Abstract
Background In a major radiation incident, the speed of sample processing and interpretation of estimated exposures will be critical for triaging individuals. The Automated Dicentric Chromosome (DC) Identifier and Dose Estimator System (ADCI) selects and processes images to identify DCs and determines radiation dose without manual review. The goal of this study was to broaden accessibility and speed of this system with data parallelization while protecting data and software integrity.
Methods ADCI_Online is a secure web-streaming platform that can be accessed worldwide from distributed local nodes. Data and software are separated until they are linked for estimation of radiation exposures. Performance is assessed with data from multiple biodosimetry laboratories.
Results Dose estimates from ADCI_Online are identical to ADCI running on dedicated GPU-accelerated hardware. Metaphase image processing, automated image selection, calibration curve generation, and radiation dose estimation of a typical set of samples of unknown exposures were completed in <2 days. Parallelized processing and analyses using cloned software instances on different hardware configurations of samples at the scale of an intermediate-sized radiation accident (54,595 metaphase images) accelerated estimation of radiation doses to within clinically-relevant time frames.
Conclusions The ADCI_Online streaming platform is intended for on-demand, standardized radiation research assessment, biodosimetry proficiency testing, inter-laboratory comparisons, and training. The platform has the capacity to handle analytic bottlenecks in intermediate to large radiation accidents or events.
Competing Interest Statement
Ben C. Shirley is an employee and Peter K. Rogan and Joan H.M. Knoll are cofounders of CytoGnomix Inc. Eliseos J. Mucaki does not have any competing interests. The company has developed software which incorporates the methods presented in this study.
