A team of scientists from Sechenov University has developed a system for selecting personalized treatment for urolithiasis. This approach should reduce the risk of errors, complications, and recurrence of the disease. This was reported to socialbites.ca at the First Moscow State Medical University named after IM Sechenov.
The implementation of the project will form the basis for the creation of personalized treatment methods and will reduce possible risks during and after the operations, reduce the need for repetitive interventions, shorten the hospital stay and increase patient satisfaction with the medical care provided. , an employee of the Institute of Regenerative Medicine told socialbites.ca Tkachev.
Now doctors use lithotripsy to treat urolithiasis. It is the crushing of stones using ultrasound, laser and other methods. The effectiveness of a particular lithotripsy method depends on the size of the stone, its density, and other characteristics collected using X-ray or CT. However, insufficient data increase the risk of complications and recurrence of the disease.
The algorithm developed by the scientists will determine the most effective approach in the treatment of urolithiasis. The system is trained to look for relationships between parameters of previously collected stone samples and compare them with patient data obtained using CT or radiography.
The aim of the system is to calculate the missing parameters, physicochemical, structural and mechanical properties of the stones. The more information about the stone, the more accurately it will be possible to choose the best way to get rid of it. This will allow you to determine as accurately as possible which stone breaking method is appropriate for each situation.
Currently, urine stone samples have been selected, a database has been created, and research algorithms have been created. A classifier program is being developed to search for dependencies and patterns in the created database. These dependencies will form the basis of a trainable smart smart system.