Using artificial intelligence to improve radiation dose prediction accuracy

Adayabalam Balajee, director of the REAC/TS Cytogenetic Biodosimetry Laboratory

The November 2024 special issue of “Applied Sciences” recently published an artificial intelligence (AI) study co-authored by ORISE Cytogenetic Biodosimetry Laboratory Director Dr. Adayabalam Balajee (pictured). The article, titled “Neural Network Ensemble to Detect Dicentric Chromosomes in Metaphase Images,” examines how AI-based tools can advance the field of radiation biodosimetry by improving the accuracy of absorbed dose estimation and by facilitating the development of predictive modeling for radiation induced short- and long-term health effects in humans.

Balajee collaborated with researchers from the Universidad de Málaga and the Hospital Universitario y Politécnico La Fe in Valencia, Spain to use the AI-based Convolutional Neural Networks to improve the precision of automated detection of radiation induced dicentric chromosomes in human lymphocytes. The AI-based dicentric chromosome detection methodology will support a rapid triage by improving the dose prediction accuracy.

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