Radiology Professionals Research Articles

Characterization of X-ray Beams: Determination of K\(_{a,e}\) for Comparison with DRLs

Aims: The aim of this experimental study was to quantify the entrance surface air kerma (Ka,e) of X-ray beams in radiographic examinations, and the obtained values were compared with national and international Diagnostic Reference Levels (DRLs). Place and Duration of Study: Sample: Department of Medical Physics and Radiology, Franciscan University (UFN), between June 2023 and August 2024. Methodology: Initially, the main quality control (QC) tests were applied to characterize the radiographic equipment. During the evaluation of the X-ray beam, a dosimetric set was used, and for the same QC geometry, Air Kerma Rate (KAIR) measurements were obtained to determine the output of the X-ray tube. Under the same conditions, a phantom without water and the same filled with water were used to determine the backscatter factor (BSF), which was then used to estimate the Ka,e for the main radiographic examinations. Results: The results of this study are consistent with other studies and identified the region/anatomy with the highest radiation exposure, highlighting the importance of its optimization and verifying that continued professional training should be integrated into best practices to ensure greater safety in procedures. The results obtained evidence the necessity for adjustments in image acquisition protocols, as the Ka,e values found in this study, although within the limits established by the national DRLs, exceed the reference values of countries such as the United Kingdom and Japan in the areas of the skull, abdomen, and lumbar spine. This discrepancy highlights the importance of the periodic review and update of DRLs, in accordance with international recommendations and the technological evolution of equipment. Dose optimization is an ongoing process that involves the evaluation of image quality, the appropriate selection of technical parameters, and the implementation of specific dose protocols. The continuous training of radiology professionals is essential to ensure the correct application of imaging techniques and the minimization of patient exposure to ionizing radiation. Conclusion: This study shows the importance of evaluating radiation dose in diagnostic imaging and the need for a multidisciplinary approach to dose optimization. Implementing quality assurance programs, updating DRLs, and providing ongoing training for professionals are essential to ensure patient safety and high-quality diagnostic imaging. The methodology adopted in this study can contribute to the ongoing education of radiology professionals, promoting the correct application of imaging techniques and reducing patient exposure to ionizing radiation.

The aftermath of COVID-19: generalized anxiety disorder and burnout among radiology practitioners and interns in Saudi Arabia.

The coronavirus disease (COVID-19) pandemic has presented unprecedented stressors and difficulties for healthcare professionals. This study explored the prevalence of generalized anxiety disorders and burnout among radiology practitioners and interns in various hospitals in Saudi Arabia after the end of the COVID-19 global public health emergency. A cross-sectional survey of 230 radiology practitioners and interns was conducted between October and November 2023. This study utilized the Generalized Anxiety Disorder 7-item (GAD-7) scale and Maslach Burnout Inventory-Human Services Survey for Medical Personnel (MBI-HSS-MP) 22-item questionnaire, employing a non-probability convenience sampling method. The average scores of the individual components constituting the GAD-7 scale and each burnout scale were calculated, and statistical analyses were conducted using the Mann-Whitney U and Kruskal-Wallis H nonparametric tests. Of 382 radiology practitioners and interns, 230 (60.2%) responded to the survey. Notably, 42.6% of the participants reported experiencing GAD. Regarding burnout, 82.3% were at moderate-to-high risk for emotional exhaustion, 93.5% for depersonalization, and 52.1% for personal achievement. The 31-40 years age group showed significantly higher burnout rates (p = 0.001) compared with the other age groups. Those with more than three years of experience had notably higher emotional exhaustion scores (p = 0.002) and a nearly significant increase in depersonalization scores (p = 0.051) than those with less experience. Our study revealed that 42.6% of radiology practitioners and interns experienced GAD, with the majority facing significant burnout. Furthermore, our research indicates a decline in GAD levels among radiology practitioners and interns compared with the peak COVID-19 pandemic period. It also showed a significant increase in both the incidence and severity of burnout, surpassing pre-pandemic levels in a comparable cohort. These findings emphasize the pressing challenges of GAD and burnout among healthcare workers, especially radiology professionals.

Evaluation of the Impact of Artificial Intelligence on Clinical Practice of Radiology in Saudi Arabia.

Artificial Intelligence (AI) is becoming integral to the health sector, particularly radiology, because it enhances diagnostic accuracy and optimizes patient care. This study aims to assess the awareness and acceptance of AI among radiology professionals in Saudi Arabia, identifying the educational and training needs to bridge knowledge gaps and enhance AI-related competencies. This cross-sectional observational study surveyed radiology professionals across various hospitals in Saudi Arabia. Participants were recruited through multiple channels, including direct invitations, emails, social media, and professional societies. The survey comprised four sections: demographic details, perceptions of AI, knowledge about AI, and willingness to adopt AI in clinical practice. Out of 374 radiology professionals surveyed, 45.2% acknowledged AI's significant impact on their field. Approximately 44% showed enthusiasm for AI adoption. However, 58.6% reported limited AI knowledge and inadequate training, with 43.6% identifying skill development and the complexity of AI educational programs as major barriers to implementation. While radiology professionals in Saudi Arabia are generally positive about integrating AI into clinical practice, significant gaps in knowledge and training need to be addressed. Tailored educational programs are essential to fully leverage AI's potential in improving medical imaging practices and patient care outcomes.

Optimizing radiology remote reading: leveraging technology to improve efficiency.

Remote work has been increasingly utilized in the profession of radiology over recent years. Setting up your individual workstation offers an opportunity to tailor it to suit your preferences without the restriction of a universal setup to accommodate multiple users. Important considerations when setting up a home workstation include selecting the optimal work location, choosing the proper desk and chair, and configuring an ideal computer monitor layout. The use of peripheral devices, such as programmable mice and hands-free dictation tools can improve efficiency and reduce repetitive strain injuries. This article also explores the use of smart home devices and programmable scripts using AutoHotKey to further streamline workflow and maximize the benefits of a remote workstation.

Canadian Medical Imaging Inventory 2022–2023: Provincial and Territorial Overview

The average age of imaging equipment in Canada has increased for all imaging modalities over the past 2 decades. Most imaging equipment is more than 5 years old, and at least one-third of equipment is more than 10 years old. Investing in new equipment to meet growing demand may not increase imaging capacity without also considering staffing. There is no current international benchmark for the optimal number of imaging units per population, but there is a general assumption that too few units may limit access and increase wait times while too many units may encourage low-value imaging. Canada remains below the average for Organisation for Economic Co-operation and Development (OECD) countries and is positioned in the bottom 30% of OECD countries in units per million population for CT, MRI, and PET-CT. Canada is positioned in the bottom 50% of OECD countries for volume of publicly funded CT, MRI, and PET-CT exams per 1,000 population. Since 2012, both the overall numbers of MRI, PET-CT, and SPECT-CT units and the numbers of MRI, PET-CT, and SPECT-CT units per million people have grown. The number of units for all modalities operating in Canada has increased since 2019–2020, except for SPECT units. In most provinces, the number of SPECT units per population decreased, indicating that population growth outpaced installation. The imaging workforce is under strain. Clearing the backlog of exams deferred during the pandemic has exacerbated existing staffing shortages. New investment in radiology staffing, particularly imaging technologists, is required, including improved recruitment and retention policies. Compared to the prepandemic period, there are now fewer full-time radiology professionals in practice across Canada, with medical physicists experiencing the largest decline per million population. Wait times for medical imaging remain above the recommended maximum wait time in many jurisdictions and are influenced by a variety of factors. The adoption of supportive tools and technologies — such as clinical decision support tools, automated order entry, and AI-driven solutions — can assist the workforce, add value to imaging services, and increase access to medical imaging.

Evaluating the Pros and Cons of Evening and Weekend Outpatient Medical Imaging: Implications for Patients and Radiology Professionals

Evening and weekend imaging services at outpatient radiology centers offer extended access to diagnostic imaging, potentially increasing patient satisfaction and accessibility, especially for underserved populations. This review explores the benefits and challenges associated with these after-hours operations, focusing on health equity, patient satisfaction, economic considerations, energy and cost savings, and the impact on healthcare professionals. Findings indicate that while after-hours operations can enhance patient satisfaction and access, they also pose challenges such as increased operational costs and staff fatigue. Strategies for improvement include optimizing shift schedules, leveraging technology for better scheduling and communication, and enhancing patient-centered care. Collaborative efforts among imaging centers can further improve service delivery and efficiency.

Work autonomy and its associated factors among health professionals in public hospitals of North East Ethiopia

A low level of work autonomy is the bottleneck for the health service delivery and the quality of the service. Although work autonomy is the pillar of organizational commitment and a means of employee retention mechanism, information about the magnitude of work autonomy among health professionals is limited in Ethiopia. Therefore, this study aimed to assess work autonomy and its predictors among health professionals working in public hospitals of Northeast Ethiopia. Institution-based cross-sectional study was conducted from March 24 to April 24, 2021, among health professionals using a stratified sampling technique. Variables with a p-value of < 0.25 in bivariable analysis were included in the multivariable analysis and variables with a p-value of < 0.05 in multivariable analysis were regarded as significantly associated factors. The overall good work autonomy in public hospitals (Dessie and Boru Meda Hospital) of North East Ethiopia was 54.5% (95% CI 54.48–54.53). Satisfaction with organizational policy and strategy (AOR 2.34, 95% CI 1.29–4.25), satisfaction with supervisor support (AOR 7.20, 95% CI 3.97–13.07), good health service delivery planning practice (AOR 1.88, 95%CI: 1.13–3.13), being married (AOR 4.26, 95%CI: 2.06–8.82) being pharmacy professionals (AOR 0.44, 95% CI 0.19–0.98), and being anesthesia and radiology professionals (AOR 4.66, 95% CI 1.65–13.19) were significantly associated with work autonomy of health professionals. More than half of the health professionals working in public hospitals in Northeast Ethiopia are autonomous in their work. Satisfaction with organizational policy and strategy, satisfaction with supervisor support, having good health service delivery planning practice, being married, and type of profession were significantly associated factors in public hospitals. Thus, strengthening strategies aimed at shaping poor health service delivery planning practices and dissatisfaction of employees concerning supervisor support and organizational policy might have a substantial contribution to improving the work autonomy of health professionals.

Radiologists’ perceptions on AI integration: An in-depth survey study

PurposeTo assess the perceptions and attitudes of radiologists toward the adoption of artificial intelligence (AI) in clinical practice. MethodsA survey was conducted among members of the SIRM Lombardy. Radiologists’ attitudes were assessed comprehensively, covering satisfaction with AI-based tools, propensity for innovation, and optimism for the future. The questionnaire consisted of two sections: the first gathered demographic and professional information using categorical responses, while the second evaluated radiologists’ attitudes toward AI through Likert-type responses ranging from 1 to 5 (with 1 representing extremely negative attitudes, 3 indicating a neutral stance, and 5 reflecting extremely positive attitudes). Questionnaire refinement involved an iterative process with expert panels and a pilot phase to enhance consistency and eliminate redundancy. Exploratory data analysis employed descriptive statistics and visual assessment of Likert plots, supported by non-parametric tests for subgroup comparisons for a thorough analysis of specific emerging patterns. ResultsThe survey yielded 232 valid responses. The findings reveal a generally optimistic outlook on AI adoption, especially among young radiologist (<30) and seasoned professionals (>60, p<0.01). However, while 36.2 % (84 out 232) of subjects reported daily use of AI-based tools, only a third considered their contribution decisive (30 %, 25 out of 84). AI literacy varied, with a notable proportion feeling inadequately informed (36 %, 84 out of 232), particularly among younger radiologists (46 %, p < 0.01). Positive attitudes towards the potential of AI to improve detection, characterization of anomalies and reduce workload (positive answers > 80 %) and were consistent across subgroups. Radiologists’ opinions were more skeptical about the role of AI in enhancing decision-making processes, including the choice of further investigation, and in personalized medicine in general. Overall, respondents recognized AI’s significant impact on the radiology profession, viewing it as an opportunity (61 %, 141 out of 232) rather than a threat (18 %, 42 out of 232), with a majority expressing belief in AI’s relevance to future radiologists’ career choices (60 %, 139 out of 232). However, there were some concerns, particularly among breast radiologists (20 of 232 responders), regarding the potential impact of AI on the profession. Eighty-four percent of the respondents consider the final assessment by the radiologist still to be essential. ConclusionOur results indicate an overall positive attitude towards the adoption of AI in radiology, though this is moderated by concerns regarding training and practical efficacy. Addressing AI literacy gaps, especially among younger radiologists, is essential. Furthermore, proactively adapting to technological advancements is crucial to fully leverage AI’s potential benefits. Despite the generally positive outlook among radiologists, there remains significant work to be done to enhance the integration and widespread use of AI tools in clinical practice.

Studying the history of histopathology in preclinical medical education as a guide for the uniform integration of radiology in medical education

Radiology and pathology, though distinct fields within medical education, share a common foundation in their essential roles for accurate diagnosis and understanding of diseases. While pathology, particularly histopathology, has long been integrated into preclinical medical education in the United States, radiology education has traditionally been less emphasized. This paper examines the historical development of histopathology training in medical education and its central role, contrasting it with the comparatively peripheral position of radiology education. We explore the historical context of medical education in the United States, tracing the integration of histopathology following the Flexner Report of 1910. In contrast, radiology, emerging later as a specialized field, has faced challenges in achieving comparable integration into medical curricula. Despite the increasing importance of medical imaging in diagnosis and treatment, radiology education remains variable and often lacking in standardization across medical schools. We highlight the need for greater emphasis on radiology education to better prepare medical students for modern clinical practice, where medical imaging plays an increasingly pivotal role. A call for a comprehensive assessment of radiology education and advocacy for its integration into preclinical curricula is made, emphasizing the importance of collaboration between the radiology profession and accrediting bodies to ensure competence in imaging across medical specialties. As medical imaging continues to advance and become more integral to healthcare, it is imperative that medical education reflects this evolution by establishing radiology as a fundamental component of preclinical training.

Knowledge level of the community and healtcare workers about radiological examinations and harmful effects of radiation: a review

Patients, doctors, and radiology professionals are exposed to ionizing radiation in the diagnostic and therapeutic applications of radiology. Despite the proven harmful effects of medical radiological methods used for diagnosis and treatment, it is not possible to completely abandon these procedures. There is a lack of knowledge in the community about radiological examinations, the effects of radiation, and radiation protection. To increase awareness about radiation, it is essential to educate patients, enhance the education of healthcare students, and provide in-service training for all hospital staff. This way, knowledge levels can be improved, and the harmful effects of radiation can be minimized.

Online teaching of radiation protection to health professionals: Lessons learned

The study examines the online teaching of radiation protection to health professionals. Training in Radiological Protection is considered a basic requirement for radiology professionals and students in Health Sciences. Over the past 22 years, various educational materials, including videos and teaching resources, have been produced following the Guidelines on education and training in radiological protection for medical exposures of the European Commission. These materials have been utilized in various online learning modalities. The objective of this work is to assess the impact of this study on student training. To achieve this, different data collection processes have been carried out, according to the period, and have been analyzed. The results of this training have been analyzed through diverse methods, demonstrating adequate acquisition of basic theoretical contents and a positive acceptance of this methodology by the 6,518 students examined. However, the need to create original specialized materials and resources, the increase in the teacher's workload, and the lack of institutional recognition of the teacher's effort limit its development. On the other hand, it can also be concluded that the acquisition of practical skills by the student is a weakness of the online learning system that requires the incorporation of the teacher in a face-to-face or traditional manner in the delivery of these specialized practical contents. This study allows for a better utilization of the available tools in the training of students in this subject.

Radiologists and trainees’ perspectives on artificial intelligence

Background and objectivesThe purpose of this study was to investigate perspectives held by radiologists on the use of artificial intelligence (AI) in their day-to-day work and to identify factors limiting its routine implementation. Materials and methodsSpanish board-certified radiologists and trainees completed an online survey of 21 questions on general information and communications technology (ICT) and AI in radiology. Analysis was carried out for the subgroups of gender, age, and professional experience. Associations with a p-value <0.05 were considered statistically significant. ResultsA total of 102 radiologists and trainees completed the questionnaire. No se observaron diferencias estadísticas significativas entre los grupos de sexo. A significant difference was detected in ICT and AI knowledge between age groups, with participants under 40 and those between 40 and 55 years old demonstrating better ICT knowledge (p < 0.01). The survey results revealed that 77.4% of participants believed that AI represents an opportunity for the radiology profession in the future, while 9.8% believed it would have no impact. Three main practical application areas for AI in radiology were proposed: in screening (23.36%), in image interpretation and reporting (21.17%), and in the requesting of imaging and patient scheduling (14.6%). The biggest concern among the surveyed population was the potential increase in workload. ConclusionsA positive attitude toward AI was observed among Spanish radiologists, with the majority believing that AI could offer opportunities for the radiology profession in the near future. AI training programmes may further improve its acceptance among professionals.

A comprehensive review and experimental comparison of deep learning methods for automated hemorrhage detection

Hemorrhagic stroke poses a critical medical emergency that necessitates prompt and accurate diagnosis to prevent irreversible brain damage. The emergence of automated deep learning methods for identifying hemorrhagic stroke on medical imaging scans has garnered significant interest in recent years. These methods are specifically designed to assist radiologists in identifying and highlighting abnormalities in medical scans. In this study, we conducted a systematic review and comprehensive gap analysis of various deep learning methodologies for automated hemorrhagic stroke identification. Our experiments were conducted using various datasets, particularly a publicly available large dataset of non-contrast computed tomography scans of the brain, in a regulated setting. Through an evaluation strategy that encompasses unexplored domain aspects, we identified the key challenges and areas for improvement. This strategy involved the utilization of multisite datasets and the evaluation based on the complexity in the appearance of hemorrhage. The subsequent analysis of the evaluation results revealed gaps in the current state of the art, emphasizing the need to bridge these gaps to achieve higher accuracy and reliability. This study offers valuable insights into the current state of automated hemorrhage stroke identification, highlighting areas for future research and development. The findings have significant implications for improving patient outcomes and alleviating the burden on radiology professionals, leading to more sustainable healthcare practices. The study concludes with recommendations for researchers in this field to foster the future development of automated tools for diagnosing hemorrhagic stroke.

Protocol parameter extraction and centralization framework for comprehensive and in-depth CT protocol review and management.

CT protocol management is an arduous task that requires expertise from a variety of radiology professionals, including technologists, radiologists, radiology IT professionals, and medical physicists. Each CT vendor has unique, proprietary protocol file structures, some of which may vary by scanner model, making it difficult to develop a universal framework for distilling technical parameters to a human-readable file format. An ideal solution for CT protocol management is to minimize the work required for parameter extraction by introducing a data format into the workflow that is universal to all CT scanners. In this paper, we report a framework for CT protocol management that converts raw protocol files to an intermediary format before outputting them in a human-readable format for a variety of practical clinical applications, including routine protocol review, protocol version tracking, and cross-protocol comparisons. The framework was developed in Python 3. Technical parameters of interest were determined via collaborative effort between medical physicists and lead technologists. Protocol files were extracted and analyzed from a variety of scanners across our hospital-wide CT fleet, including various systems from Siemens and GE. Protocols were subcategorized based on relevant technical parameters into regular, dual-energy, and cardiac CT protocols. Backend code for technical parameter extraction from raw protocol files to a JavaScript Object Notation (JSON) format was performed on a per-system basis. Conversion from JSON to a readable output format (MS Excel) was performed identically for all scanners using the universal framework developed and presented in this work. Example results for Siemens and GE scanners are shown, including side-by-side comparisons for protocols with similar clinical indications. In conclusion, our CT protocol management framework may be deployed on any CT system to improve clinical efficiency in protocol review and upkeep.

Radiology Professionals’ Knowledge, Attitude, Perception and Awareness of Artificial Intelligence in Medical Radiology and Imaging Technology

Artificial intelligence (AI) has rapidly emerged as a transformative force in the field of medical radiology and imaging technology. The advent of artificial intelligence (AI) has introduced transformative changes across various fields, including healthcare. Within medical radiology and imaging technology, AI has shown tremendous potential in enhancing diagnostic accuracy, improving workflow efficiency, and ultimately optimizing patient care. Advanced algorithms and machine learning models are increasingly being integrated into imaging modalities to aid in the detection, classification, and quantification of pathologies, offering a powerful adjunct to traditional radiological practices.

Radiographers' workload and burnout on performance: an empirical study.

To assess the prevalence of burnout among radiographers, and whether demographic variables and work-related factors had any influence on burnout and perceived stress among them. A cross-sectional quantitative survey design is adopted in this study. The participants included radiographers from Saudi Arabia. Both Maslach Burnout Inventory (MBI) and Perceived Stress Scale (PSS) were used for data collection. Participation was voluntary, and the survey was conducted online, resulting in 322 final responses considered for the data analysis. The mean emotional exhaustion (EE) score achieved was 26.01, representing medium burnout risk. However, the mean depersonalization (DP: μ = 25.25) and personal accomplishment (PA: μ = 23.65) represented high burnout risk among radiographers. Statistically significant differences (p < 0.05) were observed among the participants grouped by genders, age groups, nature of work type, and work experience. The mean perceived stress score for radiographers was identified to be 27.8, indicating high. The findings underscore the critical need for targeted interventions and support mechanisms within the radiology profession, particularly focusing on younger radiographers and those with extensive work experience.

Position Statements of the Emerging Trends Committee of the Asian Oceanian Society of Radiology on the Adoption and Implementation of Artificial Intelligence for Radiology.

Artificial intelligence (AI) is rapidly gaining recognition in the radiology domain as a greater number of radiologists are becoming AI-literate. However, the adoption and implementation of AI solutions in clinical settings have been slow, with points of contention. A group of AI users comprising mainly clinical radiologists across various Asian countries, including India, Japan, Malaysia, Singapore, Taiwan, Thailand, and Uzbekistan, formed the working group. This study aimed to draft position statements regarding the application and clinical deployment of AI in radiology. The primary aim is to raise awareness among the general public, promote professional interest and discussion, clarify ethical considerations when implementing AI technology, and engage the radiology profession in the ever-changing clinical practice. These position statements highlight pertinent issues that need to be addressed between care providers and care recipients. More importantly, this will help legalize the use of non-human instruments in clinical deployment without compromising ethical considerations, decision-making precision, and clinical professional standards. We base our study on four main principles of medical care-respect for patient autonomy, beneficence, non-maleficence, and justice.

Quality and Image Processing Analysis versus Radiation Dose in Hip Radiographic Examinations

Aims: Evaluate the quality and processing of images in hip radiographic examinations.&#x0D; Study Design: Six combinations of exposure techniques commonly used in clinical practice were used, keeping the voltage (kVp) value constant, varying only the current and time relationship (mA.s).&#x0D; Place and Duration of Study: The study was carried out at the Radiodiagnosis Laboratory of the Radiology and Medical Physics graduation courses at the Franciscan University (UFN), as part of the research developed during the Image Processing course in the second semester of 2023.&#x0D; Methodology: Image acquisition and radiation dose measurement were performed using clinical radiographic equipment and a model resembling hip anatomy, along with a dosimetric system to quantify radiation dose at the entrance surface (ESAK). The captured images were digitized through a computerized radiography (CR) system, enabling quantitative analysis using histograms and Regions of Interest (ROI) in anatomical structures relevant for diagnosis. Signal and noise values were evaluated, allowing the determination of the signal-to-noise ratio (SNR) and radiographic contrast (CR).&#x0D; Results: The results revealed significant differences among the various techniques employed. Some images displayed lower SNR, ranging from 8.14% to 15.60%, yet with higher CR, varying between 5.48% and 22.29%. Remarkably, one technique (70 kVp with 16 mA.s) demonstrated the best cost-benefit relationship, reducing radiation exposure by 48.7% (from 6.32 to 3.24 mGy) with minimal reduction in SNR, less than 10%, and a 5.47% increase in CR.&#x0D; Conclusion: These findings highlight the importance for radiology professionals seeking imaging techniques that guarantee high diagnostic quality with minimal patient exposure. Prioritizing safer and more effective radiological practices is essential for delivering quality healthcare services.

The Impact of Artificial Intelligence on the Future of Radiographer Career in Saudi Arabia

The emergence of artificial intelligence (AI) technologies in the healthcare imaging domain has heightened discourse regarding the responsibilities and role of radiologists, thereby bringing this issue to the fore. On the other hand, the extent to which this kind of equipment will alter the radiographer's job has not been much discussed. To examine the potential effects of AI on the radiography job, this study assesses current processes and cross-maps potential areas for automation, such as procedure organizing, picture capture, and processing images. We also draw attention to the advantages that AI offers, such as improved patient care, greater technology skills, elevated cross-modality learning and teamwork, and an extension of radiographer duties into AI-supported picture auditing and reporting roles. For the purpose of incorporating AI systems into medical imaging, radiologists are crucial because they serve as a liaison between people and technological advances. Studies have looked at the knowledge, beliefs, and attitudes of Saudi Arabian radiology professionals toward AI to investigate how radiologists react toward AI in radiology and to find out their opinions about AI in the future of radiology.

Radiological investigations in nephrolithiasis and: a narrative review

Nephrolithiasis and ureterolithiasis are increasing in incidence and prevalence worldwide, which are significant clinical challenges in management. Radiological assessments are vital in early diagnosis and effective management to decrease morbidity and healthcare costs. This narrative review explores the role of various radiological investigations in nephrolithiasis and ureterolithiasis, focusing on their clinical implications and limitations. Plain X-ray of the kidney, ureter, and bladder (X-ray KUB) is a widely available, relatively inexpensive modality with limited sensitivity, mainly for smaller stones. However, it is most beneficial when assessing follow-up patients diagnosed with renal or ureteric calculi, but it is less effective in acute ureteric colic. Intravenous Urogram/Intravenous Pyelography (IVU/IVP) is an obsolete investigation and has largely been replaced by newer modalities due to numerous drawbacks. Ultrasonography (USG) is a widely available, relatively lowcost, non-invasive radiological modality without ionising radiation, considered first-line for children and pregnant patients. However, its sensitivity and specificity are traditionally lower than computed tomography and largely depend on the operator and patient factors. Computed tomography kidney, ureter, and bladder (CT-KUB) is the gold standard for diagnosing urolithiasis. It offers high sensitivity, specificity, and the ability to calculate the exact size and stone composition, but it comes with substantial radiation exposure. However, low-dose and ultralow-dose CT (LDCTKUB) protocols reduce radiation to the patient significantly, compromising image clarity. Magnetic Resonance Urography (MRU) is a second-line investigation in obstructive uropathy, particularly in pregnancy and children. It provides vital anatomical and functional information without ionising radiation. Urology and radiology professionals should collaborate to identify individualised and optimal radiological investigations, considering the risks and benefits associated with each modality.