New York, NY — October 16, 2025 — Leads & Copy — Yandex has developed the world’s first AI solution to assess brain development in infants. The solution analyzes MRI scans and can distinguish between gray and white brain matter with over 90% accuracy.
Cutting evaluation time from days to minutes, it enables earlier detection and more effective rehabilitation planning for infants with cerebral palsy and other central nervous system disorders. It is freely available on GitHub and can be used by healthcare and clinical research institutions worldwide.
Yandex B2B Tech, together with the Yandex School of Data Analysis and St. Petersburg State Pediatric Medical University, developed the AI solution for assessing brain development in infants under 12 months of age. The neural network automates MRI analysis, cutting processing time. Designed as a decision-support tool for suspected cerebral palsy and other central nervous system disorders, it helps physicians determine effective rehabilitation strategies.
Cerebral palsy affects an estimated 2–3 out of every 1000 live births. Early diagnosis is critical for improving outcomes and ensuring effective rehabilitation. Yet detecting cerebral palsy within the first 12 months of life remains one of the most difficult tasks in modern medicine.
An MRI testing procedure typically takes 20–40 minutes, but interpreting the images and preparing a report can take an experienced radiologist anywhere from several hours to several days.
The resulting model achieved over 90% accuracy in distinguishing gray and white matter in infant brains on internal evaluation data, demonstrating its potential for clinical use.
According to Anna Lemyakina, Head of the Yandex Cloud Center for Technologies and Society, their goal is to make the most advanced Yandex technologies accessible to doctors, helping them deliver accurate and timely diagnoses, select optimal treatments, and develop new medicines.
Because the code is open-source and free to use, the solution can be adopted by medical institutions worldwide, helping advance the global practice of early cerebral palsy diagnosis. With over 90% accuracy, the model highlights outlines and quantifies the ratio of gray to white matter in an infant’s brain. MRI analysis is reduced from days to minutes, which is critical for timely therapy and especially valuable in longitudinal monitoring, where hundreds or thousands of scans may need review. Automating routine scan segmentation allows radiologists to focus on complex cases and direct patient care.
The neural network code is available on GitHub and can be integrated into existing medical IT systems.
Contact: NettResults, press@nettresults.com
Source: Yandex
