification-based methods are time-intensive and involve bacterial lysis,” IISc said.
In Raman microspectroscopy, a laser beam is flashed on the sample using an optical microscope, and the light scattered by the bacterium gives a unique spectrum, which would allow scientists or clinicians to identify them individually. “But making out subtle differences between different spectra can be challenging for the human eye. This is where AI can help,” IISc added.
The researchers employed a state-of-the-art deep learning model called Residual Network (ResNet) to identify and differentiate between the spectra of eight bacterial species.
In a new study, Siva Umapathy and Deepak Kumar Saini’s teams have demonstrated another application for this combination: Quickly identifying bacterial pathogens in different types of clinical samples. “Rapidly identifying the causative bacterium can help clinicians treat and manage the symptoms of infectious diseases more efficiently. Current gold-standard tests are either time-consuming or work only for a specific type of sample nucleic acid. Amplification-based methods are time-intensive and involve bacterial lysis,” IISc said.
In Raman microspectroscopy, a laser beam is flashed on the sample using an optical microscope, and the light scattered by the bacterium gives a unique spectrum, which would allow scientists or clinicians to identify them individually. “But making out subtle differences between different spectra can be challenging for the human eye. This is where AI can help,” IISc added.
The researchers employed a state-of-the-art deep learning model called Residual Network (ResNet) to identify and differentiate between the spectra of eight bacterial species.
Read More:Iisc Scientists Use Ai, Raman Spectroscopy To Detect Bacterial Pathogen | Bengaluru News
2022-12-28 02:44:00
