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Reducing unnecessary retakes is a critical responsibility of radiologic technologists and plays an important role in promoting medical safety by minimizing patient radiation exposure. This study aimed to evaluate the impact of retake visualization using the general radiography management system "RADInsight" and the effectiveness of the educational interventions through periodic retake review meetings. General radiographic examinations performed at our institution from June 2023 to October 2024 were retrospectively analyzed. Retake rates and contributing factors were examined by anatomical region and cause. The overall retake rate significantly decreased from a maximum of 7.5% to 2.1%. Positioning errors were the most frequent cause of retakes, with notable improvements observed in lateral views of the knee and elbow following educational intervention. Inexperienced technologists showed a higher incidence of retakes due to motion artifacts and insufficient exposure. The combined use of RADInsight for retake monitoring and targeted educational intervention effectively reduced retake rates. This approach demonstrated its value as a practical strategy to support radiologic technologist training and reduce patient radiation exposure, thereby contributing to enhanced medical safety. Wider adoption and multi-institutional application are anticipated in the future.
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Nurses are frequently involved in radiologic imaging procedures; however, opportunities to learn about radiology in clinical practice are limited. This study aimed to assess the current level of radiology-related knowledge among newly hired nurses at Tohoku Medical and Pharmaceutical University Hospital (hereafter referred to as our hospital) and to clarify the need for radiology education. A questionnaire survey was conducted among 77 nurses who started working at our hospital in April 2022. The survey included items related to radiology education, knowledge of radiation exposure, and interest in working in radiology departments. Although 58.4% of respondents had attended radiation-related classes during their studies, 93.5% were unable to distinguish between radioactivity and radiation. Misunderstandings were also observed regarding the safe distance required during portable X-ray imaging and the hereditary effects of radiation exposure. In contrast, 62.3% of the nurses expressed interest in working in radiology departments in the future. Newly hired nurses surveyed in this study demonstrated insufficient fundamental knowledge and several misconceptions regarding radiology, yet showed interest in working in radiology, highlighting the need for structured educational programs.
This study aimed to verify the contouring accuracy of the artificial intelligence (AI)-based auto-segmentation software Contour+ (MVision AI Oy, Helsinki, Finland) both quantitatively and visually, and to evaluate its clinical validity for the thoracic region in Japanese patients. Ten thoracic radiotherapy cases with lung lesions were analyzed. Contour+ was used to automatically delineate both lungs, trachea, bronchus, esophagus, spinal cord, and heart. Three observers visually evaluated the auto-contours using a five-point scoring system, and the final manually corrected contours were used as the reference to calculate the dice similarity coefficient (DSC), Hausdorff distance (HD), and volume differences. In all cases, the AI auto-contours were evaluated as "clinically acceptable with minor modifications (score ≥3)," with an average score of 4.4. The mean DSC values were 1.00 for the lungs, 0.99 for the trachea, 0.91 for the bronchi, 0.86 for the esophagus, 0.99 for the spinal cord, and 0.99 for the heart, indicating high agreement. The mean HD values were 5.68 mm, 8.72 mm, and 3.30 mm for the bronchi, esophagus, and heart, respectively. The mean volume changes after manual correction were 4.02 cc for the bronchi, 2.55 cc for the esophagus, and 2.58 cc for the heart. AI-based auto-segmentation software Contour+ demonstrated high geometric agreement and clinical validity for major thoracic organs in Japanese patients, suggesting its potential to reduce the contouring workload and promote standardization in radiotherapy treatment planning.
This study aimed to investigate the usefulness of scatter correction in emergency lateral cervical spine radiography. A lead disc or an aluminum circular plate was placed on a water-equivalent phantom with a thickness of 13 cm, and radiographic images were acquired by varying the distances between the phantom and the flat panel detector (air gaps). The content rate of scattered radiation, signal-difference-to-noise ratio measured from image-processed images (SDNRIP), and contrast were evaluated for images with scatter correction technique (virtual grid images: VG images), images with a grid (real grid images: RG images), and images without a grid or scatter correction technique (WG images). Additionally, lateral cervical spine radiographs were obtained using a phantom, and paired comparison methods (contrast, granularity, sharpness, overall assessment) were performed by seven radiologic technologists. The content rate of scattered radiations in VG images decreased with increasing air gap and became lower than in RG images at air gaps of 15 cm or more. SDNRIP and contrast for VG images were significantly higher than for RG images at air gaps of 15 cm and 20 cm (p<0.05). In the observer study, VG images were significantly superior to others in all evaluation items except granularity (p<0.05). The use of scatter correction in emergency lateral cervical spine radiography proves useful in achieving image quality superior to that of a grid.
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This study aimed to evaluate the performance of several large language models (LLMs) on the Japanese National Examination for Radiological Technologists and to characterize their performance profiles. We utilized a dataset comprising questions from 12 consecutive years of the national examination (the 65th to the 76th iterations), excluding items that were officially retracted or deemed inappropriate. 5 distinct LLMs (ChatGPT-3.5, Gemini 2.5 Flash, Gemini 2.5 Pro, Copilot, and Claude Sonnet 4) were prompted to answer these questions. The accuracy of each LLM was calculated for the entire question set and for subsets categorized by question format. Across the entire examination and within numerous subject areas, Gemini 2.5 Pro achieved the highest accuracy. An analysis by question format revealed a general trend: most LLMs demonstrated superior performance on text-based questions, followed by calculation-based and then image-based questions. However, some models exhibited notably strong performance specifically on calculation-based problems. While LLMs demonstrate considerable proficiency in answering questions from the National Examination for Radiological Technologists, our findings also reveal significant limitations, particularly in their capacity to interpret image-based problems. This study highlights both the potential utility and the current challenges of leveraging LLMs as supplementary learning tools for this professional certification examination.
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This study aimed to clarify the actual conditions of near-miss incidents experienced by radiological technology students during clinical training and to analyze the contributing factors, to provide suggestions for future safety education and curriculum development. An anonymous self-administered questionnaire was conducted among students and graduates of a radiological technology training program. The survey items included the specific nature of the incidents, background factors at the time of occurrence, the modality in which the incidents occurred, and the discoverer of the incidents. Quantitative data were analyzed using descriptive statistics and cross-tabulation, while qualitative responses were analyzed using content analysis. A total of 75 valid responses were obtained. The reported near-miss incidents were diverse, with many related to basic confirmation procedures such as patient misidentification, entanglement of tubes and cables, and failure to remove metallic items. Most of the incidents occurred in general radiography and computed tomography, accounting for approximately 70% of all reports. In terms of discoverers, the majority of incidents were noticed by the students themselves (58.3%), followed by clinical instructors (30.5%). Near-miss experiences serve as valuable educational resources in student training. Redesigning safety education by focusing on common patterns shared with cases reported by licensed professionals, and systematizing these experiences into structured learning modules, may help enhance both the quality of education and safety awareness in radiological practice.
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The objective of this study is to achieve site-specific three-dimensional (3D) automatic segmentation of skeletal muscles in body CT images. We aimed to improve recognition accuracy of nine muscle regions: sternocleidomastoid, erector spinae, trapezius, supraspinatus, rectus abdominis, obliques, quadratus lumborum, psoas major, and iliacus. Then, we focused on utilizing all skeletal muscle areas outside the target recognition regions that were not previously used. Our method trains the 2D U-Net to learn both the target site-specific skeletal muscle region and all other skeletal muscles together. We utilized 30 cases of unenhanced body CT images and performed three-fold cross-validation. The proposed method achieved an average Dice coefficient of 88.37% across nine regions, showing improvements of 25.78% and 1.86% compared to the individual learning of each region (baseline) and the simultaneous learning of erector spinae (previous method), respectively. Comprehensive learning of all skeletal muscle regions successfully improved the accuracy of U-Net-based 3D automatic segmentation for site-specific skeletal muscles in body CT. It leads to enhanced, precise body composition analysis for skeletal muscle regions.
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