High sensitivity methods for automated rib fracture detection in pediatric radiographs.

Rib fractures are highly predictive of non-accidental trauma in children under 3 years old. Rib fracture detection in pediatric radiographs is challenging because fractures can be obliquely oriented to the imaging detector, obfuscated by other structures, incomplete, and non-displaced. Prior studies have shown up to two-thirds of rib fractures may be missed during initial interpretation. In this paper, we implemented methods for improving the sensitivity (i.e. recall) performance for detecting and localizing rib fractures in pediatric chest radiographs to help augment performance of radiology interpretation. These methods adapted two convolutional neural network (CNN) architectures, RetinaNet and YOLOv5, and our previously proposed decision scheme, "avalanche decision", that dynamically reduces the acceptance threshold for proposed regions in each image. Additionally, we present contributions of using multiple image pre-processing and model ensembling techniques. Using a custom dataset of 1109 pediatric chest radiographs manually labeled by seven pediatric radiologists, we performed 10-fold cross-validation and reported detection performance using several metrics, including F2 score which summarizes precision and recall for high-sensitivity tasks. Our best performing model used three ensembled YOLOv5 models with varied input processing and an avalanche decision scheme, achieving an F2 score of 0.725 ± 0.012. Expert inter-reader performance yielded an F2 score of 0.732. Results demonstrate that our combination of sensitivity-driving methods provides object detector performance approaching the capabilities of expert human readers, suggesting that these methods may provide a viable approach to identify all rib fractures.

Women in Radiology: A perspective from Spain.

INTRODUCTION: There are gender inequalities in all fields, including radiology. Although the situation is improving, the presence of radiologists in leadership positions continues to be a minority. The objective of this article is to analyse the situation of women in the spanish radiology, comparing it with Europe and the United States. MATERIALS AND METHODS: We selected the years 2000-2022 as reference period to make a comparison with feminization data throughout history. In addition, relevant specific data from the just begun 2023 were also included. The variables in which we investigated feminization were the following: medical students, medical graduates, radiology residents and specialists, section chiefs, department chairs, radiology residency programme directors, radiology university professors, presidents of the main radiological entities and societies in Spain, Europe and the United States, recipients of the main awards given by these radiological societies and chief editors of their journals. In order to perform this analysis we conducted an in-depth bibliographic research, we contacted the radiological societies of Spain, Europe and the USA and we carried out a survey in the main Spanish radiology departments. RESULTS: The female presence in radiology decreases as we rise to leadership positions, a situation that is patent in Spain, Europe and the US, comparison that will be analysed in depth throughout the article. In Spanish hospitals in 2021 there were 58.1% female radiology residents, 55% female radiologists, 42.9% female section chiefs and 24.4% female department chairs. In SERAM's history there have been 10% female presidents, 22% female gold medallists and 5% female editors-in-chief. If we analyse data from 2000 to 2023, female presidents reach 32% and female gold medallists 31%. CONCLUSIONS: Although gender inequality is declining, in radiology women continue to be underrepresented in leadership positions. Work must be done in order to build a diverse and inclusive profession that reflects demographic reality.

Teaching radiology in integrated educational programs.

Radiology is a medical discipline, an area of transversal knowledge integrated into any clinical situation. The optimal training of learning knowledge, skills and aptitudes in Radiology in the Degree in Medicine requires the integration of any imaging modality in the different areas of knowledge; from the basic subjects to any clinical subject of the Degree. This article describes the integration of Radiology teaching into the curriculum throughout the Medicine Degree at the University of Girona (UdG), describing the different radiology teaching activities that are taught. The specific activities of the subject "Radiology" are detailed; through workshops, seminars, practices, interactive computer game; and describing the characteristics of the main teaching methodological activity of the UdG, Problem-Based Learning.

Checklist: Neck computed tomography in non-traumatic emergencies.

Patients attending the emergency department (ED) with cervical inflammatory/infectious symptoms or presenting masses that may involve the aerodigestive tract or vascular structures require a contrast-enhanced computed tomography (CT) scan of the neck. Its radiological interpretation is hampered by the anatomical complexity and pathophysiological interrelationship between the different component systems in a relatively small area. Recent studies propose a systematic evaluation of the cervical structures, using a 7-item checklist, to correctly identify the pathology and detect incidental findings that may interfere with patient management. As a conclusion, the aim of this paper is to review CT findings in non-traumatic pathology of the neck in the ED, highlighting the importance of a systematic approach in its interpretation and synthesis of a structured, complete, and concise radiological report.

Challenges in radiological evaluation of brain metastases, beyond progression.

MRI is the cornerstone in the evaluation of brain metastases. The clinical challenges lie in discriminating metastases from mimickers such as infections or primary tumors and in evaluating the response to treatment. The latter sometimes leads to growth, which must be framed as pseudo-progression or radionecrosis, both inflammatory phenomena attributable to treatment, or be considered as recurrence. To meet these needs, imaging techniques are the subject of constant research. However, an exponential growth after radiotherapy must be interpreted with caution, even in the presence of results suspicious of tumor progression by advanced techniques, because it may be due to inflammatory changes. The aim of this paper is to familiarize the reader with inflammatory phenomena of brain metastases treated with radiotherapy and to describe two related radiological signs: "the inflammatory cloud" and "incomplete ring enhancement", in order to adopt a conservative management with close follow-up.

The teaching of Radiology in other university studies.

After the implementation of the European Space for Higher Education, the contents of the Radiology and Physical Medicine Area that were taught in the Medicine Degree have also been incorporated into the new degrees of Dentistry, Nursing, Physiotherapy, Podiatry, and, to a lesser extent, Pharmacy, Occupational Therapy, Logopedia, and Biomedical Engineering As a whole, the basic concepts of radiology and radiological protection are taught in Murcia in 5 different degrees with a total of 52.5 ECTS credits, participating in the training of 1219 students each academic year. This incorporation in the new degrees has tripled the number of subjects in which undergraduate teaching is taught, and doubled both the number of ECTS credits and the number of undergraduate students to whom it directs its training work. Thus, given the possible creation of new university degrees in the near future (Diagnostic Imaging and Radiotherapy Technicians), it would be necessary to involve a greater number of accredited professionals, from different specialties, and to optimize teaching resources (bibliography, material teacher, clinical cases, etc.,) for its usefulness in the different subjects that share similar contents.

Potential of GPT-4 for Detecting Errors in Radiology Reports: Implications for Reporting Accuracy.

Background Errors in radiology reports may occur because of resident-to-attending discrepancies, speech recognition inaccuracies, and large workload. Large language models, such as GPT-4 (ChatGPT; OpenAI), may assist in generating reports. Purpose To assess effectiveness of GPT-4 in identifying common errors in radiology reports, focusing on performance, time, and cost-efficiency. Materials and Methods In this retrospective study, 200 radiology reports (radiography and cross-sectional imaging [CT and MRI]) were compiled between June 2023 and December 2023 at one institution. There were 150 errors from five common error categories (omission, insertion, spelling, side confusion, and other) intentionally inserted into 100 of the reports and used as the reference standard. Six radiologists (two senior radiologists, two attending physicians, and two residents) and GPT-4 were tasked with detecting these errors. Overall error detection performance, error detection in the five error categories, and reading time were assessed using Wald χ2 tests and paired-sample t tests. Results GPT-4 (detection rate, 82.7%;124 of 150; 95% CI: 75.8, 87.9) matched the average detection performance of radiologists independent of their experience (senior radiologists, 89.3% [134 of 150; 95% CI: 83.4, 93.3]; attending physicians, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; residents, 80.0% [120 of 150; 95% CI: 72.9, 85.6]; P value range, .522-.99). One senior radiologist outperformed GPT-4 (detection rate, 94.7%; 142 of 150; 95% CI: 89.8, 97.3; P = .006). GPT-4 required less processing time per radiology report than the fastest human reader in the study (mean reading time, 3.5 seconds ± 0.5 [SD] vs 25.1 seconds ± 20.1, respectively; P < .001; Cohen d = -1.08). The use of GPT-4 resulted in lower mean correction cost per report than the most cost-efficient radiologist ($0.03 ± 0.01 vs $0.42 ± 0.41; P < .001; Cohen d = -1.12). Conclusion The radiology report error detection rate of GPT-4 was comparable with that of radiologists, potentially reducing work hours and cost. © RSNA, 2024 See also the editorial by Forman in this issue.

Role of radiomics in staging liver fibrosis: a meta-analysis.

BACKGROUND: Fibrosis has important pathoetiological and prognostic roles in chronic liver disease. This study evaluates the role of radiomics in staging liver fibrosis. METHOD: After literature search in electronic databases (Embase, Ovid, Science Direct, Springer, and Web of Science), studies were selected by following precise eligibility criteria. The quality of included studies was assessed, and meta-analyses were performed to achieve pooled estimates of area under receiver-operator curve (AUROC), accuracy, sensitivity, and specificity of radiomics in staging liver fibrosis compared to histopathology. RESULTS: Fifteen studies (3718 patients; age 47 years [95% confidence interval (CI): 42, 53]; 69% [95% CI: 65, 73] males) were included. AUROC values of radiomics for detecting significant fibrosis (F2-4), advanced fibrosis (F3-4), and cirrhosis (F4) were 0.91 [95%CI: 0.89, 0.94], 0.92 [95%CI: 0.90, 0.95], and 0.94 [95%CI: 0.93, 0.96] in training cohorts and 0.89 [95%CI: 0.83, 0.91], 0.89 [95%CI: 0.83, 0.94], and 0.93 [95%CI: 0.91, 0.95] in validation cohorts, respectively. For diagnosing significant fibrosis, advanced fibrosis, and cirrhosis the sensitivity of radiomics was 84.0% [95%CI: 76.1, 91.9], 86.9% [95%CI: 76.8, 97.0], and 92.7% [95%CI: 89.7, 95.7] in training cohorts, and 75.6% [95%CI: 67.7, 83.5], 80.0% [95%CI: 70.7, 89.3], and 92.0% [95%CI: 87.8, 96.1] in validation cohorts, respectively. Respective specificity was 88.6% [95% CI: 83.0, 94.2], 88.4% [95% CI: 81.9, 94.8], and 91.1% [95% CI: 86.8, 95.5] in training cohorts, and 86.8% [95% CI: 83.3, 90.3], 94.0% [95% CI: 89.5, 98.4], and 88.3% [95% CI: 84.4, 92.2] in validation cohorts. Limitations included use of several methods for feature selection and classification, less availability of studies evaluating a particular radiological modality, lack of a direct comparison between radiology and radiomics, and lack of external validation. CONCLUSION: Although radiomics offers good diagnostic accuracy in detecting liver fibrosis, its role in clinical practice is not as clear at present due to comparability and validation constraints.

Assessment of Claimant, Clinical, and Financial Characteristics of Teleradiology Medical Malpractice Cases.

Background The increasing use of teleradiology has been accompanied by concerns relating to risk management and patient safety. Purpose To compare characteristics of teleradiology and nonteleradiology radiology malpractice cases and identify contributing factors underlying these cases. Materials and Methods In this retrospective analysis, a national database of medical malpractice cases was queried to identify cases involving telemedicine that closed between January 2010 and March 2022. Teleradiology malpractice cases were identified based on manual review of cases in which telemedicine was coded as one of the contributing factors. These cases were compared with nonteleradiology cases that closed during the same time period in which radiology had been determined to be the primary responsible clinical service. Claimant, clinical, and financial characteristics of the cases were recorded, and continuous or categorical data were compared using the Wilcoxon rank-sum test or Fisher exact test, respectively. Results This study included 135 teleradiology and 3474 radiology malpractices cases. The death of a patient occurred more frequently in teleradiology cases (48 of 135 [35.6%]) than in radiology cases (685 of 3474 [19.7%]; P < .001). Cerebrovascular disease was a more common final diagnosis in the teleradiology cases (13 of 135 [9.6%]) compared with the radiology cases (124 of 3474 [3.6%]; P = .002). Problems with communication among providers was a more frequent contributing factor in the teleradiology cases (35 of 135 [25.9%]) than in the radiology cases (439 of 3474 [12.6%]; P < .001). Teleradiology cases were more likely to close with indemnity payment (79 of 135 [58.5%]) than the radiology cases (1416 of 3474 [40.8%]; P < .001) and had a higher median indemnity payment than the radiology cases ($339 230 [IQR, $120 790-$731 615] vs $214 063 [IQR, $66 620-$585 424]; P = .01). Conclusion Compared with radiology cases, teleradiology cases had higher clinical and financial severity and were more likely to involve issues with communication. © RSNA, 2024 See also the editorial by Mezrich in this issue.

Radiology: Cardiothoracic Imaging Highlights 2023.

Radiology: Cardiothoracic Imaging publishes novel research and technical developments in cardiac, thoracic, and vascular imaging. The journal published many innovative studies during 2023 and achieved an impact factor for the first time since its inaugural issue in 2019, with an impact factor of 7.0. The current review article, led by the Radiology: Cardiothoracic Imaging trainee editorial board, highlights the most impactful articles published in the journal between November 2022 and October 2023. The review encompasses various aspects of coronary CT, photon-counting detector CT, PET/MRI, cardiac MRI, congenital heart disease, vascular imaging, thoracic imaging, artificial intelligence, and health services research. Key highlights include the potential for photon-counting detector CT to reduce contrast media volumes, utility of combined PET/MRI in the evaluation of cardiac sarcoidosis, the prognostic value of left atrial late gadolinium enhancement at MRI in predicting incident atrial fibrillation, the utility of an artificial intelligence tool to optimize detection of incidental pulmonary embolism, and standardization of medical terminology for cardiac CT. Ongoing research and future directions include evaluation of novel PET tracers for assessment of myocardial fibrosis, deployment of AI tools in clinical cardiovascular imaging workflows, and growing awareness of the need to improve environmental sustainability in imaging. Keywords: Coronary CT, Photon-counting Detector CT, PET/MRI, Cardiac MRI, Congenital Heart Disease, Vascular Imaging, Thoracic Imaging, Artificial Intelligence, Health Services Research © RSNA, 2024.

Artificial Intelligence-Assisted Cancer Status Detection in Radiology Reports.

Cancer research is dependent on accurate and relevant information of patient's medical journey. Data in radiology reports are of extreme value but lack consistent structure for direct use in analytics. At Memorial Sloan Kettering Cancer Center (MSKCC), the radiology reports are curated using gold-standard approach of using human annotators. However, the manual process of curating large volume of retrospective data slows the pace of cancer research. Manual curation process is sensitive to volume of reports, number of data elements and nature of reports and demand appropriate skillset. In this work, we explore state of the art methods in artificial intelligence (AI) and implement end-to-end pipeline for fast and accurate annotation of radiology reports. Language models (LM) are trained using curated data by approaching curation as multiclass or multilabel classification problem. The classification tasks are to predict multiple imaging scan sites, presence of cancer and cancer status from the reports. The trained natural language processing (NLP) model classifiers achieve high weighted F1 score and accuracy. We propose and demonstrate the use of these models to assist in the manual curation process which results in higher accuracy and F1 score with lesser time and cost, thus improving efforts of cancer research. SIGNIFICANCE: Extraction of structured data in radiology for cancer research with manual process is laborious. Using AI for extraction of data elements is achieved using NLP models' assistance is faster and more accurate.

The new era of artificial intelligence in neuroradiology: current research and promising tools.

Radiology has a number of characteristics that make it an especially suitable medical discipline for early artificial intelligence (AI) adoption. These include having a well-established digital workflow, standardized protocols for image storage, and numerous well-defined interpretive activities. The more than 200 commercial radiologic AI-based products recently approved by the Food and Drug Administration (FDA) to assist radiologists in a number of narrow image-analysis tasks such as image enhancement, workflow triage, and quantification, corroborate this observation. However, in order to leverage AI to boost efficacy and efficiency, and to overcome substantial obstacles to widespread successful clinical use of these products, radiologists should become familiarized with the emerging applications in their particular areas of expertise. In light of this, in this article we survey the existing literature on the application of AI-based techniques in neuroradiology, focusing on conditions such as vascular diseases, epilepsy, and demyelinating and neurodegenerative conditions. We also introduce some of the algorithms behind the applications, briefly discuss a few of the challenges of generalization in the use of AI models in neuroradiology, and skate over the most relevant commercially available solutions adopted in clinical practice. If well designed, AI algorithms have the potential to radically improve radiology, strengthening image analysis, enhancing the value of quantitative imaging techniques, and mitigating diagnostic errors.

A radiologia tem uma série de características que a torna uma disciplina médica especialmente adequada à adoção precoce da inteligência artificial (IA), incluindo um fluxo de trabalho digital bem estabelecido, protocolos padronizados para armazenamento de imagens e inúmeras atividades interpretativas bem definidas. Tal adequação é corroborada pelos mais de 200 produtos radiológicos comerciais baseados em IA recentemente aprovados pelo Food and Drug Administration (FDA) para auxiliar os radiologistas em uma série de tarefas restritas de análise de imagens, como quantificação, triagem de fluxo de trabalho e aprimoramento da qualidade das imagens. Entretanto, para o aumento da eficácia e eficiência da IA, além de uma utilização clínica bem-sucedida dos produtos que utilizam essa tecnologia, os radiologistas devem estar atualizados com as aplicações em suas áreas específicas de atuação. Assim, neste artigo, pesquisamos na literatura existente aplicações baseadas em IA em neurorradiologia, mais especificamente em condições como doenças vasculares, epilepsia, condições desmielinizantes e neurodegenerativas. Também abordamos os principais algoritmos por trás de tais aplicações, discutimos alguns dos desafios na generalização no uso desses modelos e introduzimos as soluções comercialmente disponíveis mais relevantes adotadas na prática clínica. Se cautelosamente desenvolvidos, os algoritmos de IA têm o potencial de melhorar radicalmente a radiologia, aperfeiçoando a análise de imagens, aumentando o valor das técnicas de imagem quantitativas e mitigando erros de diagnóstico.

From explanation to intervention: Interactive knowledge extraction from Convolutional Neural Networks used in radiology.

Deep Learning models such as Convolutional Neural Networks (CNNs) are very effective at extracting complex image features from medical X-rays. However, the limited interpretability of CNNs has hampered their deployment in medical settings as they failed to gain trust among clinicians. In this work, we propose an interactive framework to allow clinicians to ask what-if questions and intervene in the decisions of a CNN, with the aim of increasing trust in the system. The framework translates a layer of a trained CNN into a measurable and compact set of symbolic rules. Expert interactions with visualizations of the rules promote the use of clinically-relevant CNN kernels and attach meaning to the rules. The definition and relevance of the kernels are supported by radiomics analyses and permutation evaluations, respectively. CNN kernels that do not have a clinically-meaningful interpretation are removed without affecting model performance. By allowing clinicians to evaluate the impact of adding or removing kernels from the rule set, our approach produces an interpretable refinement of the data-driven CNN in alignment with medical best practice.

Evaluation of radiological capacity and usage in paediatric TB diagnosis: A mixed-method protocol of a comparative study in Mozambique, South Africa and Spain.

INTRODUCTION: Tuberculosis remains one of the top ten causes of mortality globally. Children accounted for 12% of all TB cases and 18% of all TB deaths in 2022. Paediatric TB is difficult to diagnose with conventional laboratory tests, and chest radiographs remain crucial. However, in low-and middle-income countries with high TB burden, the capacity for radiological diagnosis of paediatric TB is rarely documented and data on the associated radiation exposure limited. METHODS: A multicentre, mixed-methods study is proposed in three countries, Mozambique, South Africa and Spain. At the national level, official registry databases will be utilised to retrospectively compile an inventory of licensed imaging resources (mainly X-ray and Computed Tomography (CT) scan equipment) for the year 2021. At the selected health facility level, three descriptive cross-sectional standardised surveys will be conducted to assess radiology capacity, radiological imaging diagnostic use for paediatric TB diagnosis, and radiation protection optimization: a site survey, a clinician-targeted survey, and a radiology staff-targeted survey, respectively. At the patient level, potential dose optimisation will be assessed for children under 16 years of age who were diagnosed and treated for TB in selected sites in each country. For this component, a retrospective analysis of dosimetry will be performed on TB and radiology data routinely collected at the respective sites. National inventory data will be presented as the number of units per million people by modality, region and country. Descriptive analyses will be conducted on survey data, including the demographic, clinical and programmatic characteristics of children treated for TB who had imaging examinations (chest X-ray (CXR) and/or CT scan). Dose exposure analysis will be performed by children's age, gender and disease spectrum. DISCUSSION: As far as we know, this is the first multicentre and multi-national study to compare radiological capacity, radiation protection optimization and practices between high and low TB burden settings in the context of childhood TB management. The planned comparative analyses will inform policy-makers of existing radiological capacity and deficiencies, allowing better resource prioritisation. It will inform clinicians and radiologists on best practices and means to optimise the use of radiological technology in paediatric TB management.

Photon-counting CT in Thoracic Imaging: Early Clinical Evidence and Incorporation Into Clinical Practice.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.