Impact of long duty hours on education and well-being of diagnostic radiology residents: A national survey in Saudi Arabia

Objectives: The primary purpose of this study was to assess and report the perceived negative impact of long duty hours on education and personal well-being among medical trainees in the diagnostic radiology residency training program in Saudi Arabia. Methods: This cross-sectional study used a questionnaire (sent by email) with eight indicators related to the education and well-being of radiology residents in Saudi Arabia during the academic year 2019–2020. Participants were given a five-point Likert response format for each indicator. The relative importance index (RII) was calculated to rank the different indicators. Results: Our of 337 residents, 116 diagnostic radiology trainees completed the survey, with a response rate of 34.4%. A total of 102 (87.9%) indicated their preference for 16-hour shifts instead of the currently implemented 24-hour duty system. Using the RII, three items related to the post-duty day ranked at the top of the list. The negative impact on sleep rhythm during the post-call day ranked first (mean 4.23 ± 1.02, RII 0.84), followed by the impact on social life, family activities, and exercise during the post-call day (mean 4.09 ± 1.06, RII 0.81). The third highest ranking factor was missing academic activities on the post-call day (mean 3.91 ± 1.15, RII 0.78). There was no relationship between negative perception and gender (P > 0.05). Conclusion: The 24-hour duty system had a negative impact on radiology residents’ education and personal well-being, especially for items related to the post-call day. Reforming duty hours should be considered to promote residents’ well-being. doi: https://doi.org/10.12669/pjms.38.3.4440 How to cite this:Alhasan AS, Alahmadi SM, Altayeb YA, Daqqaq TS. Impact of long duty hours on education and well-being of diagnostic radiology residents: A national survey in Saudi Arabia. Pak J Med Sci. 2022;38(3):---------. doi: https://doi.org/10.12669/pjms.38.3.4440 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Diagnostic effect of artificial intelligence solution for referable thoracic abnormalities on chest radiography: a multicenter respiratory outpatient diagnostic cohort study

Objectives We aim ed to evaluate a commercial artificial intelligence (AI) solution on a multicenter cohort of chest radiographs and to compare physicians' ability to detect and localize referable thoracic abnormalities with and without AI assistance. Methods In this retrospective diagnostic cohort study, we investigated 6,006 consecutive patients who underwent both chest radiography and CT. We evaluated a commercially available AI solution intended to facilitate the detection of three chest abnormalities (nodule/masses, consolidation, and pneumothorax) against a reference standard to measure its diagnostic performance. Moreover, twelve physicians, including thoracic radiologists, board-certified radiologists, radiology residents, and pulmonologists, assessed a dataset of 230 randomly sampled chest radiographic images. The images were reviewed twice per physician, with and without AI, with a 4-week washout period. We measured the impact of AI assistance on observer's AUC, sensitivity, specificity, and the area under the alternative free-response ROC (AUAFROC). Results In the entire set (n = 6,006), the AI solution showed average sensitivity, specificity, and AUC of 0.885, 0.723, and 0.867, respectively. In the test dataset (n = 230), the average AUC and AUAFROC across observers significantly increased with AI assistance (from 0.861 to 0.886; p = 0.003 and from 0.797 to 0.822; p = 0.003, respectively). Conclusions The diagnostic performance of the AI solution was found to be acceptable for the images from respiratory outpatient clinics. The diagnostic performance of physicians marginally improved with the use of AI solutions. Further evaluation of AI assistance for chest radiographs using a prospective design is required to prove the efficacy of AI assistance. Key Points • AI assistance for chest radiographs marginally improved physicians’ performance in detecting and localizing referable thoracic abnormalities on chest radiographs. • The detection or localization of referable thoracic abnormalities by pulmonologists and radiology residents improved with the use of AI assistance.

Assessment of the Awareness of Radiology Residents About Ionizing Radiations and Their Risks of Educational Hospitals, Kermanshah, Iran

Background: Although many radiological examinations are requested daily to aid clinical diagnosis by the referring physician, it should be kept in mind that ionizing radiation has adverse biological effects on the life of living organisms, which may vary in individuals depending on the dose and duration of exposure. Therefore, radiologists and their assistants should have comprehensive information about ionizing radiation. Methods: In this descriptive cross-sectional study, all the radiology residents (41) were included in the study. The questionnaire consisted of two parts, the first part including of demographic information and part 2 includes 21 questions about the effective dose created by a chest X-ray, the approximate effective dose from various imaging examines, as well as the awareness of the risks of brain, abdominal CT scans. Results: The results showed that out of 41 residents, 56.1% were male. Based on these results, 19.5% were aware of the approximate effective dose received by a patient on PA chest X-ray. Also, knowledge of some X-ray absorptiometry parameters based on chest X-ray in PA position are: 12.2% abdominal CT scan, 17.1% and 2.4% brain and chest CT scan respectively. In this study the radiology resident’s awareness about dangers of ionizing radiation and the likelihood of cancer were evaluated in 31% and 48.8% of brain and abdominal CT scan, respectively. Conclusions: According to the findings, awareness of radiology residents in Kermanshah university of medical sciences is at an inappropriate level. Therefore, training is needed to raise the awareness of radiology residents by conducting several workshops.

Factors Influencing Trainees’ Interest in Breast Imaging

Purpose: To gauge the level of interest in breast imaging (BI) and determine factors impacting trainees’ decision to pursue this subspecialty. Methods: Canadian radiology residents and medical students were surveyed from November 2020 to February 2021. Training level, actual vs preferred timing of breast rotations, fellowship choices, perceptions of BI, and how artificial intelligence (AI) will impact BI were collected. Chi-square, Fisher’s exact tests and univariate logistic regression were performed to determine the impact of trainees’ perceptions on interest in pursuing BI/women’s imaging (WI) fellowships. Results: 157 responses from 80 radiology residents and 77 medical students were collected. The top 3 fellowship subspecialties desired by residents were BI/WI (36%), abdominal imaging (35%), and interventional radiology (25%). Twenty-five percent of the medical students were unsure due to lack of exposure. The most common reason that trainees found BI unappealing was repetitiveness (20%), which was associated with lack of interest in BI/WI fellowships (OR = 3.9, 95% CI: 1.6-9.5, P = .002). The most common reason residents found BI appealing was procedures (59%), which was associated with interest in BI/WI fellowships (OR, 3.2, 95% CI, 1.2-8.6, P = .02). Forty percent of residents reported an earlier start of their first breast rotation (PGY1-2) would affect their fellowship choice. Conclusion: This study assessed the current level of Canadian trainees’ interest in BI and identified factors that influenced their decisions to pursue BI. Solutions for increased interest include earlier exposure to breast radiology and addressing inadequacies in residency training.

Three-dimensional simulator: training for beginners in endovascular embolization with liquid agents

Background To design a simulator for novices without prior experience in embolization with liquid agents such as n-Butyl cyanoacrylate (n-BCA) and to evaluate the simulator using surveys and post hoc video analysis. Materials and methods The simulator was created using computer-aided design software and three-dimensionally printed. Before an embolization, trainees completed questionnaires regarding their level of expertise and self-reported confidence level. The participants were shown an instruction video and each participant performed four embolizations on the simulator. Subsequently, the participants completed surveys on self-reported confidence level and assessed the simulator’s face and content validity. Results Five experts and twelve novices trained on the simulator. The experts were radiology residents and fellows with at least 5 years of work experience in interventional radiology. The novices were medical students and radiology residents without any previous experience with embolization. Based on the surveys, the experts assessed the simulator as very useful for embolization training. Performance, e.g. mean duration embolization between experts (mean ± standard deviation = 189 ± 42 s) and novices (mean ± standard deviation = 235 ± 66 s) were significantly different (p = .001). The overall simulation of the embolization process, simulated complications, and educational capabilities of the simulator were evaluated positively. In the novice group the self-reported confidence level significantly increased (p = .001). Conclusion The liquid embolization simulator proposed here is a suitable educational tool for training embolization procedures. It reduces the duration of embolization procedures and improves the confidence level of beginners in embolization.

Interpretation of Thoracic Radiography Shows Large Discrepancies Depending on the Qualification of the Physician—Quantitative Evaluation of Interobserver Agreement in a Representative Emergency Department Scenario

(1) Background: Chest radiography (CXR) is still a key diagnostic component in the emergency department (ED). Correct interpretation is essential since some pathologies require urgent treatment. This study quantifies potential discrepancies in CXR analysis between radiologists and non-radiology physicians in training with ED experience. (2) Methods: Nine differently qualified physicians (three board-certified radiologists [BCR], three radiology residents [RR], and three non-radiology residents involved in ED [NRR]) evaluated a series of 563 posterior-anterior CXR images by quantifying suspicion for four relevant pathologies: pleural effusion, pneumothorax, pneumonia, and pulmonary nodules. Reading results were noted separately for each hemithorax on a Likert scale (0–4; 0: no suspicion of pathology, 4: safe existence of pathology) adding up to a total of 40,536 reported pathology suspicions. Interrater reliability/correlation and Kruskal–Wallis tests were performed for statistical analysis. (3) Results: While interrater reliability was good among radiologists, major discrepancies between radiologists’ and non-radiologists’ reading results could be observed in all pathologies. Highest overall interrater agreement was found for pneumothorax detection and lowest agreement in raising suspicion for malignancy suspicious nodules. Pleural effusion and pneumonia were often suspected with indifferent choices (1–3). In terms of pneumothorax detection, all readers mainly decided for a clear option (0 or 4). Interrater reliability was usually higher when evaluating the right hemithorax (all pathologies except pneumothorax). (4) Conclusions: Quantified CXR interrater reliability analysis displays a general uncertainty and strongly depends on medical training. NRR can benefit from radiology reporting in terms of time efficiency and diagnostic accuracy. CXR evaluation of long-time trained ED specialists has not been tested.