Researchers developed MAGIC, an AI-based system integrating automated microscopy and genomics to study chromosomal abnormalities linked to cancer.
Key Details
- 1MAGIC uses automated microscopy, AI-driven image analysis, and single-cell genome sequencing to identify and tag abnormal cells.
- 2The system can analyse nearly 100,000 cells in less than a day, vastly exceeding manual methods.
- 3Chromosomal abnormalities were seen in just over 10% of cell divisions, doubling with mutated p53 gene.
- 4MAGIC relies on a laser and photoconvertible dye to mark target cells, facilitating further analysis.
- 5The tool is versatile and can be trained to identify a variety of visually distinct cellular features.
Why It Matters
MAGIC represents a significant step forward in combining imaging AI and genomic methods for the high-throughput study of cellular abnormalities, offering potential advances in cancer detection and understanding cancer origins. Such AI-driven image analysis tools could pave the way for faster, more precise research and diagnostics in radiology and oncology.
Source
EurekAlert
Related News
•EurekAlert
ML and Multimodal Imaging Power Cerebral Blood Flow Monitoring for Spaceflight
Researchers developed a machine learning model that uses ultrasound and MRI data to predict cerebral blood flow in simulated microgravity for astronaut health.
•EurekAlert
Deep Learning Model Predicts Language Outcomes After Cochlear Implants Using MRI
AI model using deep transfer learning accurately predicts spoken language outcomes in deaf children after cochlear implantation based on pre-implantation brain MRI scans.
•EurekAlert
LSTM Deep Learning Enhances Optical Sensing for Biochemical and Medical Applications
Researchers have developed an LSTM-driven interferometric sensing system that achieves both high sensitivity and wide measurement range, overcoming previous trade-offs in optical sensing.