Deep learning performance compared to healthcare experts in detecting wrist fractures from radiographs: A systematic review and meta-analysis.

OBJECTIVE: To perform a systematic review and meta-analysis of the diagnostic accuracy of deep learning (DL) algorithms in the diagnosis of wrist fractures (WF) on plain wrist radiographs, taking healthcare experts consensus as reference standard. METHODS: Embase, Medline, PubMed, Scopus and Web of Science were searched in the period from 1 Jan 2012 to 9 March 2023. Eligible studies were patients with wrist radiographs for radial and ulnar fractures as the target condition, studies using DL algorithms based on convolutional neural networks (CNN), and healthcare experts consensus as the minimum reference standard. Studies were assessed with a modified QUADAS-2 tool, and we applied a bivariate random-effects model for meta-analysis of diagnostic test accuracy data. RESULTS: Our study was registered at PROSPERO with ID: CRD42023431398. We included 6 unique studies for meta-analysis, with a total of 33,026 radiographs. CNN performance compared to reference standards for the included articles found a summary sensitivity of 92% (95% CI: 80%-97%) and a summary specificity of 93% (95% CI: 76%-98%). The generalized bivariate I-squared statistic indicated considerable heterogeneity between the studies (81.90%). Four studies had one or more domains at high risk of bias and two studies had concerns regarding applicability. CONCLUSION: The diagnostic accuracy of CNNs was comparable to that of healthcare experts in wrist radiographs for investigation of WF. There is a need for studies with a robust reference standard, external data-set validation and investigation of diagnostic performance of healthcare experts aided with CNNs. CLINICAL RELEVANCE STATEMENT: DL matches healthcare experts in diagnosing WFs, which potentially benefits patient diagnosis.

Nordic radiographers' and students' perspectives on artificial intelligence - A cross-sectional online survey.

INTRODUCTION: The integration of artificial intelligence (AI) into the domain of radiography holds substantial potential in various aspects including workflow efficiency, image processing, patient positioning, and quality assurance. The successful implementation of AI within a Radiology department necessitates the participation of key stakeholders, particularly radiographers. The study aimed to provide a comprehensive investigation about Nordic radiographers' perspectives and attitudes towards AI in radiography. METHODS: An online 29-item survey was distributed via social media platforms to Nordic students and radiographers working in Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Islands including items on demographics, specialization, educational background, place of work and perspectives and knowledge on AI. The items were a mix of closed-type and scaled questions, with the option for free-text responses when relevant. RESULTS: The survey received responses from all Nordic countries with 586 respondents, 26.8% males, 72.1% females, and 1.1% non-binary/self-defined or preferred not to say. The mean age was 37.2 with a standard deviation (SD) of ±12.1 years, and the mean number of years since qualification was 14.2 SD ± 10.3 years. A total of 43% (n = 254) of the respondents had not received any AI training in clinical practice. Whereas 13% (n = 76) had received AI during radiography undergrad training. A total of 77.9% (n = 412) expressed interest in pursuing AI education. The majority of respondents were aware of the potential use of AI (n = 485, 82.8%) and 39.1% (n = 204) had no reservations about AI. CONCLUSION: Overall, this study found that Nordic radiographers have a positive attitude toward AI. Very limited training or education has been provided to the radiographers. Especially since 82.8% reports on plans to implement AI in clinical practice. In general, awareness of AI applications is high, but the educational level is low for Nordic radiographers. IMPLICATION FOR PRACTICE: This study emphasises the favourable view of AI held by students and Nordic radiographers. However, there is a need for continuous professional development to facilitate the implementation and effective utilization of AI tools within the field of radiography.

A Nordic survey on artificial intelligence in the radiography profession - Is the profession ready for a culture change?

INTRODUCTION: The impact of artificial intelligence (AI) on the radiography profession remains uncertain. Although AI has been increasingly used in clinical radiography, the perspectives of the radiography professionals in Nordic countries have yet to be examined. The primary aim was to examine views of Nordic radiographers 'on AI, with focus on perspectives, engagement, and knowledge of AI. METHODS: Radiographers from Denmark, Norway, Sweden, Iceland, Greenland, and the Faroe Island were invited through social media platforms to participate in an online survey from March to June 2023. The survey encompassed 29-items and included 4 sections a) demographics, b) barriers and enablers on AI, c) perspectives and experiences of AI and d) knowledge of AI in radiography. Edgars Schein's model of organizational culture was employed to analyse Nordic radiographers' perspectives on AI. RESULTS: Overall, a total of 421 respondents participated in the survey. A majority were positive/somewhat positive towards AI in radiography e.g., 77.9 % (n = 342) thought that AI would have a positive effect on the profession, and 26% thought that AI would reduce the administrative workload. Most radiographers agreed or strongly agreed that clinicians may have access to AI generated reports (76.8 %, n = 297). Nevertheless, a total of 86 (20.1%) agree or somewhat agreed that AI a potential risk for radiography. CONCLUSION: Nordic radiographers are generally positive towards AI, yet uncertainties regarding its implementation persist. The findings underscore the importance of understanding these challenges for the responsible integration of AI systems. Carefully weighing the expected influence of AI against key incentives will support a seamless integration of AI for the benefit not just of the patients, but also of the radiography profession. IMPLICATIONS FOR PRACTICE: Understanding incentives factors and barriers can help address uncertainties during implementation of AI in clinical practice.

Virtual reality simulation for mastery learning of wrist radiograph technique.

INTRODUCTION: Virtual reality (VR) simulation is a technology that empowers students and radiographers to practice radiography in a virtual environment that resembles real-life clinical scenarios. The purpose of this randomised study was to examine the relationship between clinical specialty and the ability to assess and obtain a lateral wrist radiograph using a VR simulator. METHODS: Radiographers and radiography students were recruited from the EFRS Research Hub at the 2024 European Congress of Radiology. After completing a background questionnaire, participants entered a VR simulator where they assessed lateral wrist radiographs and, if necessary, attempted a retake. Fisher's exact test was used to evaluate the relationship between specialties and participants' ability to assess positioning and perform retakes. Rank-biserial correlation estimated the relationship between participants' ability to reposition the VR patient and their VR experience and self-perceived confidence in wrist radiograph positioning. RESULTS: The cohort included 173 participants from 14 specialties across 21 countries. There was a borderline significant trend between clinical specialty and correct positioning assessment (p = 0.052) and between self-perceived confidence in acquiring wrist radiographs and repositioning for a retake (p = 0.052). Neither clinical specialty (p = 0.480) nor previous VR experience (p = 0.409) correlated with ability to reposition for a retake. CONCLUSION: While results indicated a potential correlation between participants' ability to position a VR patient and both clinical specialty and confidence in wrist radiography, these trends were not statistically significant. Nevertheless, the findings suggest that VR holds promise for radiography training, though further research is necessary to explore the factors that influence performance and learning. IMPLICATIONS FOR PRACTICE: The incorporation of VR technology into standard radiography training programs could potentially improve patient outcomes by ensuring that radiography students are more skilled at acquiring quality radiographs prior to their first clinical practice. It should be noted though, that knowledge on positioning criteria and anatomy is an advantage when practicing correct positioning in a VR simulator.

The effect of Gaussian noise on pneumonia detection on chest radiographs, using convolutional neural networks.

INTRODUCTION: Chest X-rays (CXR) with under-exposure increase image noise and this may affect convolutional neural network (CNN) performance. This study aimed to train and validate CNNs for classifying pneumonia on CXR as normal or pneumonia acquired at different image noise levels. METHODS: The study used the curated and publicly available "Chest X-Ray Pneumonia" dataset of 5856 AP CXR classified into 1583 normal, 4273 viral and bacterial pneumonia cases. Gaussian noise with zero mean was added to the images, at 5 image noise variance levels, corresponding to decreasing exposure. Each noise-level dataset was split into 80% for training, 10% for validation, and 10% for test data and then classified using custom trained sequential CNN architecture. Six classification tasks were developed for five Gaussian noise levels and the original dataset. Sensitivity, specificity, predictive values and accuracy were used as evaluation performance metrics. RESULTS: CNN evaluation on the different datasets revealed no performance drop from the original dataset to the five datasets with different noise levels. Sensitivity, specificity and accuracy for the normal datasets were 98.7%, 76.1% and 90.2%. For the five Gaussian noise levels the sensitivity, specificity and accuracy ranged from 96.9% to 98.2%, 94.4%-98.7% and 96.8%-97.6%, respectively. A heat map was used for visual explanation of the CNNs. CONCLUSION: The CNNs sensitivity maintained, and the specificity increased in distinguishing between normal and pneumonia CXR with the introduction of image noise. IMPLICATIONS FOR PRACTICE: No performance drops of CNNs in distinguishing cases with and without pneumonia CXR with different Gaussian noise levels was observed. This has potential for decreasing radiation dose to patients or maintaining exposure parameters for patients that require additional radiographs.

Low-cost 3D-printed anthropomorphic cardiac phantom, for computed tomography automatic left ventricle segmentation and volumetry - A pilot study.

INTRODUCTION: Accurate cardiac left ventricle (LV) delineation is essential to CT-derived left ventricular ejection fraction (LVEF). To evaluate dose-reduction potential, an anatomically accurate heart phantom, with realistic X-ray attenuation is required. We demonstrated and tested a custom-made phantom using 3D-printing, and examined the influence of image noise on automatically measured LV volumes METHODS: A single coronary CT angiography (CCTA) dataset was segmented and converted to Standard Tessellation Language (STL) mesh, using open-source software. A 3D-printed model, with hollow left heart chambers, was printed and cavities filled with gelatinized contrast media. This was CT-scanned in an anthropomorphic chest phantom, at different exposure conditions. LV and "myocardium" noise and attenuation was measured. LV volume was automatically measured using two different methods. We calculated Spearmans' correlation of LV volume with noise and contrast-noise ratio respectively om 486 scans of the phantom. Source images were compared to one phantom series with similar parameters. This was done using Dice coefficient on LV short-axis segmentations. RESULTS: Phantom "Myocardium" and LV attenuation was comparable to measurements on source images. Automatic volume measurement succeeded, with mean volume deviation to patient images less than 2 ml. There was a moderate correlation of volume with CNR, and strong correlation of volume with image noise. With papillary muscles included in LV volume, the correlation was positive, but negative when excluded. Variation of volumes was lowest at 90-100 kVp for both methods in the 486 repeat scans. The Dice coefficient was 0.87, indicating high overlap between the single phantom series and source scan. Cost of 3D-printer and materials was 400 and 30 Euro respectively. CONCLUSION: Both anatomically and radiologically the phantom mimicked the source scans closely. LV volumetry was reliably performed with automatic algorithms. IMPLICATIONS FOR PRACTICE: Patient-specific cardiac phantoms may be produced at minimal cost and can potentially be used for other anatomies and pathologies. This enables radiographic phantom studies without need for dedicated 3D-labs or expensive commercial phantoms.

Pill or pump? Nitroglycerin 0.5 mg tablet vs 0.8 mg spray: Effect on proximal vessel diameters at Coronary CT Angiography (CCTA).

INTRODUCTION: The administration of sublingual Nitroglycerin (NTG) prior to CT coronary angiography (CCTA) can be perfomed using pump spray or tablets. Choice of method seems to be based on local preference, rather than published guidelines. This retrospective analysis tested whether proximal coronary diameters differed dependent on the sublingual administration of 0.5 mg Nitroglycerin (NTG) tablets or 0.8 mg NTG spray. METHODS: 287 ECG-gated CCTA studies with optimal image quality and Agatston scores<400 were included in this retrospective analysis. 143 of the patients were dosed with NTG tablets at a dose of 0.5 mg prior to CCTA. 144 patients received 2 puffs of 0.4 mg NTG spray for a total dose of 8 mg. All were scanned on a second-generation Dual Source CT. Diameters of proximal segments of Left Main (LM), Right (RCA), Left Anterior (LAD) and circumflex (CX) coronary arteries were measured using semi-automatic electronic callipers by two blinded readers. Results were summarised as the mean of maximum and minimum diameters. Sex-specific analysis of diameters was carried out using repeated-measures ANOVA for each vessel. Agreement between readers was examined with Bland-Altman analysis and intra-class-correlation coefficient (ICC). RESULTS: No significant differences in coronary diameters were found except in the RCA for women and LM for men. In both cases, diameters were smaller in the spray group (11 and 9%, respectively). Reader agreement was excellent, with ICC>0.96 for all vessels, and no significant bias, except in CX (0.03 mm). CONCLUSIONS: We found no evidence for the systematic superiority of either administration method in proximal coronary vessels. IMPLICATIONS FOR PRACTICE: Choosing between tablet or spray NTG prior to CCTA can be guided by practical, economical and hygienic considerations alone.

Collimation border with U-Net segmentation on chest radiographs compared to radiologists.

INTRODUCTION: Chest Radiography (CXR) is a common radiographic procedure. Radiation exposure to patients should be kept as low as reasonably achievable (ALARA), and monitored continuously as part of quality assurance (QA) programs. One of the most effective dose reduction tools is proper collimation practice. The purpose of this study is to determine whether a U-Net convolutional neural networks (U-CNN) can be trained to automatically segment the lungs and calculate an optimized collimation border on a limited CXR dataset. METHODS: 662 CXRs with manual lung segmentations were obtained from an open-source dataset. These were used to train and validate three different U-CNNs for automatic lung segmentation and optimal collimation. The U-CNN dimensions were 128 × 128, 256 × 256, and 512 × 512 pixels and validated with five-fold cross validation. The U-CNN with the highest area under the curve (AUC) was tested externally, using a dataset of 50 CXRs. Dice scores (DS) were used to compare U-CNN segmentations with manual segmentations by three radiographers and two junior radiologists. RESULTS: DS for the three U-CNN dimensions with segmentation of the lungs ranged from 0.93 to 0.96, respectively. DS of the collimation border for each U-CNN was 0.95 compared to the ground truth labels. DS for lung segmentation and collimation border between the junior radiologists was 0.97 and 0.97. One radiographer differed significantly from the U-CNN (p = 0.016). CONCLUSION: We demonstrated that a U-CNN could reliably segment the lungs and suggest a collimation border with great accuracy compared to junior radiologists. This algorithm has the potential to automate collimation auditing of CXRs. IMPLICATIONS FOR PRACTICE: Creating an automatic segmentation model of the lungs can produce a collimation border, which can be used in CXR QA programs.

Image quality of bedside chest radiographs in intensive care beds with integrated detector tray: A phantom study.

INTRODUCTION: Vendors offer intensive care beds with integrated detector trays for bedside radiography, promoting better ergonomics and patient comfort. However, no documentation of the effects on diagnostic image quality has been located. This study examines measured and subjective image quality of supine bedside chest radiographs with and without use of such a detector tray. METHODS: A contrast-detail phantom (CDRAD 2.0) was exposed using standard supine chest exposure parameters. Plexiglass plates of 16 and 21 cm were placed in front to simulate patient attenuation for standard and adipose patients. Exposures were repeated with the detector placed in tray and directly in bed. Images were analysed using dedicated software giving a figure-of-merit IQFinv. Results were compared using ANOVA. Then an anthropomorphic chest phantom (Lungman) was exposed using the same parameters, and the same placements of the detector. Exposures were done with and without extra conformal tissue to simulate varying patient sizes, and with and without added typical intensive care equipment. Images were analysed by two radiologists using a three-point scale, on five image quality criteria. Radiologist also stated whether the images were sufficient for diagnosis. Results were compared using Visual Grading Characteristics, using dedicated software, resulting in Areas Under the Curve (AUC-VGC) for each combination and criteria. Inter- and intra-rater reliability were assessed with kappa statistics. Composite Visual Grading Analysis (VGAS) scores were calculated for each image. Both IQFinv and were normalized and compared. RESULTS: For all criteria both IQFinv and AUC-VGC was significantly better when exposing the detector directly in bed, than with the detector placed in the tray across all exposures. When stratified into thin and adipose patients, IQFinv decreased significantly for thin patients, while VGAS-scores did not. For adipose patients, both figures were significantly lower with the detector in the tray. CONCLUSION: Use of detector tray for bedside chest imaging decreases image quality. IMPLICATIONS FOR PRACTICE: Radiographers should critically evaluate image quality and experimentally determine optimal exposure factors, when taking equipment with integrated trays into use.

Anode heel effect: Does it impact image quality in digital radiography? A systematic literature review.

INTRODUCTION: The anode heel effect can be used to optimize image quality and/or patient dose in digital radiography (DR). In film-screen radiography, the effect can equalize optical density in regions of varying attenuation. Clinical experience suggests that the implementation of DR has led to less awareness of anode orientation. Post-processing is assumed to compensate, but may also alter image impression and potentially obscure image details. Published evidence was examined for the influence of the anode heel effect on image quality in DR. METHOD: A systematic literature search was carried out using PubMed, Embase, and Web of Science databases. Title and abstracts were screened blinded by three authors, according to in-/exclusion criteria, followed by full-text analysis for final inclusion. Studies where technical and/or visual image quality were reported, was included. All studies were analyzed and assigned quality scores, according to relevant questions. The authors devised a scoring system based on reported information pertaining to reproducibility, interpretation, and generalizability of the methods and conclusions. RESULTS: Five studies were included of heterogeneous design, each with methodological shortcomings. Only a few anatomical areas were covered. Very few patients were examined, and in no studies were images evaluated by radiologists or reporting radiographers. Relevant information such as post-processing, image quality criteria and analysis was insufficient in most studies, making reproduction difficult. Results were contradictory, especially concerning technical vs visual image quality. CONCLUSION: Limited published evidence was found quantifying the influence of the anode heel effect on image quality using DR technology. More methodologically, robust studies are needed. The published evidence neither proves nor disproves the impact of the heel effect on image quality in DR. IMPLICATIONS FOR PRACTICE: Based on a systematic review, no firm recommendations for anode orientation relating to image quality in DR can be provided.