Are We Overdoing It? Changes in Diagnostic Imaging Workload during the Years 2010-2020 including the Impact of the SARS-CoV-2 Pandemic

Ethnic differences in CT derived abdominal body composition measures: a comparative retrospect pilot study between European and Inuit study population

Understanding ethnic variations in body composition is crucial for assessing health risks. Universal models may not suit all ethnicities, and there is limited data on the Inuit population. This study aimed to compare body composition between Inuit and European adults using computed tomography (CT) scans and to investigate the influence of demographics on these measurements. A retrospective analysis was conducted on 50 adults (29 Inuit and 21 European) who underwent standard trauma CT scans. Measurements focused on skeletal muscle index (SMI), various fat indices, and densities at the third lumbar vertebra level, analyzed using the Wilcoxon-Mann-Whitney test and multiple linear regression. Inuit women showed larger fat tissue indices and lower muscle and fat densities than European women. Differences in men were less pronouncehd, with only Intramuscular fat density being lower among Inuit men. Regression indicated that SMI was higher among men, and skeletal muscle density decreased with Inuit ethnicity and age, while visceral fat index was positively associated with age. This study suggests ethnic differences in body composition measures particularly among women, and indicates the need for Inuit-specific body composition models. It higlights the importance of further research into Inuit-specific body composition measurements for better health risk assessment.

A cross-national investigation of CT, MRI, PET, mammography, and radiation therapy resources and utilization

PURPOSE

This study aimed to analyze the domestic and international landscape of imaging diagnostics and treatments, focusing on Japan, to provide current insights for policymaking, clinical practice enhancement, and international collaboration.

METHODS

Data from 1996 to 2021 were collected from Japan's Ministry of Health, Labor and Welfare database for medical device counts of CT, MRI, PET, mammography, and radiotherapy. The National Database of Health Insurance Claims and Specific Health Checkups of Japan was utilized for examination numbers. An international comparison was made with data from 41 countries using the Organization for Economic Cooperation and Development (OECD) database.

RESULTS

The data included a total of 108,596 CT devices, 47,233 MRI devices, 2998 PET devices, 20,641 MMG devices, and 8023 RT devices during the survey period. Upon international comparison, Japan ranked first in CT and MRI devices per million people and second in examination numbers per 1000 people. The number of PET devices per million people exceeded OECD averages; however, the number of examinations per 1000 people was below the OECD average in 2020 (Japan: 4.0, OECD: 4.9). Although Japan exceeded OECD averages in mammography device counts (Japan: 33.8, OECD: 24.5 in 2020), radiotherapy device counts were similar to OECD averages (Japan: 8.3, OECD: 7.9 in 2020).

CONCLUSION

We have analyzed the utilization of equipment in the context of diagnostic imaging and radiotherapy in Japan. Since the initial survey year, all devices have shown an upward trend. However, it is essential not only to increase the number of devices and examinations but also to address the chronic shortage of radiologists and allied health professionals. Based on the insights gained from this study, understanding the actual status of diagnostic imaging and radiation therapy equipment is critical for grasping the domestic situation and may contribute to improving the quality of healthcare in Japan.

Merlin: A Vision Language Foundation Model for 3D Computed Tomography

Over 85 million computed tomography (CT) scans are performed annually in the US, of which approximately one quarter focus on the abdomen. Given the current shortage of both general and specialized radiologists, there is a large impetus to use artificial intelligence to alleviate the burden of interpreting these complex imaging studies while simultaneously using the images to extract novel physiological insights. Prior state-of-the-art approaches for automated medical image interpretation leverage vision language models (VLMs) that utilize both the image and the corresponding textual radiology reports. However, current medical VLMs are generally limited to 2D images and short reports. To overcome these shortcomings for abdominal CT interpretation, we introduce Merlin - a 3D VLM that leverages both structured electronic health records (EHR) and unstructured radiology reports for pretraining without requiring additional manual annotations. We train Merlin using a high-quality clinical dataset of paired CT scans (6+ million images from 15,331 CTs), EHR diagnosis codes (1.8+ million codes), and radiology reports (6+ million tokens) for training. We comprehensively evaluate Merlin on 6 task types and 752 individual tasks. The non-adapted (off-the-shelf) tasks include zero-shot findings classification (31 findings), phenotype classification (692 phenotypes), and zero-shot cross-modal retrieval (image to findings and image to impressions), while model adapted tasks include 5-year chronic disease prediction (6 diseases), radiology report generation, and 3D semantic segmentation (20 organs). We perform internal validation on a test set of 5,137 CTs, and external validation on 7,000 clinical CTs and on two public CT datasets (VerSe, TotalSegmentator). Beyond these clinically-relevant evaluations, we assess the efficacy of various network architectures and training strategies to depict that Merlin has favorable performance to existing task-specific baselines. We derive data scaling laws to empirically assess training data needs for requisite downstream task performance. Furthermore, unlike conventional VLMs that require hundreds of GPUs for training, we perform all training on a single GPU. This computationally efficient design can help democratize foundation model training, especially for health systems with compute constraints. We plan to release our trained models, code, and dataset, pending manual removal of all protected health information.

Quality of referrals for lower extremity ultrasonography and computed tomography pulmonary angiography and associations with positive findings of venous thromboembolism

INTRODUCTION

The referral is the basis for radiologists' assessment of modality, protocol and urgency, and insufficient information may threaten patient safety. The aim of this study was to assess the completeness of referrals for lower extremity venous duplex ultrasonography (LEVDUS) and computed tomography pulmonary angiography (CTPA), and to investigate associations between the provided clinical information including risk factors, symptoms and lab results in the referrals and positive findings of deep vein thrombosis (DVT) and pulmonary embolism (PE), respectively.

METHODS

Referrals for LEVDUS (801) and CTPA (800) performed from 2016 to 2019 were obtained. Three categories of clinical information from the referrals were recorded: symptoms, risk factors and laboratory results, as well as positive imaging findings of venous thromboembolism (VTE). Referral completeness was rated from zero to three according to how many categories of clinical information the referral provided.

RESULTS

Information from all three clinical information categories was provided in 15% and 25% of referrals for LEVDUS and CTPA, respectively, while 2% and 10% of referrals did not contain any clinical information. Symptoms were provided most often (85% for LEVDUS and 94% for CTPA). Provided information about risk factors was significantly associated with positive findings for LEVDUS, (p = 0.02) and CTPA (p < 0.001).

CONCLUSION

A great majority of referrals failed to provide one or more categories of clinical information. Risk factors were associated with a positive finding of VTE on LEVDUS and CTPA.

IMPLICATIONS FOR PRACTICE

Improving clinical information in referrals may improve justification, patient safety and quality of radiology services.

Assessing the Responses of Large Language Models (ChatGPT-4, Gemini, and Microsoft Copilot) to Frequently Asked Questions in Breast Imaging: A Study on Readability and Accuracy

Background Large language models (LLMs), such as ChatGPT-4, Gemini, and Microsoft Copilot, have been instrumental in various domains, including healthcare, where they enhance health literacy and aid in patient decision-making. Given the complexities involved in breast imaging procedures, accurate and comprehensible information is vital for patient engagement and compliance. This study aims to evaluate the readability and accuracy of the information provided by three prominent LLMs, ChatGPT-4, Gemini, and Microsoft Copilot, in response to frequently asked questions in breast imaging, assessing their potential to improve patient understanding and facilitate healthcare communication. Methodology We collected the most common questions on breast imaging from clinical practice and posed them to LLMs. We then evaluated the responses in terms of readability and accuracy. Responses from LLMs were analyzed for readability using the Flesch Reading Ease and Flesch-Kincaid Grade Level tests and for accuracy through a radiologist-developed Likert-type scale. Results The study found significant variations among LLMs. Gemini and Microsoft Copilot scored higher on readability scales (p < 0.001), indicating their responses were easier to understand. In contrast, ChatGPT-4 demonstrated greater accuracy in its responses (p < 0.001). Conclusions While LLMs such as ChatGPT-4 show promise in providing accurate responses, readability issues may limit their utility in patient education. Conversely, Gemini and Microsoft Copilot, despite being less accurate, are more accessible to a broader patient audience. Ongoing adjustments and evaluations of these models are essential to ensure they meet the diverse needs of patients, emphasizing the need for continuous improvement and oversight in the deployment of artificial intelligence technologies in healthcare.

Rogue AI: Cautionary Cases in Neuroradiology and What We Can Learn From Them

Introduction In recent years, artificial intelligence (AI) in medical imaging has undergone unprecedented innovation and advancement, sparking a revolutionary transformation in healthcare. The field of radiology is particularly implicated, as clinical radiologists are expected to interpret an ever-increasing number of complex cases in record time. Machine learning software purchased by our institution is expected to help our radiologists come to a more prompt diagnosis by delivering point-of-care quantitative analysis of suspicious findings and streamlining clinical workflow. This paper explores AI's impact on neuroradiology, an area accounting for a substantial portion of recent radiology studies. We present a case series evaluating an AI software's performance in detecting neurovascular findings, highlighting five cases where AI interpretations differed from radiologists' assessments. Our study underscores common pitfalls of AI in the context of CT head angiograms, aiming to guide future AI algorithms.  Methods We conducted a retrospective case series study at Stony Brook University Hospital, a large medical center in Stony Brook, New York, spanning from October 1, 2021 to December 31, 2021, analyzing 140 randomly sampled CT angiograms using AI software. This software assessed various neurovascular parameters, and AI findings were compared with neuroradiologists' interpretations. Five cases with divergent interpretations were selected for detailed analysis. Results Five representative cases in which AI findings were discordant with radiologists' interpretations are presented with diagnoses including diffuse anoxic ischemic injury, cortical laminar necrosis, colloid cyst, right superficial temporal artery-to-middle cerebral artery (STA-MCA) bypass, and subacute bilateral subdural hematomas. Discussion The errors identified in our case series expose AI's limitations in radiology. Our case series reveals that AI's incorrect interpretations can stem from complexities in pathology, challenges in distinguishing densities, inability to identify artifacts, identifying post-surgical changes in normal anatomy, sensitivity limitations, and insufficient pattern recognition. AI's potential for improvement lies in refining its algorithms to effectively recognize and differentiate pathologies. Incorporating more diverse training datasets, multimodal data, deep-reinforcement learning, clinical context, and real-time learning capabilities are some ways to improve AI's performance in the field of radiology. Conclusion Overall, it is apparent that AI applications in radiology have much room for improvement before becoming more widely integrated into clinical workflows. While AI demonstrates remarkable potential to aid in diagnosis and streamline workflows, our case series highlights common pitfalls that underscore the need for continuous improvement. By refining algorithms, incorporating diverse datasets, embracing multimodal information, and leveraging innovative machine learning strategies, AI's diagnostic accuracy can be significantly improved.

Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review

BACKGROUND

Artificial intelligence (AI) technology has the potential to transform medical practice within the medical imaging industry and materially improve productivity and patient outcomes. However, low acceptability of AI as a digital healthcare intervention among medical professionals threatens to undermine user uptake levels, hinder meaningful and optimal value-added engagement, and ultimately prevent these promising benefits from being realised. Understanding the factors underpinning AI acceptability will be vital for medical institutions to pinpoint areas of deficiency and improvement within their AI implementation strategies. This scoping review aims to survey the literature to provide a comprehensive summary of the key factors influencing AI acceptability among healthcare professionals in medical imaging domains and the different approaches which have been taken to investigate them.

METHODS

A systematic literature search was performed across five academic databases including Medline, Cochrane Library, Web of Science, Compendex, and Scopus from January 2013 to September 2023. This was done in adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) guidelines. Overall, 31 articles were deemed appropriate for inclusion in the scoping review.

RESULTS

The literature has converged towards three overarching categories of factors underpinning AI acceptability including: user factors involving trust, system understanding, AI literacy, and technology receptiveness; system usage factors entailing value proposition, self-efficacy, burden, and workflow integration; and socio-organisational-cultural factors encompassing social influence, organisational readiness, ethicality, and perceived threat to professional identity. Yet, numerous studies have overlooked a meaningful subset of these factors that are integral to the use of medical AI systems such as the impact on clinical workflow practices, trust based on perceived risk and safety, and compatibility with the norms of medical professions. This is attributable to reliance on theoretical frameworks or ad-hoc approaches which do not explicitly account for healthcare-specific factors, the novelties of AI as software as a medical device (SaMD), and the nuances of human-AI interaction from the perspective of medical professionals rather than lay consumer or business end users.

CONCLUSION

This is the first scoping review to survey the health informatics literature around the key factors influencing the acceptability of AI as a digital healthcare intervention in medical imaging contexts. The factors identified in this review suggest that existing theoretical frameworks used to study AI acceptability need to be modified to better capture the nuances of AI deployment in healthcare contexts where the user is a healthcare professional influenced by expert knowledge and disciplinary norms. Increasing AI acceptability among medical professionals will critically require designing human-centred AI systems which go beyond high algorithmic performance to consider accessibility to users with varying degrees of AI literacy, clinical workflow practices, the institutional and deployment context, and the cultural, ethical, and safety norms of healthcare professions. As investment into AI for healthcare increases, it would be valuable to conduct a systematic review and meta-analysis of the causal contribution of these factors to achieving high levels of AI acceptability among medical professionals.

The radiology job market in the Netherlands: which subspecialties and other skills are in demand?

OBJECTIVES

To evaluate the current job market for medical specialists in radiology and nuclear medicine (NM) in the Netherlands.

METHODS

Vacancies posted for radiologists and nuclear medicine physicians in the Netherlands between December 2020 and February 2022 were collected and analyzed.

RESULTS

A total of 157 vacancies (146 for radiologist and 11 for nuclear medicine physicians) were included. The most sought-after subspecialties were all-round (22%), abdominal (19%), and interventional radiology (14%), and 30% of vacancies preferred applicants with additional non-clinical skills (research, teaching, management, information and communications technology (ICT)/artificial intelligence (AI)). Non-academic hospitals significantly more frequently requested all-round radiologists (n = 31) than academic hospitals (n = 1) (p = 0.001), while the distribution of other requested subspecialties was not significantly different between non-academic and academic vacancies. Non-academic hospitals also significantly more frequently requested additional research tasks in their vacancies (n = 35) compared to academic hospitals (n = 4) (p = 0.011). There were non-significant trends for non-academic hospitals more frequently requesting teaching tasks in their vacancies (n =18) than academic hospitals (n = 1) (p = 0.051), and for non-academic hospitals more frequently asking for management skills (n = 11) than academic hospitals (n = 0) (p = 0.075).

CONCLUSION

All-round, abdominal, and interventional radiologists are most in demand on the job market in the Netherlands. All-round radiologists are particularly sought after by non-academic hospitals, whereas nuclear radiologists who completed the Dutch integrated NM and radiology residency seem to be welcomed by hospitals searching for a nuclear medicine specialist. Finally, non-clinical skills (research, teaching, management, ICT/AI) are commonly requested. These data can be useful for residents and developers of training curricula.

CLINICAL RELEVANCE STATEMENT

An overview of the radiology job market and the requested skills is important for residents, for those who seek work as a radiologist, and for those who are involved in the design and revision of residency programs.

KEY POINTS

Review of job vacancies over an extended period of time provides valuable information to residents and feedback to potentially improve radiology and nuclear medicine (NM) residency programs. All-round radiologists are wanted in non-academic hospitals and nuclear radiologists (those who have completed an integrated NM-radiology curriculum) are welcomed by hospitals searching for nuclear medicine specialists in the Netherlands. There is a need to train residents in important non-clinical skills, such as research and teaching, but also management and communications technology/artificial intelligence.

The growing role of telemedicine - possibilities and regulations concerning teleradiology in Poland

In recent years, imaging studies have become increasingly used at various stages in the management of patients with various conditions and disorders. This process results in a necessity to provide an increasing number of exams, which involves a growing role of radiologists in assessing and reporting those exams. The article discusses tele-radiology as a method that can improve access to radiology services, presenting its potential benefits, as well as the risks involved. It analyses access to radiology healthcare services in Poland in the context of the international and Polish legal provisions concerning the right to healthcare. While funding for imaging studies for patients is widely available and imaging equipment in Poland is improving despite some shortages, the main barrier is identified in the number of specialists capable of assessing the exams. Teleradiology can alleviate this shortage, so the article presents legal provisions and international good practice guidelines in this area, focusing on documents issued by the European Society of Radiology, the American College of Radiology, and the British Royal College of Radiologists. The guidelines concerning such aspects as patients' rights, teleradiologists' qualifications, communication and reporting, responsibility, and technical requirements may help make teleradiology a safe and valuable component of the healthcare system in Poland.

Inappropriate CT examinations: how much, who and where? Insights from a clinical decision support system (CDSS) analysis

OBJECTIVE

To assess the appropriateness of Computed Tomography (CT) examinations, using the ESR-iGuide.

MATERIAL AND METHODS

A retrospective study was conducted in 2022 in a medium-sized acute care teaching hospital. A total of 278 consecutive cases of CT referral were included. For each imaging referral, the ESR-iGuide provided an appropriateness score using a scale of 1-9 and the Relative Radiation Level using a scale of 0-5. These were then compared with the appropriateness score and the radiation level of the recommended ESR-iGuide exam.

DATA ANALYSIS

Pearson's chi-square test or Fisher exact test was used to explore the correlation between ESR-iGuide appropriateness level and physician, patients, and shift characteristics. A stepwise logistic regression model was used to capture the contribution of each of these factors.

RESULTS

Most of exams performed were CT head (63.67%) or CT abdominal pelvis (23.74%). Seventy percent of the actual imaging referrals resulted in an ESR-iGuide score corresponding to "usually appropriate." The mean radiation level for actual exam was 3.2 ± 0.45 compared with 2.16 ± 1.56 for the recommended exam. When using a stepwise logistic regression for modeling the probability of non-appropriate score, both physician specialty and status were significant (p = 0.0011, p = 0.0192 respectively). Non-surgical and specialist physicians were more likely to order inappropriate exams than surgical physicians.

CONCLUSIONS

ESR-iGuide software indicates a substantial rate of inappropriate exams of CT head and CT abdominal-pelvis and unnecessary radiation exposure mainly in the ED department. Inappropriate exams were found to be related to physicians' specialty and seniority.

CLINICAL RELEVANCE STATEMENT

These findings underscore the urgent need for improved imaging referral practices to ensure appropriate healthcare delivery and effective resource management. Additionally, they highlight the potential benefits and necessity of integrating CDSS as a standard medical practice. By implementing CDSS, healthcare providers can make more informed decisions, leading to enhanced patient care, optimized resource allocation, and improved overall healthcare outcomes.

KEY POINTS

• The overall mean of appropriateness for the actual exam according to the ESR-iGuide was 6.62 ± 2.69 on a scale of 0-9. • Seventy percent of the actual imaging referrals resulted in an ESR-iGuide score corresponding to "usually appropriate." • Inappropriate examination is related to both the specialty of the physician who requested the exam and the seniority status of the physician.

Use of Large Language Models to Predict Neuroimaging

PURPOSE

Large language models (LLMs) have demonstrated a level of competency within the medical field. The aim of this study was to explore the ability of LLMs to predict the best neuroradiologic imaging modality given specific clinical presentations. In addition, the authors seek to determine if LLMs can outperform an experienced neuroradiologist in this regard.

METHODS

ChatGPT and Glass AI, a health care-based LLM by Glass Health, were used. ChatGPT was prompted to rank the three best neuroimaging modalities while taking the best responses from Glass AI and the neuroradiologist. The responses were compared with the ACR Appropriateness Criteria for 147 conditions. Clinical scenarios were passed into each LLM twice to account for stochasticity. Each output was scored out of 3 on the basis of the criteria. Partial scores were given for nonspecific answers.

RESULTS

ChatGPT and Glass AI scored 1.75 and 1.83, respectively, with no statistically significant difference. The neuroradiologist scored 2.20, significantly outperforming both LLMs. ChatGPT was also found to be the more inconsistent of the two LLMs, with the score difference between both outputs being statistically significant. Additionally, scores between different ranks output by ChatGPT were statistically significant.

CONCLUSIONS

LLMs perform well in selecting appropriate neuroradiologic imaging procedures when prompted with specific clinical scenarios. ChatGPT performed the same as Glass AI, suggesting that with medical text training, ChatGPT could significantly improve its function in this application. LLMs did not outperform an experienced neuroradiologist, indicating the need for continued improvement in the medical context.

The Performance of a Deep Learning-Based Automatic Measurement Model for Measuring the Cardiothoracic Ratio on Chest Radiographs

OBJECTIVE

Prior studies on models based on deep learning (DL) and measuring the cardiothoracic ratio (CTR) on chest radiographs have lacked rigorous agreement analyses with radiologists or reader tests. We validated the performance of a commercially available DL-based CTR measurement model with various thoracic pathologies, and performed agreement analyses with thoracic radiologists and reader tests using a probabilistic-based reference.

MATERIALS AND METHODS

This study included 160 posteroanterior view chest radiographs (no lung or pleural abnormalities, pneumothorax, pleural effusion, consolidation, and n = 40 in each category) to externally test a DL-based CTR measurement model. To assess the agreement between the model and experts, intraclass or interclass correlation coefficients (ICCs) were compared between the model and two thoracic radiologists. In the reader tests with a probabilistic-based reference standard (Dawid-Skene consensus), we compared diagnostic measures-including sensitivity and negative predictive value (NPV)-for cardiomegaly between the model and five other radiologists using the non-inferiority test.

RESULTS

For the 160 chest radiographs, the model measured a median CTR of 0.521 (interquartile range, 0.446-0.59) and a mean CTR of 0.522 ± 0.095. The ICC between the two thoracic radiologists and between the model and two thoracic radiologists was not significantly different (0.972 versus 0.959, p = 0.192), even across various pathologies (all p-values > 0.05). The model showed non-inferior diagnostic performance, including sensitivity (96.3% versus 97.8%) and NPV (95.6% versus 97.4%) (p < 0.001 in both), compared with the radiologists for all 160 chest radiographs. However, it showed inferior sensitivity in chest radiographs with consolidation (95.5% versus 99.9%; p = 0.082) and NPV in chest radiographs with pleural effusion (92.9% versus 94.6%; p = 0.079) and consolidation (94.1% versus 98.7%; p = 0.173).

CONCLUSION

While the sensitivity and NPV of this model for diagnosing cardiomegaly in chest radiographs with consolidation or pleural effusion were not as high as those of the radiologists, it demonstrated good agreement with the thoracic radiologists in measuring the CTR across various pathologies.

Evaluating Health Disparities in Radiology Practices in New Jersey: Exploring Radiologist Geographical Distribution

OBJECTIVE

This study aimed to determine if a disproportionate number of radiologists practice in high-income versus low-income counties in New Jersey (NJ), identify which vulnerable populations are most in need of more radiologists, and discuss how these relative differences can ultimately influence health outcomes.

METHODS

The NJ Health Care Profile, a database overseen and maintained by the Division of Consumer Affairs, was queried for all actively practicing radiologists within the state of NJ. These results were grouped into diagnostic and interventional radiologists followed by further stratification of physicians based on the counties where they currently practice. The median household income and population size of each county for 2021 were obtained from the US Census database. The ratio of the population size of each county over the number of radiologists in that county was used as a surrogate marker for disparities in patient care within the state and was compared between counties grouped by levels of income.

RESULTS

Of the 1,186 board-certified radiologists actively practicing within the state of NJ, 86% are solely diagnostic radiologists and 14% are interventional radiologists. About 44% of radiologists practice within counties that are within the top one-third of median household income in NJ, 25% practice within counties in the middle one-third, and 31% practice within counties in the bottom one-third.

CONCLUSIONS

There is a disproportionate number of radiologists practicing in high-income counties as opposed to lower-income counties. A contradiction to this trend was noted in three low-income counties: Essex, Camden, and Atlantic County, all of which exhibited low numbers of individuals per radiologist that rivaled those of higher-income counties. This finding is a concrete measure of successful radiologist recruitment efforts within these counties during the past few years to combat the increased prevalence of disease and associated complications that historically marginalized communities tend to disproportionately exhibit. Other low-income counties should look to what Essex, Camden, and Atlantic County have done to increase radiologist recruitment to levels that rival those of high-income areas.

Artificial Intelligence-Based Software with CE Mark for Chest X-ray Interpretation: Opportunities and Challenges

Chest X-ray (CXR) is the most important technique for performing chest imaging, despite its well-known limitations in terms of scope and sensitivity. These intrinsic limitations of CXR have prompted the development of several artificial intelligence (AI)-based software packages dedicated to CXR interpretation. The online database "AI for radiology" was queried to identify CE-marked AI-based software available for CXR interpretation. The returned studies were divided according to the targeted disease. AI-powered computer-aided detection software is already widely adopted in screening and triage for pulmonary tuberculosis, especially in countries with few resources and suffering from high a burden of this disease. AI-based software has also been demonstrated to be valuable for the detection of lung nodules detection, automated flagging of positive cases, and post-processing through the development of digital bone suppression software able to produce digital bone suppressed images. Finally, the majority of available CE-marked software packages for CXR are designed to recognize several findings, with potential differences in sensitivity and specificity for each of the recognized findings.

Association between thoracic and third lumbar CT-derived muscle mass and density in Caucasian patients without chronic disease: a proof-of-concept study

BACKGROUND

Computed tomography (CT) is increasingly used in the clinical workup, and existing scan contains unused body composition data, potentially useful in a clinical setting. However, there is no healthy reference for contrast-enhanced thoracic CT-derived muscle measures. Therefore, we aimed at investigating whether there is a correlation between each of the thoracic and third lumbar vertebra level (L3) skeletal muscle area (SMA), skeletal muscle index (SMI), and skeletal muscle density (SMD) at contrast-enhanced CT in patients without chronic disease.

METHODS

A proof-of-concept retrospective observational study was based on Caucasian patients without chronic disease, who received CT for trauma between 2012 and 2014. Muscle measures were assessed using a semiautomated threshold-based software by two raters independently. Pearson's correlation between each thoracic level and third lumbar and intraclass correlation between two raters and test-retest with SMA as proxy parameters were used.

RESULTS

Twenty-one patients (11 males, 10 females; median age 29 years) were included. The second thoracic vertebra (T2) had the highest median of cumulated SMA (males 314.7 cm², females 118.5 cm²) and SMI (97.8 cm²/m² and 70.4 cm²/m², respectively). The strongest SMA correlation was observed between T5 and L3 (r = 0.970), the SMI between T11 and L3 (r = 0.938), and the SMD between the T10 and L3 (r = 0.890).

CONCLUSIONS

This study suggests that any of the thoracic levels can be valid to assess skeletal muscle mass. However, the T5 may be most favourable for measuring SMA, the T11 for SMI, and T10 for SMD when using contrast-enhanced thoracic CT.

RELEVANCE STATEMENT

In COPD patients, a CT-derived thoracic muscle mass assessment may help identify who would benefit from focused pulmonary rehabilitation: thoracic contrast-enhanced CT conducted as part of the standard clinical workup can be used for this evaluation.

KEY POINTS

• Any thoracic level can be used to assess thoracic muscle mass. • Thoracic level 5 is strongly associated with the 3rd lumbar muscle area. • A strong correlation between the thoracic level 11 and the 3rd lumbar muscle index. • Thoracic level 10 is strongly associated with the 3rd lumbar muscle density.

A Global Review of the Impacts of the Coronavirus (COVID-19) Pandemic on Radiology Practice, Finances, and Operations

The Coronavirus Disease 2019 (COVID-19) pandemic ushered in rapid changes in healthcare, including radiology, globally. This review discusses the impact of the pandemic on various radiology departments globally. We analyze the implications of the COVID-19 pandemic on the imaging volumes, finances, and clinical operations of radiology departments in 2020. Studies from health systems and outpatient imaging centers were analyzed, and the activity throughout 2020 was compared to the pre-pandemic activity, including activity during similar timeframes in 2019. Imaging volumes across modalities, including MRI and CT scans, were compared, as were the Relative Value Units (RVUs) for imaging finances. Furthermore, we compared clinical operations, including staffing and sanitation procedures. We found that imaging volumes in private practices and academic centers decreased globally. The decreases in volume could be attributed to delayed patient screenings, as well as the implementation of protocols, such as the deep cleaning of equipment between patients. Revenues from imaging also decreased globally, with many institutions noting a substantial decline in RVUs and revenue compared with pre-COVID-19 levels. Our analysis thus found significant changes in the volumes, finances, and operations of radiology departments due to the COVID-19 pandemic.

Performance and Agreement When Annotating Chest X-ray Text Reports—A Preliminary Step in the Development of a Deep Learning-Based Prioritization and Detection System

A chest X-ray report is a communicative tool and can be used as data for developing artificial intelligence-based decision support systems. For both, consistent understanding and labeling is important. Our aim was to investigate how readers would comprehend and annotate 200 chest X-ray reports. Reports written between 1 January 2015 and 11 March 2022 were selected based on search words. Annotators included three board-certified radiologists, two trained radiologists (physicians), two radiographers (radiological technicians), a non-radiological physician, and a medical student. Consensus labels by two or more of the experienced radiologists were considered “gold standard”. Matthew’s correlation coefficient (MCC) was calculated to assess annotation performance, and descriptive statistics were used to assess agreement between individual annotators and labels. The intermediate radiologist had the best correlation to “gold standard” (MCC 0.77). This was followed by the novice radiologist and medical student (MCC 0.71 for both), the novice radiographer (MCC 0.65), non-radiological physician (MCC 0.64), and experienced radiographer (MCC 0.57). Our findings showed that for developing an artificial intelligence-based support system, if trained radiologists are not available, annotations from non-radiological annotators with basic and general knowledge may be more aligned with radiologists compared to annotations from sub-specialized medical staff, if their sub-specialization is outside of diagnostic radiology.

Novel measures for the diagnosis of hepatic steatosis using contrast-enhanced computer tomography images

PURPOSE

Hepatic steatosis is often diagnosed non-invasively. Various measures and accompanying diagnostic thresholds based on contrast-enhanced CT and virtual non-contrast images have been proposed. We compare these established criteria to novel and fully automated measures.

METHOD

CT data sets of 197 patients were analyzed. Regions of interest (ROIs) were manually drawn for the liver, spleen, portal vein, and aorta to calculate four established measures of liver-fat. Two novel measures capturing the deviation between the empirical distributions of HU measurements across all voxels within the liver and spleen were calculated. These measures were calculated with both manual ROIs and using fully automated organ segmentations. Agreement between the different measures was evaluated using correlational analysis, as well as their ability to discriminate between fatty and healthy liver.

RESULTS

Established and novel measures of fatty liver were at a high level of agreement. Novel methods were statistically indistinguishable from the established ones when taking established diagnostic thresholds or physicians' diagnoses as ground truth and this high performance level persisted for automatically selected ROIs.

CONCLUSION

Automatically generated organ segmentations led to comparable results as manual ROIs, suggesting that the implementation of automated methods can prove to be a valuable tool for incidental diagnosis. Differences in the distribution of HU measurements across voxels between liver and spleen can serve as surrogate markers for the liver-fat-content. Novel measures do not exhibit a measurable disadvantage over established methods based on simpler measures such as across-voxel averages in a population with low incidence of fatty liver.

Gender Differences in the Impact of Recommendations on Diagnostic Imaging Tests: A Retrospective Study 2007-2021

(1) Background: The frequency of imaging tests grew exponentially in recent years. This increase may differ according to a patient's sex, age, or socioeconomic status. We aim to analyze the impact of the Council Directive 2013/59/Euratom to control exposure to radiation for men and women and explore the impact of patients' age and socioeconomic status; (2) Methods: The retrospective observational study that includes a catchment population of 234,424. We included data of CT, mammography, radiography (conventional radiography and fluoroscopy) and nuclear medicine between 2007-2021. We estimated the associated radiation effective dose per test according using previously published evidence. We calculated a deprivation index according to the postcode of their residence. We divided the study in 2007-2013, 2014-2019 and 2020-2021 (the pandemic period). (3) Results: There was an increase in the number of imaging tests received by men and women after 2013 (p < 0.001), and this increase was higher in women than in men. The frequency of imaging tests decreased during the pandemic period (2020-2021), but the frequency of CT and nuclear medicine tests increased even during these years (p < 0.001) and thus, the overall effective mean dose. Women and men living in the least deprived areas had a higher frequency of imaging test than those living in the most deprived areas. (4) Conclusions: The largest increase in the number of imaging tests is due to CTs, which account for the higher amount of effective dose. The difference in the increase of imaging tests carried out in men and women and according to the socioeconomic status could reflect different management strategies and barriers to access in clinical practice. Given the low impact of the available recommendations on the population exposure to radiation and the performance of high-dose procedures such as CT, deserve special attention when it comes to justification and optimization, especially in women.

The Impact of Data Management on the Achievable Dose and Efficiency of Mammography and Radiography During the COVID-19 Era: A Facility-Based Cohort Study

Purpose

To evaluate the impact of using computational data management resources and analytical software on radiation doses in mammography and radiography during the COVID-19 pandemic, develop departmental diagnostic reference levels (DRLs), and describe achievable doses (ADs) for mammography and radiography based on measured dose parameters.

Patients and Methods

This ambispective cohort study enrolled 795 and 12,115 patients who underwent mammography and radiography, respectively, at the King Fahd Hospital of the University, Al-Khobar City, Saudi Arabia between May 25 and November 4, 2021. Demographic data were acquired from patients' electronic medical charts. Data on mammographic and radiographic dose determinants were acquired from the data management software. Based on the time when the data management software was operational in the institute, the study was divided into the pre-implementation and post-implementation phases. Continuous and categorical variables were compared between the two phases using an unpaired t-test and the chi-square test.

Results

The median accumulated average glandular dose (AGD; a mammographic dose determinant) in the post-implementation phase was three-fold higher than that in the pre-implementation phase. The average mammographic exposure time in the post-implementation phase was 16.3 ms shorter than that in the pre-implementation phase. Furthermore, the median values of the dose area product ([DAP], a radiographic dose determinant) were 9.72 and 19.4 cGycm² in the pre-implementation and post-implementation phases, respectively.

Conclusion

Although the data management software used in this study helped reduce the radiation exposure time by 16.3 ms in mammography, its impact on the mean accumulated AGD was unfavorable. Similarly, radiographic exposure indices, including DAP, tube voltage, tube current, and exposure time, were not significantly different after the data management software was implemented. Close monitoring of patient radiation doses in mammography and radiography, and dose reduction will become possible if imaging facilities use DRLs and ADs via automated systems.

The Assessment of the Rationale for Urgent Head CT-Comparative Analysis of Referrals and Results of Examinations without and with Contrast Enhancement

The study analyzes the correlation between the indications and results of head CT examinations in search of evidence of the excessive use of this diagnostic method. In total, 1160 referrals for urgent head CT were analyzed retrospectively, including the following parameters: patients’ sex and age, type of scan (C−, C+, angio-CT), description of symptoms and presence of diagnostic target. Pathologies identified by the radiologist were assigned to four classes, regarding the severity of diagnosed conditions. The analysis of the CT results has shown that over half (55.22%) of the examinations revealed no deviations or showed chronic, asymptomatic lesions. As many as 73.71% referrals constituted group 0 in terms of the lack of a diagnostic target of a specific pathology. The presence of specific clinical targeting in a referral correlated significantly with a higher frequency of acute diagnosis. Contrast-enhanced follow-up examinations allowed the unequivocal classification of patients into extreme classes (I or IV) and accurate identification of patients requiring urgent or chronic treatment. Excessive use of diagnostic imaging is harmful, not only to patients, who often are unnecessarily exposed to radiation, but also to the quality of healthcare, since it increases the costs and radiologists’ workload.

The Impact of Iodine Concentration Disorders on Health and Cancer

Iodine deficiency is an ongoing problem. The implementation of salt iodization has significantly reduced the effects of iodine deficiency worldwide in recent years, and the remaining iodine deficiency is mild to moderate. Iodine is an essential substrate for the synthesis of thyroid hormones in the thyroid gland. It can also act as an antioxidant, as well as an anti-proliferative and pro-apoptotic factor. Pregnant women, breastfeeding women, and children are particularly affected by iodine deficiency. It leads to thyroid diseases and metabolic and developmental disorders, as well as cancer. However, an excessive iodine intake may, similarly to iodine deficiency, lead to the development of goiter, and toxic amounts of iodine can lead to thyroiditis, hyperthyroidism, and hypothyroidism, and even to the development of papillary thyroid cancer. Correcting iodine deficiency potentially reduces the chance of developing malignancies. Additional research is needed to better understand both the effect of iodine on carcinogenesis and the clinical outcome of iodine deficiency compensation on cancer patients' prognosis. The upcoming public health challenge appears to be reducing salt consumption, which could result in a lower iodine intake. Thus, an iodine enrichment vehicle other than salt could be considered if salt iodine levels are not increased to compensate, and urine iodine levels should be monitored more frequently.

Model for ASsessing the value of Artificial Intelligence in medical imaging (MAS-AI)

Objectives

Artificial intelligence (AI) is seen as a major disrupting force in the future healthcare system. However, the assessment of the value of AI technologies is still unclear. Therefore, a multidisciplinary group of experts and patients developed a Model for ASsessing the value of AI (MAS-AI) in medical imaging. Medical imaging is chosen due to the maturity of AI in this area, ensuring a robust evidence-based model.

Methods

MAS-AI was developed in three phases. First, a literature review of existing guides, evaluations, and assessments of the value of AI in the field of medical imaging. Next, we interviewed leading researchers in AI in Denmark. The third phase consisted of two workshops where decision makers, patient organizations, and researchers discussed crucial topics for evaluating AI. The multidisciplinary team revised the model between workshops according to comments.

Results

The MAS-AI guideline consists of two steps covering nine domains and five process factors supporting the assessment. Step 1 contains a description of patients, how the AI model was developed, and initial ethical and legal considerations. In step 2, a multidisciplinary assessment of outcomes of the AI application is done for the five remaining domains: safety, clinical aspects, economics, organizational aspects, and patient aspects.

Conclusions

We have developed an health technology assessment-based framework to support the introduction of AI technologies into healthcare in medical imaging. It is essential to ensure informed and valid decisions regarding the adoption of AI with a structured process and tool. MAS-AI can help support decision making and provide greater transparency for all parties.