NEW DELHI
IIT Delhi, in collaboration with clinical partners, is coming up with medical imaging solutions where machine learning will be used in healthcare for diagnosis, grading, and treatment monitoring of diseases such as cancer, stroke, and osteoarthritis. This will make the entire process convenient and help the doctors make informed decisions.
“A doctor has to see hundreds of scans every day, which somehow leads to a delay for those cases that need to be monitored first because of humans’ work efficiency saturation, but when tools like this enter the health sector, it can screen and will flag the abnormality in priority and hence save time,” said Prof (Dr) Amit Mehndiratta, Professor, IIT-Delhi.
Medical imaging, also known clinically as radiology, is the field of medicine in which medical professionals recreate various images of parts of the body for diagnostic or treatment purposes. X-rays, computed tomography, MRI imaging, PET imaging, and nuclear imaging, all fall under the umbrella of imaging. “This artificial intelligence-based solution will give a probability map of whether this patient will be a responder or a non-responder. And if it is going to be a non-responder with a very high probability, then the doctor could opt for an alternate therapeutic procedure because if we expect the patient is not going to respond, why unnecessarily expose him to the severe side effects?” said Dr Mehndiratta.
“Similarly, in scans like MRI, sometimes a dye is injected to the patient for medical imaging, but when this study hypothesis comes into action, we might not use it. Instead, we might use diffusion imaging, which will make the entire process convenient, especially for those patients who are suffering from any kidney-related disease, as these dyes stay in their body for a longer period than a normal, healthy person,” he added. “We have already deployed five tools to our clinical partners, which we have published and copy-righted, and we are confident that they will work, but it will take 3 to 5 years to be accepted by the clinical community, “