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Background: CT lung imaging protocols need to be optimized. This claim is especially important due to the possible introduction of low-dose CT (LDCT) for lung cancer screening. Given the incorporation of non-linear reconstructions and post-processing, the use of phantoms that consider task-based evaluation is needed. This is also true for acceptance and continuous QC use.
Purpose: To present and validate a lung-CT hybrid phantom composed of two setups, one for task-based image quality metrics and the other anthropomorphic.
Methods: The task-based metrics setup was based on the well-known Mercury phantom and the anthropomorphic setup named Freddie (from Figure of Merit Performance evaluation of Detectability in Diagnostic CT Imaging Equipment) was designed with the same basic dimensions of the Mercury phantom, but including pieces and materials for mimicking chest structures, such as tracheobronchial tree and lung parenchyma. This setup allows the inclusion of pieces of different sizes to mimic ground-glass opacities, and sub-solid and solid lung nodules. The validation of the phantom adopted three methods: comparative evaluation of the attenuation properties and the corresponding Hounsfield Units (HU) values of the selected materials; image assessment according to five chest radiologists and eight non-radiologists' observations (reader study), and measurement of task-based metrics. Images of both setups were acquired using two clinical thorax protocols, both using automatic tube current modulation (TCM). Two x-ray filter combinations were adopted. The images were reconstructed using a deep learning-based algorithm.
Results: The agreement of nominal and observed HU values in the task-based setup was within 15%, except for three (TangoBlack+, VeroClear, and HIPS) of the materials employed in the phantom construction, at some beam energies. In the reader study, synthetic solid nodules printed in VeroClear received average Likert scores 4.0 (range 3.0-4.0) from radiologists and 3 (range 2.6-3.8) from non-radiologists, printed in TangoBlack+ received an average Likert score of 3.9 (range 3.8-4.2) from radiologists and 4.0 (range 3.8-4.4) from non-radiologists, while those printed in HIPS scored an average Likert of 3.8 (range 3.3-3.9) from radiologists and 3.3 (range 3.1-3.3) from non-radiologists. The synthetic ground-glass opacities (GGO) nodules manufactured in EVA received an average Likert score of 3.8 (range 2.8-4.6) from radiologists and 4.3 (range 3.6-4.8) from non-radiologists. The task-based setup demonstrated detectability index variations across protocols influenced by the dose levels, voltage, and x-ray beam filtration used.
Conclusions: The novelty of the proposed design is concentrated on the possibility of associating the response of the task-based setup (Mercury) with a patient-based setup (Freddie) in a unique phantom. This hybrid design enhances the potential to apply the detectability index for optimizing CT protocols in clinical scenarios.
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http://dx.doi.org/10.1002/mp.17990 | DOI Listing |
PLOS Digit Health
September 2025
Department of Dermatology, Stanford University, Stanford, California, United States of America.
Large Language Models (LLMs) are increasingly deployed in clinical settings for tasks ranging from patient communication to decision support. While these models demonstrate race-based and binary gender biases, anti-LGBTQIA+ bias remains understudied despite documented healthcare disparities affecting these populations. In this work, we evaluated the potential of LLMs to propagate anti-LGBTQIA+ medical bias and misinformation.
View Article and Find Full Text PDFCureus
August 2025
Department of Gastroenterology, Sixth Affiliated Hospital, Sun Yat-sen University, Guangzhou, CHN.
Purpose To evaluate the feasibility of artificial intelligence language generation models (AILMs) in medical education, we examined the utilization patterns and attitudes of medical students in a developed area of Southern China. Methods We conducted a cross-sectional questionnaire survey assessing educational background, awareness, usage, and attitudes towards AILMs. Attitudes were measured using a five-point Likert scale, where scores of 4 or above indicated support, scores of 2 or below indicated opposition, and a score of 3 indicated a neutral stance.
View Article and Find Full Text PDFAnat Sci Educ
September 2025
University of Florida College of Medicine, Gainesville, Florida, USA.
Self-efficacy and anatomical knowledge have been shown to be important in the development of medical students. Validated instruments designed to measure the construct of anatomical self-efficacy during the clinical years of medical school are limited. In this study, the Anatomical Self-Efficacy Instrument for Clinical Clerkships (ASEI-CC) was developed, and evidence for the reliability of the scores and the validity of the interpretations of the scores was gathered.
View Article and Find Full Text PDFJMIR Form Res
September 2025
Department of Psychiatry and the Behavioral Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States.
Background: Residency is a critical period in a physician's training, characterized by significant physical, cognitive, and emotional demands that make residents highly susceptible to stress and associated negative health outcomes. While physiological signals such as heart rate have been explored as potential biomarkers of stress, their predictive utility in high-stress environments such as the intensive care unit (ICU) remains inconclusive, especially when factoring in atypical events that can further exacerbate resident stress levels.
Objective: This study aimed to investigate the relationship between daily average heart rate (AHR) and perceived stress among ICU residents and examine the moderating effect of atypical events on this relationship.
Ulus Travma Acil Cerrahi Derg
September 2025
Department of Thoracic Surgery, Dr. Siyami Ersek Thoracic and Cardiovascular Surgery Training and Research Hospital, Istanbul-Türkiye.
Background: This study aims to evaluate the performance of ChatGPT-4o in thoracic trauma management by comparing its responses to established clinical guidelines.
Methods: Five major thoracic surgery guidelines were reviewed, including the Advanced Trauma Life Support (ATLS) Guidelines 2018, Eastern Association for the Surgery of Trauma (EAST) Guidelines 2020, Evaluation and management of traumatic pneumothorax: A Western Trauma Association critical decisions algorithm 2022, European Trauma Course (ETC) Guidelines 2016, and the National Institute for Health and Care Excellence (NICE) Guidelines for Trauma 2020. Fifty open-ended questions were developed based on these guidelines and submitted to ChatGPT-4o.