Complete Technical Scheme for Automatic Biological Dose Estimation Platform

Awareness and preparedness level of medical workers for radiation and nuclear emergency response

Radiological science and nuclear technology have made great strides in the twenty-first century, with wide-ranging applications in various fields, including energy, medicine, and industry. However, those developments have been accompanied by the inherent risks of exposure to nuclear radiation, which is a source of concern owing to its potentially adverse effects on human health and safety and which is of particular relevance to medical personnel who may be exposed to certain cancers associated with low-dose radiation in their working environment. While medical radiation workers have seen a decrease in their occupational exposure since the 1950s thanks to improved measures for radiation protection, a concerning lack of understanding and awareness persists among medical professionals regarding these potential hazards and the required safety precautions. This issue is further compounded by insufficient capabilities in emergency response. This highlights the urgent need to strengthen radiation safety education and training to ensure the well-being of medical staff who play a critical role in radiological and nuclear emergencies. This review examines the health hazards of nuclear radiation to healthcare workers and the awareness and willingness and education of healthcare workers on radiation protection, calling for improved training programs and emergency response skills to mitigate the risks of radiation exposure in the occupational environment, providing a catalyst for future enhancement of radiation safety protocols and fostering of a culture of safety in the medical community.

Intercomparison of conventional and QuickScan dicentric scoring for the validation of individual biodosimetry analysis in Taiwan

PURPOSE

The dicentric chromosome assay (DCA), the gold standard for radiation biodosimetry, evaluates an individual absorbed radiation dose by the analysis of DNA damage in human lymphocytes. The conventional (C-DCA) and QuickScan (QS-DCA) scoring methods are sensitive for estimating radiation dose. The Biodosimetry Laboratory at Institute of Nuclear Energy Research (INER), Taiwan, participated in intercomparison exercises conducted by Health Canada (HC) in 2014, 2015 and 2018 to validate the laboratory's accuracy and performance.

MATERIAL AND METHODS

Blood samples for the conventional dose response curve for Taiwan were irradiated with 0, 0.25, 0.5, 1, 2, 3, 4 and 5 Gy. Ten blind blood samples were provided by HC. Either or both of two methods of conventional (C) or QuickScan (QS) scoring could be chosen for the HC's intercomparison. For C-DCA triage scoring, only cells with 46 centromeres were counted and each scorer recorded the number of dicentrics in the first 50 metaphases or stopped scoring when 30 dicentrics were reached. Scorers also recorded how much time it took to analyze 10, 20, and 50 cells. Subsequently, the data were entered into the Dose Estimate software (DoseEstimate_v5.1) and dose estimates were calculated. With QS-DCA scoring, a minimum of 50 metaphase cells (or 30 dicentrics) were scored in apparently complete metaphases without verification of exactly 46 centromeres.

RESULTS

For the blinded blood samples irradiated at HC and shipped to INER, the mean absolute deviation (MAD) derived after scoring 50 cells for C-DCA and QS-DCA was <0.5 Gy for all three intercomparisons, meeting the criteria for acceptance.

CONCLUSION

The results indicated that the Biodosimetry Laboratory at INER can provide reliable dose estimates in the case of a large-scale radiation accident.

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay

The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial radiations. Dicentric chromosome assay (DCA) is one of the representative methods of dose estimation that discriminates the aberration in chromosomes modified by radiation. Despite the DCA-based automated radiation dose estimation methods proposed in previous studies, there are still limitations to the accuracy of dose estimation. In this study, a DCA-based automated dose estimation system using deep learning methods is proposed. The system is comprised of three stages. In the first stage, a classifier based on a deep learning technique is used for filtering the chromosome images that are not appropriate for use in distinguishing the chromosome; 99% filtering accuracy was achieved with 2,040 test images. In the second stage, the dicentric rate is evaluated by counting and identifying chromosomes based on the Feature Pyramid Network, which is one of the object detection algorithms based on deep learning architecture. The accuracies of the neural networks for counting and identifying chromosomes were estimated at over 97% and 90%, respectively. In the third stage, dose estimation is conducted using the dicentric rate and the dose-response curve. The accuracies of the system were estimated using two independent samples; absorbed doses ranging from 1- 4 Gy agreed well within a 99% confidential interval showing highest accuracy compared to those in previous studies. The goal of this study was to provide insights towards achieving complete automation of the radiation dose estimation, especially in the event of a large-scale radiation exposure incident.

Optimization and validation of automated dicentric chromosome analysis for radiological/nuclear triage applications

Dicentric Chromosome Assay (DCA) is the most preferred cytogenetic technique for absorbed radiation dose assessment in exposed humans. However, DCA is somewhat impractical for triage application owing to its labor intensive and time consuming nature. Although lymphocyte culture for 48 h in vitro is inevitable for DCA, manual scoring of dicentric chromosomes (DCs) requires an additional time of 24-48 h, making the overall turnaround time of 72-96 h for dose estimation. To accelerate the speed of DC analysis for dose estimation, an automated tool was optimized and validated for triage mode of scoring. Several image training files were created to improve the specificity of automated DC analysis algorithm. Accuracy and efficiency of the automated (unsupervised) DC scoring was compared with the semi-automated scoring that involved human verification and correction of DCs (elimination of false positives and inclusion of true positives). DC scoring was performed by both automated and semi-automated modes for different doses of X-rays and γ-rays (0 Gy-5 Gy). Biodoses estimated from the frequencies of DCs detected by both automated (unsupervised) and semi-automated (supervised) scoring modes were grossly similar to the actual delivered doses in the range of 0.5 to 3 Gy of low LET radiation. We suggest that the automated DC tool can be effectively used for large scale radiological/nuclear incidents where a rapid segregation is essential for prioritizing moderately or severely exposed humans to receive appropriate medical countermeasures.