Field Of Radiology Research Articles

Artificial intelligence in fracture detection on radiographs: a literature review.

Fractures are one of the most common reasons of admission to emergency department affecting individuals of all ages and regions worldwide that can be misdiagnosed during radiologic examination. Accurate and timely diagnosis of fracture is crucial for patients, and artificial intelligence that uses algorithms to imitate human intelligence to aid or enhance human performs is a promising solution to address this issue. In the last few years, numerous commercially available algorithms have been developed to enhance radiology practice and a large number of studies apply artificial intelligence to fracture detection. Recent contributions in literature have described numerous advantages showing how artificial intelligence performs better than doctors who have less experience in interpreting musculoskeletal X-rays, and assisting radiologists increases diagnostic accuracy and sensitivity, improves efficiency, and reduces interpretation time. Furthermore, algorithms perform better when they are trained with big data on a wide range of fracture patterns and variants and can provide standardized fracture identification across different radiologist, thanks to the structured report. In this review article, we discuss the use of artificial intelligence in fracture identification and its benefits and disadvantages. We also discuss its current potential impact on the field of radiology and radiomics.

Leveraging Virtual Containers for High-Powered, Collaborative AI Research in Radiology

Abstract Numerous obstacles confront radiologists interested in the use of artificial intelligence (AI) models within the field of radiology. For example, discrepancies between the radiologist's and an AI developer's hardware and software specifications pose a substantial hindrance to using AI models. Additionally, accessing and using GPU computers can lead to compatibility issues and add to these challenges. Finally, the dissemination of AI models and the ability to download preexisting AI models are not simple tasks due to the size and complexity of most programs. Virtual containers offer a solution to such compatibility issues and provide a simplified way for radiologists to use AI models. Virtual containers are software tools that bundle code, required programs, and necessary software packages to ensure that a program runs identically for all users, regardless of their computing environment. This article outlines the features of virtual containers (compatibility, versatility, and portability) and highlights an applied use case for virtual containers in the development of an AI model.

Artificial intelligence in the diagnosis of cerebrovascular diseases using magnetic resonance imaging: A scoping review

AbstractThe field of radiology is currently undergoing revolutionary changes owing to the increasing application of artificial intelligence (AI). This scoping review identifies and summarizes the technical methods and clinical applications of AI applied to magnetic resonance imaging of cerebrovascular diseases (CVDs). Preferred Reporting Items for Systematic reviews and Meta‐Analyses extension for Scoping Reviews was adopted and articles listed in PubMed and Cochrane databases from January 1, 2018 to December 31, 2023, were assessed. In total, 67 articles met the eligibility criteria. We obtained a general overview of the field, including lesion types, sample sizes, data sources, and databases and found that nearly half of the studies used multisequence magnetic resonance as the input. Both classical machine learning and deep learning were widely used. The evaluation metrics varied according to the five main algorithm tasks of classification, detection, segmentation, estimation, and generation. Cross‐validation was primarily used with only one third of the included studies using external validation. We also illustrate the key questions of the CVD research studies and grade the clinical utility of their AI solutions. Although most attention is devoted to improving the performance of AI models, this scoping review provides information on the availability of algorithms, reliability of external validations, and consistency of evaluation metrics and may facilitate improved clinical applicability and acceptance.

ChatGPT and radiology report: potential applications and limitations.

Large language models like ChatGPT, with their growing accessibility, are attracting increasing interest within the artificial intelligence medical field, particularly in the analysis of radiology reports. These present a valuable opportunity to explore the potential clinical applications of large language models, given their huge capabilities in processing and understanding written language. Early research indicates that ChatGPT could offer benefits in radiology reporting. ChatGPT can assist but not replace radiologists in achieving diagnoses, generating structured reports, extracting data, identifying errors or incidental findings, and can also serve as a support in creating patient-friendly reports. However, ChatGPT also has intrinsic limitations, such as hallucinations, stochasticity, biases, deficiencies in complex clinical scenarios, data privacy and legal concerns. To fully utilize the potential of ChatGPT in radiology reporting, careful integration planning and rigorous validation of their outputs are crucial, especially for tasks requiring abstract reasoning or nuanced medical context. Radiologists' expertise in medical imaging and data analysis positions them exceptionally well to lead the responsible integration and utilization of ChatGPT within the field of radiology. This article offers a topical overview of the potential strengths and limitations of ChatGPT in radiological reporting.

Image-Based Generative Artificial Intelligence in Radiology: Comprehensive Updates.

Generative artificial intelligence (AI) has been applied to images for image quality enhancement, domain transfer, and augmentation of training data for AI modeling in various medical fields. Image-generative AI can produce large amounts of unannotated imaging data, which facilitates multiple downstream deep-learning tasks. However, their evaluation methods and clinical utility have not been thoroughly reviewed. This article summarizes commonly used generative adversarial networks and diffusion models. In addition, it summarizes their utility in clinical tasks in the field of radiology, such as direct image utilization, lesion detection, segmentation, and diagnosis. This article aims to guide readers regarding radiology practice and research using image-generative AI by 1) reviewing basic theories of image-generative AI, 2) discussing the methods used to evaluate the generated images, 3) outlining the clinical and research utility of generated images, and 4) discussing the issue of hallucinations.

Optimized Deep Learning Model for Medical Image Diagnosis

Deep learning models, particularly convolutional neural networks (CNNs), have excelled in pattern recognition tasks like face recognition, pedestrian detection, and medical diagnosis. CNNs, known for their end-to-end feature extraction and classification, are widely used in computer vision. Pre-trained CNN models such as AlexNet, VGG-19, ResNet, and Inception can serve as feature extractors for various problems, though they often produce a large number of features. To address this challenge and reduce the number of features, this research employs an efficient two-layer feature selection algorithm. The first layer, a global search optimizer, guides the swarm in the search space to identify the optimal feature subset. The second layer refines and fine-tunes the solution obtained from the global search. The main contributions of this work encompass several aspects: (i) the introduction of a lightweight classification model that utilizes a subset of features, (ii) the use of an efficient optimizer to perform simultaneous feature and instance selection, and (iii) the direct implications for the radiology field, where it could serve as a supportive tool for diagnosis. To evaluate the proposed approach, it was tested on skin lesion diagnosis using 1113 abnormal and 1099 normal lesions. The pre-trained VGG-19 model provided a 1-D feature vector of size 4096, with a linear SVM used for classification. Results showed the optimizer reduced the feature vector by up to 86% while achieving over 70% accuracy. The proposed optimizer outperformed the Reinforcement Learning-based Memetic Particle Swarm Optimization (RLMPSO).

Revolutionizing Radiology With Artificial Intelligence.

Artificial intelligence (AI) is rapidly transforming the field of radiology, offering significant advancements in diagnostic accuracy, workflow efficiency, and patient care. This article explores AI's impact on various subfields of radiology, emphasizing its potential to improve clinical practices and enhance patient outcomes. AI-driven technologies such as machine learning, deep learning, and natural language processing (NLP) are playing a pivotal role in automating routine tasks, aiding in early disease detection, and supporting clinical decision-making, allowing radiologists to focus on more complex diagnostic challenges. Key applications of AI in radiology include improving image analysis through computer-aided diagnosis (CAD) systems, which enhance the detection of abnormalities in imaging, such as tumors. AI tools have demonstrated high accuracy in analyzing medical images, integrating data from multiple imaging modalities such as CT, MRI, and PET to provide comprehensive diagnostic insights. These advancements facilitate personalized treatment planning and complement radiologists' workflows. However, for AI to be fully integrated into radiology workflows, several challenges must be addressed, including ensuring transparency in how AI algorithms work, protecting patient data, and avoiding biases that could affect diverse populations. Developing explainable AI systems that can clearly show how decisions are made is crucial, as is ensuring AI tools can seamlessly fit into existing radiology systems. Collaboration between radiologists, AI developers, and policymakers, alongside strong ethical guidelines and regulatory oversight, will be key to ensuring AI is implemented safely and effectively in clinical practice. Overall, AI holds tremendous promise in revolutionizing radiology. Through its ability to automate complex tasks, enhance diagnostic capabilities, and streamline workflows, AI has the potential to significantly improve the quality and efficiency of radiology practices. Continued research, development, and collaboration will be crucial in unlocking AI's full potential and addressing the challenges that accompany its adoption.

Attitudes and perceptions of Thai medical students regarding artificial intelligence in radiology and medicine

IntroductionArtificial Intelligence (AI) has made a profound impact on the medical sector, particularly in radiology. The integration of AI knowledge into medical education is essential to equip future healthcare professionals with the skills needed to effectively leverage these advancements in their practices. Despite its significance, many medical schools have yet to incorporate AI into their curricula. This study aims to assess the attitudes of medical students in Thailand toward AI and its application in radiology, with the objective of better planning for its inclusion.MethodsBetween February and June 2022, we conducted a survey in two Thai medical schools: Chiang Mai University in Northern Thailand and Prince of Songkla University in Southern Thailand. We employed 5-point Likert scale questions (ranging from strongly agree to strongly disagree) to evaluate students’ opinions on three main aspects: (1) their understanding of AI, (2) the inclusion of AI in their medical education, and (3) the potential impact of AI on medicine and radiology.ResultsOur findings revealed that merely 31% of medical students perceived to have a basic understanding of AI. Nevertheless, nearly all students (93.6%) recognized the value of AI training for their careers and strongly advocated for its inclusion in the medical school curriculum. Furthermore, those students who had a better understanding of AI were more likely to believe that AI would revolutionize the field of radiology (p = 0.02), making it more captivating and impactful (p = 0.04).ConclusionOur study highlights a noticeable gap in the understanding of AI among medical students in Thailand and its practical applications in healthcare. However, the overwhelming consensus among these students is their readiness to embrace the incorporation of AI training into their medical education. This enthusiasm holds the promise of enhancing AI adoption, ultimately leading to an improvement in the standard of healthcare services in Thailand, aligning with the country’s healthcare vision.

Computed Tomography Diagnostic Reference Levels for Routine Pediatric Examinations in Saudi Arabia: A Systematic Review.

This work aims to review the diagnostic reference levels reported for pediatric patients who underwent routine CT exams in Saudi Arabia. The literature search was conducted in prominent search engines,including PubMed, Science Direct, Google Scholar, EBSCO, ProQuest, and Saudi Digital Library (SDL), which serve as important sources in the fields of radiology, radiography, and medical physics. The search was conducted to identify previous studies that have reported diagnostic reference levels (DRLs) for pediatric CT examinations. Only studies that specifically assessed DRLs in pediatric patients were considered for inclusion. On careful evaluation, eight publications were ultimately chosenout of the 31 that were originally considered. An assessment was made on the volume computed tomographic dose index (CTDIvol) and the dose-length product (DLP) values. The reported range of mean values for DLP (mGy.cm) included in this review for brain, chest, abdomen, abdomen and pelvis, and CAP (chest,abdomen,and pelvis) was 172-1881, 27.3-458.9, 94.5-790.1, 78-236, and 94.2, respectively. Moreover, the reported range of mean values for CTDIvol (mGy) included in this review for the brain, chest, abdomen, abdomenand pelvis, and CAP were 11.7-79.9, 1.2-20.1, 7.4-22.6, 2.6-5.5, and 3.6, respectively. The review emphasizes the need for standardized imaging protocols and DRLsin pediatric CT examinations to optimize radiation doses and patient safety. It recommends the development of annually reviewed comprehensive national DRLs in Saudi Arabia and improved training for technologists and physicians involved in pediatric CT imaging.

Imaging in France: 2024 Update.

Radiology in France has made major advances in recent years through innovations in research and clinical practice. French institutions have developed innovative imaging techniques and artificial intelligence applications in the field of diagnostic imaging and interventional radiology. These include, but are not limited to, a more precise diagnosis of cancer and other diseases, research in dual-energy and photon-counting computed tomography, new applications of artificial intelligence, and advanced treatments in the field of interventional radiology. This article aims to explore the major research initiatives and technological advances that are shaping the landscape of radiology in France. By highlighting key contributions in diagnostic imaging, artificial intelligence, and interventional radiology, we provide a comprehensive overview of how these innovations are improving patient outcomes, enhancing diagnostic accuracy, and expanding the possibilities for minimally invasive therapies. As the field continues to evolve, France's position at the forefront of radiological research ensures that these innovations will play a central role in addressing current healthcare challenges and improving patient care on a global scale.

Public perceptions related to healthcare preparedness to anti-amyloid therapies for Alzheimer’s Disease in Japan

BackgroundThe approval of lecanemab, an anti-amyloid therapy for Alzheimer’s disease (AD), necessitates addressing healthcare preparedness for disease-modifying treatment (DMT) to ensure appropriate, safe, and sustainable drug administration. Understanding public perceptions on this matter is crucial. We aimed to assess discrepancies and similarities in the perceptions of Japanese trial-ready cohort study (‘J-TRC webstudy’) participants and clinical specialists in the fields of dementia treatment and radiology, concerning affairs related to challenges in DMT preparedness.MethodsThis was a cross-sectional prospective observational study conducted in November–December 2023. The J-TRC webstudy participants were invited to participate in an online survey using Google Forms, and clinical specialists were invited to complete a mail-based survey. Main questionnaire items had been designed to be common in both surveys, and their responses were analyzed for participant attributes, interests, attitudes, expectations, and concerns about DMTs without specifying lecanemab.ResultsResponses were obtained from n = 2,050 J-TRC webstudy participants and n = 1,518 clinical specialists. Compared to specialists, more J-TRC respondents perceived the eligible proportion for DMT as smaller (59.1% versus 30.7%), perceived the eligible severity for DMT as more limited (58.0% versus 24.5%), and perceived the efficacy of DMT as slightly more encouraging (29.3% versus 34.8%). In terms of treatment prioritization, both J-TRC respondents and specialist respondents exhibited similar levels of acceptance for prioritizing patients to treat: e.g., approximately two-thirds endorsed patient prioritization under hypothetical resource constraints or other reasons. A medical rationale emerged as the most compelling reason for acceptance of patient prioritization across the surveys. In contrast, the need to address vulnerable populations was the reason that led to the least acceptance of prioritization, followed by economic considerations.ConclusionsOur findings offer valuable insights into the discrepancies in knowledge and perception between patients and healthcare providers. This could enhance the delivery of patient information in clinical settings and inform the discussion surrounding patient prioritization strategies.

Beyond the hospital walls: The lived experiences of Sidra's radiologists with home-based picture archiving and communication system during a global crisis

Objective: This study explores the adaptation of radiologists at Sidra Medicine, Qatar, to the home-based picture archiving and communication system (HPACS) during the COVID-19 pandemic. Methods: This qualitative study used a phenomenological methodology to delve into the experiences of radiologists using HPACS, which emerged as a crucial tool for remote radiology practice during the pandemic. It highlights the perceived benefits, barriers, and challenges of using HPACS, and emphasizes its role in ensuring continuity of patient care and diagnostics while adhering to safety protocols. Results: The study reveals how HPACS facilitated work efficiency and safety, and also presented challenges such as workspace limitations and technical issues. The findings suggest a transformative impact of HPACS on the field of radiology, and indicate a future marked by increasingly digital and decentralized practices. Conclusion: This research contributes to understanding the adaptation of healthcare professionals to remote work technologies and provides insights for improving remote radiology systems and preparing for future crises.

Training on Radiation Protection in Diagnostic Radiology: Role of the Regulatory Body to Ensure the Safe Use of Ionizing Radiation in Bangladesh

Radiation has been used since its invention in various sectors like medicine, agriculture, industry, research & education, power production, and so on, for the benefit of mankind. With this trend, Bangladesh is also using radiation to achieve economic development through the application of radiation in all possible ways. Proper education, knowledge, training, and awareness are a mandatory part of using radiation to ensure safe use of it against its harmful effects. Concerning the control of the safe use of radiation, an extensive regulatory system: “Bangladesh Atomic Energy Regulatory Authority (BAERA)” was set up in 2013 in Bangladesh consisting of acts, rules, and guidance. The primary objective of the regulatory body is to protect the workers, the public, and the environment from the possible adverse effects of radiation use. To deal with its objective, it is a core activity of the regulatory body to create radiation awareness among radiation workers and related personnel all over the country through the provision of the existing act and rules regarding radiation protection training. The current study is done to have an overview of the role of the regulatory body in this regard and their present status related to the radiation protection training program for radiation workers in the field of diagnostic radiology in the country.

Ability assessment of the craniofacial structures radiological anatomy on panoramic radiography among dental student during pandemic of Covid-19. Ability assessment on OPG among dental student during pandemic of Covid-19.

Orthopantomography (OPG) is a routine imaging method in dental practice and an essential di- agnostic tool in dentistry. However, OPGs are challenging to interpret due to many overlapping structures. Graduates of dental schools should be aware of image distortions caused by various factors and be able to distinguish them from typical structures to make an accurate diagnosis. The aim was to determine the correlation between the knowledge regarding the location of craniofacial structures of the 1st through 3rd- year dental students and the ability to recognized them on OPGs. The study was conducted in 2021 on 131 dental students using the Microsoft Teams program. Each participant had to determine the location of 4 anthropometric points on 4 OPGs. Using proprietary software, the authors determined the Articular angle between them. The researchers performed the statistical analysis. The Bioethics Committee approved the research. The results of students from particular years of studies did not show statistical significance. There was no statistically significant difference between males and females. Only the answers from third-year male students regarding the one Articular angle showed statistically significant differences compared to the rest of the participants. Recognizing joint structures on OPG is very important from the clinical point of view, although it is often overlooked in diagnostics. Students in the first years of dental school acquire the ability to assess and analyze TMJ on OPGs properly. OPGs have many advantages, including high availability, low cost and low radiation dose. In conclusion we can say that in the first three years of studies, dental students' image assessment skills in the craniofacial radiology field remain the same.

Simulator of X-ray Stowage

The development of a functional simulator capable of effectively and accurately positioning patients for X-ray examinations will improve the quality of X-ray images, reduce the effective radiation load on the patient, increase the level of education of specialists in the field of radiology and increase the level of medical care provided to the population. The creation of a database with stacking atlases can be used by medical personnel to enhance their professional skills and improve the quality of research. Improving the accuracy of patient positioning will help to reduce the effective radiation load and increase the level of diagnostic results.

The Impact of Artificial Intelligence on Radiology: Opportunities, Challenges, and Future Directions

This paper explores the transformative impact of Artificial Intelligence (AI) on the field of radiology. It examines the integration of AI in diagnostic imaging, its potential benefits in enhancing diagnostic accuracy, efficiency, and workflow, and the challenges associated with its implementation. The discussion also highlights future directions for AI in radiology and the implications for radiologists.

Amplifying Research: The Potential for Podcasts to Boost Radiology Journal Article Exposure.

Background Podcasts have become an increasingly popular method of communicating information in medicine, including in radiology. However, the effect of podcasts on the reach of journal articles remains unclear. Purpose To evaluate the influence of Radiology podcasts on the performance metrics, including downloads, citations, and Altmetric Attention Score (AAS), of Radiology articles. Materials and Methods This was a retrospective study. All articles published in the print version of Radiology from January 2021 to December 2022 were reviewed; editorials and case reports were excluded. Articles featured on Radiology podcasts were included in the podcast group. Articles published within the same journal issue and category were the nonpodcast group. Downloads, Google Scholar citations, Dimensions citations, and AAS metrics were recorded. The Mann-Whitney U test was used to compare medians and evaluate differences between older and more recently published articles. Results The podcast group, composed of 88 articles, exhibited significantly higher median values for downloads (PG, 4521.0; nonpodcast group, 2123.0; P < .001), Google Scholar citations (podcast group, 14.5; nonpodcast group, 10.0; P = .01), Dimensions citations (podcast group, 12.0; nonpodcast group, 9.0; P = .01), and AAS (podcast group, 43.0; nonpodcast group, 10.0; P < .001) compared with the nonpodcast group of 378 articles. Within both groups, articles published in the earlier nonpodcast group (January to June 2021) had higher downloads (podcast group, P = .08; nonpodcast group, P < .001), Google Scholar citations (podcast group and nonpodcast group, P < .001), and Dimension citations (podcast group and nonpodcast group, P < .001) than articles from the later period (July to December 2022). AAS markedly increased in recent podcast articles (P = .03), but AAS for nonpodcast articles significantly decreased over time (P = .01). Conclusion Radiology articles featured on the Radiology podcast had greater median metrics, including downloads, Google Scholar citations, Dimensions citations, and AAS, compared with nonpodcast articles, suggesting that podcasts can be an effective method of disseminating and amplifying research within the field of radiology. © RSNA, 2024 See also the editorial by Chu and Nicola in this issue.

The History and Continued Evolution of Functioning Magnetic Resonance Imaging Technology (fMRI)

This paper summarizes the history of the development of fMRI technology, beginning with the inception of the ideas on which the technology is based, and continuing through the details of its practical development. This review also briefly discusses the math behind those ideas and the physics of Magnetic Resonance Imaging (MRI) and fMRI technology. The fMRI is a remarkable development in the history of medicine and a testament to how far the field of radiology has come. This paper seeks to discuss the history and current applications of this groundbreaking innovation.

Optimizing Neuroimaging Techniques for Enhanced Visualization of Intracranial Aneurysms

Intracranial aneurysms, abnormal dilations of blood vessels in the brain, pose significant health risks, especially when ruptured, leading to conditions such as subarachnoid hemorrhage and stroke. The primary objective of this study is to evaluate the diagnostic accuracy of different computed tomography angiography (CTA) protocols for detecting intracranial aneurysms and to identify methods for optimizing scanning parameters to reduce radiation exposure while ensuring high image quality. Results from the analysis of various CTA protocols revealed that lowering tube voltages (80-120 kV) and milliampere seconds (mAs) effectively reduced radiation exposure without compromising the quality of images. Additionally, the use of advanced reconstruction algorithms, including deep learning techniques, significantly improved the visualization of small vascular structures, thereby enhancing diagnostic accuracy. These findings provide valuable insights for enhancing clinical practices in the fields of neurology and radiology.

Me, my work and AI: How radiologists craft their work and identity

This study investigates the introduction of AI in the field of radiology through a multi-level analysis (individuals, organization, and profession). Drawing on in-depth interviews with 54 participants (radiologists, radiological technologists, managers, hospital directors, and engineers), we examined how radiologists perceive AI and respond through job crafting behaviors and identity work. The findings reveal three categories of job crafting: approach, avoidance, and an emerging category of identity crafting, in which actors develop strategies to redefine their identity after the introduction of AI. This study contributes to a better understanding of AI and its implications for individuals at work and, in particular, employees' use of proactive approaches to job design, along with how such approaches are shaped by organizations and professional bodies.