Errors in imaging patients in the emergency setting

Potential strength and weakness of artificial intelligence integration in emergency radiology: a review of diagnostic utilizations and applications in patient care optimization

Artificial intelligence (AI) and its recent increasing healthcare integration has created both new opportunities and challenges in the practice of radiology and medical imaging. Recent advancements in AI technology have allowed for more workplace efficiency, higher diagnostic accuracy, and overall improvements in patient care. Limitations of AI such as data imbalances, the unclear nature of AI algorithms, and the challenges in detecting certain diseases make it difficult for its widespread adoption. This review article presents cases involving the use of AI models to diagnose intracranial hemorrhage, spinal fractures, and rib fractures, while discussing how certain factors like, type, location, size, presence of artifacts, calcification, and post-surgical changes, affect AI model performance and accuracy. While the use of artificial intelligence has the potential to improve the practice of emergency radiology, it is important to address its limitations to maximize its advantages while ensuring the safety of patients overall.

Easily missed pathologies of the musculoskeletal system in the emergency radiology setting

The musculoskeletal region is the main area in terms of easily missed pathologies in the emergency radiology setting, because the majority of diagnoses missed in the emergency setting are fractures.A review of the literature was performed by searching the PubMed and ScienceDirect databases, using the keywords ('missed injuries' or 'missed fractures') and ('emergency radiology' or 'emergency room') and ('musculoskeletal' or 'bone' or 'skeleton') for the title and abstract query. The inclusion criteria were scientific papers presented in the English and German languages. Among the 347 relevant hits between 1980 and 2024 as identified by the author of this review article, there were 114 relevant articles from the years between 2018 and 2024. Based on this literature search and the author's personal experience, this study presents useful information for reducing the number of missed pathologies in the musculoskeletal system in the emergency radiology setting.Predominant factors that make up the majority of missed fractures are 'subtle but still visible fractures' and 'radiographically imperceptible fractures'. Radiologists are able to minimize the factors contributing to fractures being missed. For example, implementing a 'four-eyes principle', i.e., two readers read the radiographs, would help to overcome the missing of 'subtle but still visible fractures' and the additional use of cross-sectional imaging would help to overcome the missing of 'radiographically imperceptible fractures'. Knowledge of what is commonly missed and evaluation of high-risk areas with utmost care also increase the diagnostic performance of radiologists. · Radiological imaging in an emergency setting increases the likelihood of radiological diagnostic errors, such as missing musculoskeletal pathologies.. · The majority of diagnoses missed in the emergency setting are fractures.. · To lessen the number of easily missed pathologies in the musculoskeletal system in the emergency radiology setting, a systematic approach is necessary.. · Adequate training of radiologists in emergency radiology and close collaboration with clinical partners are important measures to decrease the number of missed musculoskeletal injuries.. · Weber MA. Easily missed pathologies of the musculoskeletal system in the emergency radiology setting. Fortschr Röntgenstr 2024; DOI 10.1055/a-2369-8330.

Deploying artificial intelligence in the detection of adult appendicular and pelvic fractures in the Singapore emergency department after hours: efficacy, cost savings and non-monetary benefits

INTRODUCTION

Radiology plays an integral role in fracture detection in the emergency department (ED). After hours, when there are fewer reporting radiologists, most radiographs are interpreted by ED physicians. A minority of these interpretations may miss diagnoses, which later require the callback of patients for further management. Artificial intelligence (AI) has been viewed as a potential solution to augment the shortage of radiologists after hours. We explored the efficacy of an AI solution in the detection of appendicular and pelvic fractures for adult radiographs performed after hours at a general hospital ED in Singapore, and estimated the potential monetary and non-monetary benefits.

METHODS

One hundred and fifty anonymised abnormal radiographs were retrospectively collected and fed through an AI fracture detection solution. The radiographs were re-read by two radiologist reviewers and their consensus was established as the reference standard. Cases were stratified based on the concordance between the AI solution and the reviewers' findings. Discordant cases were further analysed based on the nature of the discrepancy into overcall and undercall subgroups. Statistical analysis was performed to evaluate the accuracy, sensitivity and inter-rater reliability of the AI solution.

RESULTS

Ninety-two examinations were included in the final study radiograph set. The AI solution had a sensitivity of 98.9%, an accuracy of 85.9% and an almost perfect agreement with the reference standard.

CONCLUSION

An AI fracture detection solution has similar sensitivity to human radiologists in the detection of fractures on ED appendicular and pelvic radiographs. Its implementation offers significant potential measurable cost, manpower and time savings.

Diagnostic Accuracy of Ultra-Low Dose CT Compared to Standard Dose CT for Identification of Fresh Rib Fractures by Deep Learning Algorithm

The present study aimed to evaluate the diagnostic accuracy of ultra-low dose computed tomography (ULD-CT) compared to standard dose computed tomography (SD-CT) in discerning recent rib fractures using a deep learning algorithm detection of rib fractures (DLADRF). A total of 158 patients undergoing forensic diagnosis for rib fractures were included in this study: 50 underwent SD-CT, and 108 were assessed using ULD-CT. Junior and senior radiologists independently evaluated the images to identify and characterize the rib fractures. The sensitivity of rib fracture diagnosis by radiologists and radiologist + DLADRF was better using SD-CT than ULD-CT. However, the diagnosis sensitivity of DLADRF using ULD-CT alone was slightly more than SD-CT. Nonetheless, no substantial differences were observed in specificity, positive predictive value, and negative predictive value between SD-CT and ULD-CT by the same radiologist, radiologist + DLADRF, and DLADRF (P > 0.05). The area under the curve (AUC) of receiver operating characteristic indicated that senior radiologist + DLADRF was significantly better than senior and junior radiologists, junior radiologists + DLADRF, and DLADRF alone using SD-CT or ULD-CT (all P < 0.05). Also, junior radiologists + DLADRF was better with ULD-CT than senior and junior radiologists (P < 0.05). The AUC of the rib fracture diagnosed by senior radiologists did not differ from DLADRF using ULD-CT. Also, no significant differences were observed between junior + AI and senior and between junior and DLADRF using SD-CT. DLADRF enhanced the diagnostic performance of radiologists in detecting recent rib fractures. The diagnostic outcomes between SD-CT and ULD-CT across radiologists' experience and DLADRF did not differ significantly.

Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans

Computed tomography (CT) is the most commonly used diagnostic modality for blunt abdominal trauma (BAT), significantly influencing management approaches. Deep learning models (DLMs) have shown great promise in enhancing various aspects of clinical practice. There is limited literature available on the use of DLMs specifically for trauma image evaluation. In this study, we developed a DLM aimed at detecting solid organ injuries to assist medical professionals in rapidly identifying life-threatening injuries. The study enrolled patients from a single trauma center who received abdominal CT scans between 2008 and 2017. Patients with spleen, liver, or kidney injury were categorized as the solid organ injury group, while others were considered negative cases. Only images acquired from the trauma center were enrolled. A subset of images acquired in the last year was designated as the test set, and the remaining images were utilized to train and validate the detection models. The performance of each model was assessed using metrics such as the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, specificity, positive predictive value, and negative predictive value based on the best Youden index operating point. The study developed the models using 1302 (87%) scans for training and tested them on 194 (13%) scans. The spleen injury model demonstrated an accuracy of 0.938 and a specificity of 0.952. The accuracy and specificity of the liver injury model were reported as 0.820 and 0.847, respectively. The kidney injury model showed an accuracy of 0.959 and a specificity of 0.989. We developed a DLM that can automate the detection of solid organ injuries by abdominal CT scans with acceptable diagnostic accuracy. It cannot replace the role of clinicians, but we can expect it to be a potential tool to accelerate the process of therapeutic decisions for trauma care.

Trends in non-focal neurological chief complaints and CT angiography utilization among adults in the emergency department

Prudent imaging use is essential for cost reduction and efficient patient triage. Recent efforts have focused on head and neck CTA in patients with emergent concerns for non-focal neurological complaints, but have failed to demonstrate whether increases in utilization have resulted in better care. The objective of this study was to examine trends in head and neck CTA ordering and determine whether a correlation exists between imaging utilization and positivity rates. This is a single-center retrospective observational study at a quaternary referral center. This study includes patients presenting with headache and/or dizziness to the emergency department between January 2017 and December 2021. Patients who received a head and neck CTA were compared to those who did not. The main outcomes included annual head and neck CTA utilization and positivity rates, defined as the percent of scans with attributable acute pathologies. Among 24,892 emergency department visits, 2264 (9.1%) underwent head and neck CTA imaging. The percentage of patients who received a scan over the study period increased from 7.89% (422/5351) in 2017 to 13.24% (662/5001) in 2021, representing a 67.4% increase from baseline (OR, 1.14; 95% CI 1.11-1.18; P < .001). The positivity rate, or the percentage of scans ordered that revealed attributable acute pathology, dropped from 16.8% (71/422) in 2017 to 10.4% (69/662) in 2021 (OR, 0.86; 95% CI 0.79-0.94; P = .001), a 38% reduction in positive examinations. Throughout the study period, there was a 67.4% increase in head and neck CTA ordering with a concomitant 38.1% decrease in positivity rate.

Fusion of electronic health records and radiographic images for a multimodal deep learning prediction model of atypical femur fractures

Atypical femur fractures (AFF) represent a very rare type of fracture that can be difficult to discriminate radiologically from normal femur fractures (NFF). AFFs are associated with drugs that are administered to prevent osteoporosis-related fragility fractures, which are highly prevalent in the elderly population. Given that these fractures are rare and the radiologic changes are subtle currently only 7% of AFFs are correctly identified, which hinders adequate treatment for most patients with AFF. Deep learning models could be trained to classify automatically a fracture as AFF or NFF, thereby assisting radiologists in detecting these rare fractures. Historically, for this classification task, only imaging data have been used, using convolutional neural networks (CNN) or vision transformers applied to radiographs. However, to mimic situations in which all available data are used to arrive at a diagnosis, we adopted an approach of deep learning that is based on the integration of image data and tabular data (from electronic health records) for 159 patients with AFF and 914 patients with NFF. We hypothesized that the combinatorial data, compiled from all the radiology departments of 72 hospitals in Sweden and the Swedish National Patient Register, would improve classification accuracy, as compared to using only one modality. At the patient level, the area under the ROC curve (AUC) increased from 0.966 to 0.987 when using the integrated set of imaging data and seven pre-selected variables, as compared to only using imaging data. More importantly, the sensitivity increased from 0.796 to 0.903. We found a greater impact of data fusion when only a randomly selected subset of available images was used to make the image and tabular data more balanced for each patient. The AUC then increased from 0.949 to 0.984, and the sensitivity increased from 0.727 to 0.849. These AUC improvements are not large, mainly because of the already excellent performance of the CNN (AUC of 0.966) when only images are used. However, the improvement is clinically highly relevant considering the importance of accuracy in medical diagnostics. We expect an even greater effect when imaging data from a clinical workflow, comprising a more diverse set of diagnostic images, are used.

Artificial intelligence and pelvic fracture diagnosis on X-rays: a preliminary study on performance, workflow integration and radiologists' feedback assessment in a spoke emergency hospital

Purpose

The aim of our study is to evaluate artificial intelligence (AI) support in pelvic fracture diagnosis on X-rays, focusing on performance, workflow integration and radiologists' feedback in a spoke emergency hospital.

Materials and methods

Between August and November 2021, a total of 235 sites of fracture or suspected fracture were evaluated and enrolled in the prospective study. Radiologist's specificity, sensibility accuracy, positive and negative predictive values were compared to AI. Cohen's kappa was used to calculate the agreement between AI and radiologist. We also reviewed the AI workflow integration process, focusing on potential issues and assessed radiologists' opinion on AI via a survey.

Results

The radiologist performance in accuracy, sensitivity and specificity was better than AI but McNemar test demonstrated no statistically significant difference between AI and radiologist's performance (p = 0.32). Calculated Cohen's K of 0.64.

Conclusion

Contrary to expectations, our preliminary results did not prove a real improvement of patient outcome nor in reporting time but demonstrated AI high NPV (94,62%) and non-inferiority to radiologist performance. Moreover, the commercially available AI algorithm used in our study automatically learn from data and so we expect a progressive performance improvement. AI could be considered as a promising tool to rule-out fractures (especially when used as a "second reader") and to prioritize positive cases, especially in increasing workload scenarios (ED, nightshifts) but further research is needed to evaluate the real impact on the clinical practice.

Influence of residency discipline and seniority on traumatic musculoskeletal radiographs interpretation accuracy: a multicenter study

OBJECTIVES

Imaging studies are a significant and integral part of the initial assessment of patients admitted to the emergency department. Developing imaging diagnostic abilities early in residency is of paramount importance. The purpose of this study was to evaluate and compare diagnosis accuracy of common musculoskeletal X-rays (XR) between residency disciplines and seniority.

METHODS

A multicenter study which evaluated orthopedic surgery, emergency medicine (EM), and radiology residents, through a test set of common MSK XR. Residents were classified as "beginner" or "advanced" according to postgraduate year per residency. Residents were asked to answer whether the radiograph shows normal or pathological findings (success rate) and what is the diagnosis ("diagnosis accuracy"). Residents' answers were analyzed and assessed compared to experts' consensus.

RESULTS

A total of 100 residents (62% beginners) participated in this study. Fifty-four were orthopedic surgeons, 29 were EM residents and 17 were radiologists. The entire cohort's overall success rate was 88.5%. The overall mean success rates for orthopedic, EM, and radiology residents were 93.2%, 82.8%, and 83.3%, respectively, and were significantly different (p < 0.0001). Orthopedic residents had significantly higher diagnostic accuracy rates compared with both radiology and EM residents (p < 0.001). Advanced orthopedic and EM residents demonstrated higher diagnostic accuracy rates compared to beginner residents (p = 0.001 and p = 0.03, respectively).

CONCLUSION

Orthopedic residents presented higher diagnosis accuracy of MSK imaging compared to EM and radiology residents. Seniority had a positive effect on diagnosis accuracy. The development of an educational program on MSK XR is necessary to enhance the competency of physicians in their daily practice.

Artificial intelligence versus radiologist in the accuracy of fracture detection based on computed tomography images: a multi-dimensional, multi-region analysis

Background

Extremities fractures are a leading cause of death and disability, especially in the elderly. Avulsion fracture are also the most commonly missed diagnosis, and delayed diagnosis leads to higher litigation rates. Therefore, this study evaluates the diagnostic efficiency of the artificial intelligence (AI) model before and after optimization based on computed tomography (CT) images and then compares it with that of radiologists, especially for avulsion fractures.

Methods

The digital X-ray photography [digital radiography (DR)] and CT images of adult limb trauma in our hospital from 2017 to 2020 were retrospectively collected, with or without 1 or more fractures of the shoulder, elbow, wrist, hand, hip, knee, ankle, and foot. Labeling of the fracture referred to the visualization of the fracture on the corresponding CT images. After training the pre-optimized AI model, the diagnostic performance of the pre-optimized AI, optimized AI model, and the initial radiological reports were evaluated. For the lesion level, the detection rate of avulsion and non-avulsion fractures was analyzed, whereas for the case level, the accuracy, sensitivity, and specificity were compared among them.

Results

The total datasets (1,035 cases) were divided into a training set (n=675), a validation set (n=169), and a test set (n=191) in a balanced joint distribution. At the lesion level, the detection rates of avulsion fracture (57.89% vs. 35.09%, P=0.004) and non-avulsion fracture (85.64% vs. 71.29%, P<0.001) by the optimized AI were significantly higher than that by pre-optimized AI. The average precision (AP) of the optimized AI model for all lesions was higher than that of pre-optimized AI model (0.582 vs. 0.425). The detection rate of avulsion fracture by the optimized AI model was significantly higher than that by radiologists (57.89% vs. 29.82%, P=0.002). For the non-avulsion fracture, there was no significant difference of detection rate between the optimized AI model and radiologists (P=0.853). At the case level, the accuracy (86.40% vs. 71.93%, P<0.001) and sensitivity (87.29% vs. 73.48%, P<0.001) of the optimized AI were significantly higher than those of the pre-optimized AI model. There was no statistical difference in accuracy, sensitivity, and specificity between the optimized AI model and the radiologists (P>0.05).

Conclusions

The optimized AI model improves the diagnostic efficacy in detecting extremity fractures on radiographs, and the optimized AI model is significantly better than radiologists in detecting avulsion fractures, which may be helpful in the clinical practice of orthopedic emergency.

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

Objectives

To explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.

Methods

A total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.

Results

The deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.

Conclusion

This deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency.

Bone fractures difficult to recognize in emergency: May be cone beam computed tomography (CBCT) the solution?

CBCT is an imaging tool represented by an X-ray computed tomography delivering a conic-shape X-rays source. This system produces volumetric data during a single rotation of both X-ray beam and detector around the stationary patient. CBCT is able to produce three-dimensional images as for MDCT, however, accounting some advantages over it: lower radiation dose, faster volumetric dataset acquisition, higher spatial resolution and bone contrast. For these reasons, CBCT has recently been described and adopted for extremities imaging in orthopedics. Misinterpretation of fractures may determine a delayed treatment and poor outcome for patients treated in the ED. CBCT, by easily identifying occult cortical fractures and using a lower dose of radiation, is proposed as an alternative or supplement to direct radiograms, optimizing the cost-effectiveness ratio and limiting the number of unnecessary immobilizations. The first experiences document excellent performance of CBCT in emergency radiology departments, especially thanks to transverse imaging in trauma of the extremities.

Reducing diagnostic errors in the emergency department at the time of patient treatment

OBJECTIVE

The purpose of the present study was to compare and combine the radiographic interpretation accuracy of emergency clinicians and radiographers in clinical practice.

METHODS

A total of 838 radiographic examinations were included for analysis from 1 August to 24 August 2020. The range of examinations reviewed included the appendicular and axial skeleton, chest and abdomen. Both paediatric and adult examinations were reviewed. The emergency clinician's and radiographer's interpretations for each examination were compared to the radiologist's report. This allowed mean sensitivity, specificity and diagnostic accuracy to be calculated.

RESULTS

The radiographer's interpretation demonstrated a mean sensitivity, specificity and accuracy of 80%, 98% and 92%, respectively. The emergency clinician's interpretation demonstrated a mean sensitivity, specificity and accuracy of 82%, 95% and 89%, respectively. When the radiographer's and emergency clinician's interpretations were combined, it yielded a mean sensitivity, specificity and accuracy of 90%, 93% and 92%, respectively.

CONCLUSIONS

This is the first study to directly compare and combine the accuracy of an emergency clinician's radiographic interpretation with a radiographer's interpretation within clinical practice. The present study demonstrated that with the addition of a radiographer's interpretation, an emergency clinician's interpretation can be more accurate than the emergency clinician's interpretation in isolation. This highlights the value of a radiographer's interpretation that can complement an emergency clinician's interpretation when a radiologist's report is unavailable.

Automated fracture screening using an object detection algorithm on whole-body trauma computed tomography

The emergency department is an environment with a potential risk for diagnostic errors during trauma care, particularly for fractures. Convolutional neural network (CNN) deep learning methods are now widely used in medicine because they improve diagnostic accuracy, decrease misinterpretation, and improve efficiency. In this study, we investigated whether automatic localization and classification using CNN could be applied to pelvic, rib, and spine fractures. We also examined whether this fracture detection algorithm could help physicians in fracture diagnosis. A total of 7664 whole-body CT axial slices (chest, abdomen, pelvis) from 200 patients were used. Sensitivity, precision, and F1-score were calculated to evaluate the performance of the CNN model. For the grouped mean values for pelvic, spine, or rib fractures, the sensitivity was 0.786, precision was 0.648, and F1-score was 0.711. Moreover, with CNN model assistance, surgeons showed improved sensitivity for detecting fractures and the time of reading and interpreting CT scans was reduced, especially for less experienced orthopedic surgeons. Application of the CNN model may lead to reductions in missed fractures from whole-body CT images and to faster workflows and improved patient care through efficient diagnosis in polytrauma patients.

Patient recalls associated with resident-to-attending radiology report discrepancies: predictive factors for risky discrepancies

BACKGROUND

This study aimed to identify predictive factors for risky discrepancies in the emergency department (ED) by analyzing patient recalls associated with resident-to-attending radiology report discrepancies (RRDs).

RESULTS

This retrospective study analyzed 759 RRDs in computed tomography (CT) and magnetic resonance imaging and their outcomes from 2013 to 2021. After excluding 73 patients lost to follow-up, we included 686 records in the final analysis. Risky discrepancies were defined as RRDs resulting in (1) inpatient management (hospitalization) and (2) adverse outcomes (delayed operations, 30-day in-hospital mortality, or intensive care unit admission). Predictors of risky discrepancies were assessed using multivariable logistic regression analysis. The overall RRD rate was 0.4% (759 of 171,419). Of 686 eligible patients, 21.4% (147 of 686) received inpatient management, and 6.0% (41 of 686) experienced adverse outcomes. RRDs with neurological diseases were associated with the highest ED revisit rate (79.4%, 81 of 102) but not with risky RRDs. Predictive factors of inpatient management were critical finding (odds ratio [OR], 5.60; p < 0.001), CT examination (OR, 3.93; p = 0.01), digestive diseases (OR, 2.54; p < 0.001), and late finalized report (OR, 1.65; p = 0.02). Digestive diseases (OR, 6.14; p = 0.006) were identified as the only significant predictor of adverse outcomes.

CONCLUSIONS

Risky RRDs were associated with several factors, including CT examination, digestive diseases, and late finalized reports, as well as critical image findings. This knowledge could aid in determining the priority of discrepancies for the appropriate management of RRDs.

Radiographer-led discharge for emergency care patients, requiring projection radiography of minor musculoskeletal injuries: a scoping review

Background

Pressure on emergency departments (EDs) from increased attendance for minor injuries has been recognised in the United Kingdom. Radiographer-led discharge (RLD) has potential for improving efficiency, through radiographers trained to discharge patients or refer them for treatment at the point of image assessment. This review aims to scope all RLD literature and identify research assessing the merits of RLD and requirements to enable implementation.

Methods

We conducted a scoping review of studies relating to RLD of emergency care patients requiring projection radiography of minor musculoskeletal (MSK) injuries. MEDLINE, Embase and CINAHL, relevant radiography journals and grey literature were searched. Articles were reviewed and the full texts of selected studies were screened against eligibility criteria. The data were extracted, collated and a narrative synthesis completed.

Results

Seven studies with varying study designs were included in the review. The small number of studies was possibly due to a generally low research uptake in radiography. The main outcome for four studies was reduced length of stay in ED, with recall and re-attendance to ED a primary outcome in one study and secondary outcome for two other studies. The potential for increased efficiency in the minor MSK pathway patient pathway and capacity for ED staff was recognised. Radiographers identified a concern regarding the risk of litigation and incentive of increased salary when considering RLD. The studies were broadly radiographer focussed, despite RLD spanning ED and Radiology.

Conclusion

There were a low number of RLD active radiographers, likely to be motivated individuals. However, RLD has potential for generalisability with protocol variations evident, all producing similar positive outcomes. Understanding radiography and ED culture could clarify facilitators for RLD to be utilised more sustainably into the future. Cost effectiveness studies, action research within ED, and cluster randomised controlled trial with process evaluation are needed to fully understand the potential for RLD.

The cost effectiveness of RLD may provide financial support for training radiographers and increasing their salary, with potential future benefit of reduction in workload within ED. RLD implementation would require an inter-professional approach achieved by understanding ED staff and patient perspectives and ensuring these views are central to RLD implementation.

Common and Uncommon Errors in Emergency Ultrasound

Errors in emergency ultrasound (US) have been representing an increasing problem in recent years thanks to several unique features related to both the inherent characteristics of the discipline and to the latest developments, which every medical operator should be aware of. Because of the subjective nature of the interpretation of emergency US findings, it is more prone to errors than other diagnostic imaging modalities. The misinterpretation of US images should therefore be considered as a serious risk in diagnosis. The etiology of error is multi-factorial: it depends on environmental factors, patients and the technical skills of the operator; it is influenced by intrinsic US artifacts, poor clinical correlation, US-setting errors and anatomical variants; and it is conditioned by the lack of a methodologically correct clinical approach and excessive diagnostic confidence too. In this review, we evaluate the common and uncommon sources of diagnostic errors in emergency US during clinical practice, showing how to recognize and avoid them.

AI musculoskeletal clinical applications: how can AI increase my day-to-day efficiency?

Artificial intelligence (AI) is expected to bring greater efficiency in radiology by performing tasks that would otherwise require human intelligence, also at a much faster rate than human performance. In recent years, milestone deep learning models with unprecedented low error rates and high computational efficiency have shown remarkable performance for lesion detection, classification, and segmentation tasks. However, the growing field of AI has significant implications for radiology that are not limited to visual tasks. These are essential applications for optimizing imaging workflows and improving noninterpretive tasks. This article offers an overview of the recent literature on AI, focusing on the musculoskeletal imaging chain, including initial patient scheduling, optimized protocoling, magnetic resonance imaging reconstruction, image enhancement, medical image-to-image translation, and AI-aided image interpretation. The substantial developments of advanced algorithms, the emergence of massive quantities of medical data, and the interest of researchers and clinicians reveal the potential for the growing applications of AI to augment the day-to-day efficiency of musculoskeletal radiologists.

Comparison of detection of trauma-related injuries using combined "all-in-one" fused images and conventionally reconstructed images in acute trauma CT

OBJECTIVES

To compare the accuracy of lesion detection of trauma-related injuries using combined "all-in-one" fused (AIO) and conventionally reconstructed images (CR) in acute trauma CT.

METHODS

In this retrospective study, trauma CT of 66 patients (median age 47 years, range 18-96 years; 20 female (30.3%)) were read using AIO and CR. Images were independently reviewed by 4 blinded radiologists (two residents and two consultants) for trauma-related injuries in 22 regions. Sub-analyses were performed to analyze the influence of experience (residents vs. consultants) and body region (chest, abdomen, skeletal structures) on lesion detection. Paired t-test was used to compare the accuracy of lesion detection. The effect size was calculated (Cohen's d). Linear mixed-effects model with patients as the fixed effect and random forest models were used to investigate the effect of experience, reconstruction/image processing, and body region on lesion detection.

RESULTS

Reading time of residents was significantly faster using AIO (AIO: 266 ± 72 s, CR: 318 ± 113 s; p < 0.001; d = 0.46) while no significant difference was observed in the accuracy of lesion detection (AIO: 93.5 ± 6.0%, CR: 94.6 ± 6.0% p = 0.092; d =  - 0.21). Reading time of consultants showed no significant difference (AIO: 283 ± 82 s, CR: 274 ± 95 s; p = 0.067; d = 0.16). Accuracy was significantly higher using CR; however, the difference and effect size were very small (AIO 95.1 ± 4.9%, CR: 97.3 ± 3.7%, p = 0.002; d =  - 0.39). The linear mixed-effects model showed only minor effect of image processing/reconstruction for lesion detection.

CONCLUSIONS

Residents at the emergency department might benefit from faster reading time without sacrificing lesion detection rate using AIO for trauma CT.

KEY POINTS

• Image fusion techniques decrease the reading time of acute trauma CT without sacrificing diagnostic accuracy.

Artificial Intelligence to Diagnose Tibial Plateau Fractures: An Intelligent Assistant for Orthopedic Physicians

Objective

To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of tibial plateau fractures (TPFs) and further measure its validity and feasibility.

Methods

A total of 542 X-rays of TPFs were collected as a reference database. An AI algorithm (RetinaNet) was trained to analyze and detect TPF on the X-rays. The ability of the AI algorithm was determined by indexes such as detection accuracy and time taken for analysis. The algorithm performance was also compared with orthopedic physicians.

Results

The AI algorithm showed a detection accuracy of 0.91 for the identification of TPF, which was similar to the performance of orthopedic physicians (0.92±0.03). The average time spent for analysis of the AI was 0.56 s, which was 16 times faster than human performance (8.44±3.26 s).

Conclusion

The AI algorithm is a valid and efficient method for the clinical diagnosis of TPF. It can be a useful assistant for orthopedic physicians, which largely promotes clinical workflow and further guarantees the health and security of patients.

Rib fracture detection system based on deep learning

Rib fracture detection is time-consuming and demanding work for radiologists. This study aimed to introduce a novel rib fracture detection system based on deep learning which can help radiologists to diagnose rib fractures in chest computer tomography (CT) images conveniently and accurately. A total of 1707 patients were included in this study from a single center. We developed a novel rib fracture detection system on chest CT using a three-step algorithm. According to the examination time, 1507, 100 and 100 patients were allocated to the training set, the validation set and the testing set, respectively. Free Response ROC analysis was performed to evaluate the sensitivity and false positivity of the deep learning algorithm. Precision, recall, F1-score, negative predictive value (NPV) and detection and diagnosis were selected as evaluation metrics to compare the diagnostic efficiency of this system with radiologists. The radiologist-only study was used as a benchmark and the radiologist-model collaboration study was evaluated to assess the model's clinical applicability. A total of 50,170,399 blocks (fracture blocks, 91,574; normal blocks, 50,078,825) were labelled for training. The F1-score of the Rib Fracture Detection System was 0.890 and the precision, recall and NPV values were 0.869, 0.913 and 0.969, respectively. By interacting with this detection system, the F1-score of the junior and the experienced radiologists had improved from 0.796 to 0.925 and 0.889 to 0.970, respectively; the recall scores had increased from 0.693 to 0.920 and 0.853 to 0.972, respectively. On average, the diagnosis time of radiologist assisted with this detection system was reduced by 65.3 s. The constructed Rib Fracture Detection System has a comparable performance with the experienced radiologist and is readily available to automatically detect rib fracture in the clinical setting with high efficacy, which could reduce diagnosis time and radiologists' workload in the clinical practice.

Structured Reporting of Whole-Body Trauma CT Scans Using Checklists: Diagnostic Accuracy of Reporting Radiologists Depending on Their Level of Experience

PURPOSE

 Evaluation of the diagnostic accuracy of a checklist-style structured reporting template in the setting of whole-body multislice computed tomography in major trauma patients depending on the level of experience of the reporting radiologist.

MATERIALS AND METHODS

 A total of 140 major trauma scans with the same protocol were included in this retrospective study. In a purely trial-intended reading, the trauma scans were analyzed by three radiologists with different levels of experience (resident, radiologist with 3 years of experience after board certification, and radiologist with 7 years of experience after board certification). The aim was to fill in the checklist 1 template within one minute to immediately diagnose management-altering findings. Checklist 2 was intended for the analysis of important trauma-related findings within 10 minutes. Reading times were documented. The final radiology report and the documented injuries in the patient's medical record were used as gold standard.

RESULTS

 The evaluation of checklist 1 showed a range of false-negative reports between 5.0 % and 11.4 % with the resident showing the highest accuracy. Checklist 2 showed overall high diagnostic inaccuracy (19.3-35.0 %). The resident's diagnostic accuracy was statistically significantly higher compared to the radiologist with 3 years of experience after board certification (p = 0.0197) and with 7 years of experience after board certification (p = 0.0046). Shorter average reporting time resulted in higher diagnostic inaccuracy. Most of the missed diagnoses were fractures of the spine and ribs.

CONCLUSION

 By using a structured reporting template in the setting of major trauma computed tomography, less experienced radiologists reach a higher diagnostic accuracy compared to experienced readers.

KEY POINTS

  · In the setting of a pure trial reading, the diagnostic inaccuracy of template-based reporting of major trauma CT examinations is high.. · Fractures in general and especially of the vertebral bodies and ribs were the most commonly missed diagnoses.. · In a study setting, less experienced radiologists seem to reach a higher diagnostic accuracy when using a structured reporting approach..

CITATION FORMAT

· Dendl LM, Pausch AM, Hoffstetter P et al. Structured Reporting of Whole-Body Trauma CT Scans Using Checklists: Diagnostic Accuracy of Reporting Radiologists Depending on Their Level of Experience. Fortschr Röntgenstr 2021; DOI: 10.1055/a-1541-8265.

Evaluation of Sanders Type 2 Joint Depression Calcaneal Fractures in 197 Patients from a Single Center Using Three-Dimensional Mapping

BACKGROUND This study aimed to evaluate Sanders type 2 calcaneal fractures in 197 patients from a single center using the 3D (three-dimensional) CT (computed tomography) mapping method. MATERIAL AND METHODS A consecutive series of 197 Sanders type 2 joint depression calcaneal fractures was used. The segment and split functions were used to create each calcaneal fragment using Mimics Research 20.0 software. The fracture fragments were reduced in 3-matic Research 12.0 software. In the E-3D Medical 18.01 software, after superimposing the fractured calcaneus entity with the calcaneus template, we drew the fracture line on the template. Finally, the heatmap was obtained by fracture statistical analysis function. Simultaneously, the distribution of the fracture lines in the anterior part of the calcaneus (APC) and middle talar joint was recorded. RESULTS There were 109 cases of Sanders type 2A, 46 cases of Sanders type 2B, and 42 cases of Sanders type 2C. Based on the data, we drew the characteristic fracture map of type 2A 2B and 2C. This study found that the most common types of Sanders type 2A in APC and middle talar articular surface are type AC and type AD. In Sanders type 2B, the most common type is type AC, and in Sanders type 2C it is type ACD. CONCLUSIONS The findings from this study showed that 3D CT imaging and reconstruction of the calcaneus was a useful diagnostic method to evaluate and classify joint depression calcaneal fractures. The calcaneal fracture map can be used to guide surgical planning and optimize the design of internal fixation.

Traumatic cervical spine fracture patterns on CT: a retrospective analysis at a level 1 trauma center

PURPOSE

The purpose of our study was to determine common acute traumatic cervical spine fracture patterns on CT cervical spine (CTCS).

METHODS

We retrospectively reviewed 1091 CTCS positive for traumatic fractures performed over a 10-year period at a level 1 trauma center. Fractures were classified by vertebral level, laterality, and anatomic location (anterior/posterior arch, body, odontoid, pedicle, facet, lateral mass, lamina, spinous process, transverse foramina, and transverse processes).

RESULTS

C2 was the most commonly fractured vertebra (38% of all studies), followed by C7 (32.4%). 48.7% of studies had upper cervical spine (C1 and/or C2) fractures. 39.7% of positive studies involved > 1 vertebral level. Conditioned on fractures at one cervical level, the probability of fracture was greatest at adjacent levels with a 50% chance of sustaining a C7 fracture with C6 fracture. However, 31.3% (136) of studies with multi-level fractures had non-contiguous fractures. The most common isolated vertebral process fracture was of the transverse process, seen in 89 (8.2%) studies at a single level, 27 (2.5%) studies at multiple levels. Subaxial spine vertebral process fractures outnumbered body fractures with progressive dominance of vertebral process fracture down the spine.

CONCLUSION

C2 was the most commonly fractured vertebral level. Multi-level traumatic cervical spine fractures constituted 40% of our cohort, most commonly at C6/C7 and C1/C2. Although the conditional probability of concurrent fracture in studies with multi-level fractures was greatest in contiguous levels, nearly one-third of multi-level fractures involved non-contiguous fractures.

Verbal Notification of Radiology Results: Are radiologists meeting expectations?

BACKGROUND

Delayed communication of radiographic findings is associated with poor patient outcomes and significant medicolegal risk. Radiologists verbally contact referring practitioners with urgent findings, though practitioner's expectations regarding notification has rarely been examined.

AIM

To assess differences in preferred practice between radiologists and referring practitioners in the verbal communication of urgent radiology findings.

METHODS

For 33 clinical stems, respondents were asked if they would issue (radiologists) or expect to receive (referring practitioners) verbal notification of results or routine written communication only. Surveys were emailed to radiologists and referring practitioners of varying experience at a tertiary referral hospital in Melbourne, Victoria.

RESULTS

A total of 97 survey responses were received. 80 responses were from referring practitioners and 17 from radiologists. Referring practitioners were seen to slightly prefer verbal notification more often than issued by radiologists overall (61%; 95CI 57-66% verbal notification expected versus 58%; 95CI 52-64% issued). More senior referring practitioners with greater than 10 years' experience expected verbal notification more often (67%; 95CI 59-75%), and more senior radiologists issued verbal reports less often (54%; 95CI 39-69%). More junior referring practitioners, for example registrars or fellows, expected notification less often overall (59%; 95CI 43-76%). Subgroup analysis demonstrated statistically significant differences in notification preferences for certain clinical scenarios.

CONCLUSIONS

Overall results show fair correlation between referrer's expectations of verbal notification and the provision of verbal notification by radiologists. However, there were discrepancies in the practice and preferences of more junior and senior practitioners, in addition to in certain clinical scenarios. This article is protected by copyright. All rights reserved.

Radiology and A Radiologist: A Keystone in the Turmoil of Trauma Setting

Traumatic injuries are one of the leading causes of morbidity and mortality. Precise diagnosis and management in the golden hour are key to decrease morbidity and mortality. History and physical examination alone are insufficient to avoid misdiagnosis. In this article, we tried to determine the role of a radiologist and an appropriate imaging modality in a trauma setting. We conducted a literature review of published research articles. We used the keywords imaging, trauma, imaging and trauma, and trauma imaging essentials were used on PubMed and Google Scholar. The articles published in the English language from 2015 to 2020 with full free text available were included. Using the medical subject heading (MeSH) strategy, "diagnostic imaging" (Major {Majr}) and "multiple trauma/diagnostic imaging" (Mesh) on PubMed, we identified 34 papers after applying the inclusion and exclusion criteria. Twenty articles were finally selected which included studies from 2015 to 2020 with articles focusing on the adult population and acute cases. A radiologist and imaging modalities are the essential parts of a trauma setting to lower morbidity and mortality. X-rays and Extended Focussed Assessment with Sonography for Trauma (eFAST) are the first-line imaging modality in the acute trauma setting. However, the CT scan is the most sensitive modality that should be done to avoid misdiagnosis depending upon the patient's history and physical examination.

Pitfalls and differential diagnosis on adrenal lesions: current concepts in CT/MR imaging: a narrative review

The purpose of this pictorial essay is to review the imaging findings of adrenal lesions. Adrenal lesions could be divided into functioning or non-functioning masses, primary or metastatic, and benign or malignant. Imaging techniques have undergone significant advances in recent years. The most significant objective of adrenal imaging is represented by the detection and, when possible, characterization of adrenal lesions in order to direct patient management correctly. The detection and management of adrenal lesions is based on cross-sectional imaging obtained with non-contrast CT (tumour density), contrast-enhanced CT including delayed washout (either absolute percentage washout or relative percentage one) and finally with MR chemical shift analysis (loss of signal intensity between in-phase and out-of-phase images including both qualitative and quantitative estimates of signal loss). The small incidental adrenal nodules are benign, in most of cases; some tumors such as lipid-rich adenoma and myelolipoma have characteristic features that can be diagnosed accurately in CT. On contrary, if the presenting contrast-enhanced CT shows an adrenal mass with uncertain or malignant morphologic features, particularly in patients with a known history of malignancy, further evaluations should be considered. The most significative implications for radiologists are represented by how to assess risk of malignancy on imaging and what follow-up to indicate if an adrenal incidentaloma is not surgically removed.

Deployment of artificial intelligence for radiographic diagnosis of COVID-19 pneumonia in the emergency department

OBJECTIVE

The coronavirus disease 2019 pandemic has inspired new innovations in diagnosing, treating, and dispositioning patients during high census conditions with constrained resources. Our objective is to describe first experiences of physician interaction with a novel artificial intelligence (AI) algorithm designed to enhance physician abilities to identify ground-glass opacities and consolidation on chest radiographs.

METHODS

During the first wave of the pandemic, we deployed a previously developed and validated deep-learning AI algorithm for assisted interpretation of chest radiographs for use by physicians at an academic health system in Southern California. The algorithm overlays radiographs with "heat" maps that indicate pneumonia probability alongside standard chest radiographs at the point of care. Physicians were surveyed in real time regarding ease of use and impact on clinical decisionmaking.

RESULTS

Of the 5125 total visits and 1960 chest radiographs obtained in the emergency department (ED) during the study period, 1855 were analyzed by the algorithm. Among these, emergency physicians were surveyed for their experiences on 202 radiographs. Overall, 86% either strongly agreed or somewhat agreed that the intervention was easy to use in their workflow. Of the respondents, 20% reported that the algorithm impacted clinical decisionmaking.

CONCLUSIONS

To our knowledge, this is the first published literature evaluating the impact of medical imaging AI on clinical decisionmaking in the emergency department setting. Urgent deployment of a previously validated AI algorithm clinically was easy to use and was found to have an impact on clinical decision making during the predicted surge period of a global pandemic.

Workload for radiologists during on-call hours: dramatic increase in the past 15 years

Background

The objective of this study is to investigate the workload for radiologists during on-call hours and to quantify the 15-year trend in a large general hospital in Western Europe.

Methods

Data regarding the number of X-ray, ultrasound and computed tomography (CT) studies during on-call hours (weekdays between 6.00 p.m. and 7.00 a.m., weekends, and national holidays) between 2006 and 2020 were extracted from the picture archiving and communication system. All studies were converted into relative value units (RVUs) to estimate the on-call workload. The Mann–Kendall test was performed to assess the temporal trend.

Results

The total RVUs during on-call hours showed a significant increase between 2006 and 2020 (Kendall's tau-b = 0.657, p = 0.001). The overall workload in terms of RVUs during on-call hours has quadrupled. The number of X-ray studies significantly decreased (Kendall's tau-b = − 0.433, p = 0.026), whereas the number of CT studies significantly increased (Kendall's tau-b = 0.875, p < 0.001) between 2006 and 2020. CT studies which increased by more than 500% between 2006 and 2020 are CT for head trauma, brain CTA, brain CTV, chest CT (for suspected pulmonary embolism), spinal CT, neck CT, pelvic CT, and CT for suspected aortic dissection. The number of ultrasound studies did not change significantly (Kendall's tau-b = 0.202, p = 0.298).

Conclusions

The workload for radiologists during on-call hours increased dramatically in the past 15 years. The growing amount of CT studies is responsible for this increase. Radiologist and technician workforce should be matched to this ongoing increasing trend to avoid potential burn-out and to maintain quality and safety of radiological care.

Identifying trigger concepts to screen emergency department visits for diagnostic errors

OBJECTIVES

The diagnostic process is a vital component of safe and effective emergency department (ED) care. There are no standardized methods for identifying or reliably monitoring diagnostic errors in the ED, impeding efforts to enhance diagnostic safety. We sought to identify trigger concepts to screen ED records for diagnostic errors and describe how they can be used as a measurement strategy to identify and reduce preventable diagnostic harm.

METHODS

We conducted a literature review and surveyed ED directors to compile a list of potential electronic health record (EHR) trigger (e-triggers) and non-EHR based concepts. We convened a multidisciplinary expert panel to build consensus on trigger concepts to identify and reduce preventable diagnostic harm in the ED.

RESULTS

Six e-trigger and five non-EHR based concepts were selected by the expert panel. E-trigger concepts included: unscheduled ED return to ED resulting in hospital admission, death following ED visit, care escalation, high-risk conditions based on symptom-disease dyads, return visits with new diagnostic/therapeutic interventions, and change of treating service after admission. Non-EHR based signals included: cases from mortality/morbidity conferences, risk management/safety office referrals, ED medical director case referrals, patient complaints, and radiology/laboratory misreads and callbacks. The panel suggested further refinements to aid future research in defining diagnostic error epidemiology in ED settings.

CONCLUSIONS

We identified a set of e-trigger concepts and non-EHR based signals that could be developed further to screen ED visits for diagnostic safety events. With additional evaluation, trigger-based methods can be used as tools to monitor and improve ED diagnostic performance.

Radiograph Interpretation Discrepancies in a Community Hospital Emergency Department

Introduction

In many hospitals, off-hours emergency department (ED) radiographs are not read by a radiologist until the following morning and are instead interpreted by the emergency physician (EP) at the time of service. Studies have found conflicting results regarding the radiographic interpretation discrepancies between EPs and trained radiologists. The aim of this study was to identify the number of radiologic interpretation discrepancies between EPs and radiologists in a community ED setting.

Methods

Using a pre-existing logbook of radiologic discrepancies as well as our institution’s picture archiving and communication system, all off-hours interpretation discrepancies between January 2012 and January 2015 were reviewed and recorded in a de-identified fashion. We recorded the type of radiograph obtained for each patient. Discrepancy grades were recorded based on a pre-existing 1–4 scale defined in the institution’s protocol logbook as Grade 1 (no further action needed); Grade 2 (call to the patient or pharmacy); Grade 3 (return to ED for further treatment, e.g., fracture not splinted); Grade 4 (return to ED for serious risk, e.g., pneumothorax, bowel obstruction). We also recorded the total number of radiographs formally interpreted by EPs during the prescribed time-frame to determine overall agreement between EPs and radiologists.

Results

There were 1044 discrepancies out of 16,111 EP reads, indicating 93.5% agreement. Patients averaged 48.4 ± 25.0 years of age and 53.3% were female; 25.1% were over-calls by EPs. The majority of discrepancies were minor with 75.8% Grade 1 and 22.3% Grade 2. Only 1.7% were Grade 3, which required return to the ED for further treatment. A small number of discrepancies, 0.2%, were Grade 4. Grade 4 discrepancies accounted for two of the 16,111 total reads, equivalent to 0.01%. A slight disagreement in finding between EP and radiologist accounted for 8.3% of discrepancies.

Conclusion

Results suggest that plain radiographic studies can be interpreted by EPs with a very low incidence of clinically significant discrepancies when compared to the radiologist interpretation. Due to rare though significant discrepancies, radiologist interpretation should be performed when available. Further studies are needed to determine the generalizability of this study to EDs with differing volume, patient population, acuity, and physician training.

Evaluating the effectiveness of intensive versus non-intensive image interpretation education for radiographers: a randomised controlled trial

INTRODUCTION

The purpose of this randomised controlled trial was to compare the effectiveness of intensive and non-intensive formats of delivery of image interpretation education for radiographers.

METHODS

A multi-centre, stratified (by years of experience) two group parallel arm, single blind, randomised controlled trial was conducted. Participants (n = 48) were allocated to one of two groups to receive image interpretation education: (1) intensive format (13.5 h over two consecutive days) (2) non-intensive (sequential 90-min tutorials delivered 1 week apart). Participants undertook x-ray interpretation tests before education, at 1-week post-education completion and at 12-week post-education completion.

RESULTS

Image interpretation performance was not significantly different between groups at baseline. A generalised linear model indicated that participants who received intensive education format improved image interpretation performance by a greater margin than the group that received non-intensive education at 1-week (P = 0.002) and 12-week (P < 0.001) follow-up assessments.

CONCLUSIONS

Although both formats of education delivery may be beneficial, the findings of this study have indicated that the intensive format of delivery was more effective at improving radiographers' ability to interpret trauma radiographs in the weeks after completion of the image interpretation program.

Role of dual energy CT to improve diagnosis of non-traumatic abdominal vascular emergencies

Computed tomography angiography (CTA) is the modality of choice to evaluate abdominal vascular emergencies (AVE). CTA protocols are often complex and require acquisition of multiple phases to enable a variety of diagnosis such as acute bleeding, pseudoaneurysms, bowel ischemia, and dissection. With single energy CT (SECT), differentiating between calcium, coagulated blood, and contrast agents can be challenging based on their attenuation, especially when in small quantity or present as a mixture. With dual-energy CT (DECT), virtual monoenergetic (VM) and material decomposition (MD) image reconstructions enable more robust tissue characterization, improve contrast-enhancement, and reduce beam hardening artifacts. This article will demonstrate how radiologists can utilize DECT for various clinical scenarios in assessment of non-traumatic AVE.

Convolutional Neural Networks for Automated Fracture Detection and Localization on Wrist Radiographs

PURPOSE

To demonstrate the feasibility and performance of an object detection convolutional neural network (CNN) for fracture detection and localization on wrist radiographs.

MATERIALS AND METHODS

Institutional review board approval was obtained with waiver of consent for this retrospective study. A total of 7356 wrist radiographic studies were extracted from a hospital picture archiving and communication system. Radiologists annotated all radius and ulna fractures with bounding boxes. The dataset was split into training (90%) and validation (10%) sets and used to train fracture localization models for frontal and lateral images. Inception-ResNet Faster R-CNN architecture was implemented as a deep learning model. The models were tested on an unseen test set of 524 consecutive emergency department wrist radiographic studies with two radiologists in consensus as the reference standard. Per-fracture, per-image (ie, per-view), and per-study sensitivity and specificity were determined. Area under the receiver operating characteristic curve (AUC) analysis was performed.

RESULTS

The model detected and correctly localized 310 (91.2%) of 340 and 236 (96.3%) of 245 of all radius and ulna fractures on the frontal and lateral views, respectively. The per-image sensitivity, specificity, and AUC were 95.7% (95% confidence interval [CI]: 92.4%, 97.8%), 82.5% (95% CI: 77.4%, 86.8%), and 0.918 (95% CI: 0.894, 0.941), respectively, for the frontal view and 96.7% (95% CI: 93.6%, 98.6%), 86.4% (95% CI: 81.9%, 90.2%), and 0.933 (95% CI: 0.912, 0.954), respectively, for the lateral view. The per-study sensitivity, specificity, and AUC were 98.1% (95% CI: 95.6%, 99.4%), 72.9% (95% CI: 67.1%, 78.2%), and 0.895 (95% CI: 0.870, 0.920), respectively.

CONCLUSION

The ability of an object detection CNN to detect and localize radius and ulna fractures on wrist radiographs with high sensitivity and specificity was demonstrated.© RSNA, 2019.

Imaging of Violence Against the Elderly and the Women

Emergency department assessment is a critical opportunity to identify elder abuse and violence against women, which represent a growing problem, requiring the attention of health care systems. Elder abuse is most frequently perpetrated by family members because of the higher levels of stress, burnout, and financial problems affecting the caregivers that can even lead to deadly consequences. Intimate partner violence is defined as physical, sexual, or psychological harm caused to another by a current or former partner or spouse, and can range from a single acute hit to chronic battering, varying in frequency and severity. Radiologists have a critical role in detecting those injury findings suggestive of abuse and violence. When appropriate, additional information about the social circumstances in which an injury took place, linked with imaging findings, may also be helpful in diagnosing abuse. The purpose of this article is to highlight the role of diagnostic imaging in the detection of lesions compatible with domestic abuse in elderly patients and women, and to allow the recognition of the alterations most frequently associated with this type of violence.

Whole-Body CT in Patients with Multiple Traumas: Factors Leading to Missed Injury

Purpose To determine radiologic and clinical markers predictive of missed injuries at early whole-body CT image interpretation. Materials and Methods For this retrospective study, 2354 consecutive whole-body CT examinations were performed in patients with multiple traumas from 26 hospitals interpreted at a teleradiology center study during on-call period from February 2011 to September 2016. All whole-body CT images were interpreted by the on-call radiologist and reviewed within 12-48 hours by another radiologist to detect missed injury as the standard of reference. The first and review reports of all examinations were retrospectively reviewed. Univariable and multivariable logistic regression with a stepwise selection method were performed to identify clinical and radiologic predictors of missed injury. Results This study included 639 women (27.1%) and 1715 men (72.8%). The median age of men, women, and the entire population was 34 years (age range, 1-96 years). On a per-scan basis, there were 304 (12.9%) missed injuries and 59 (2.5%) were clinically significant. On a per-injury basis, the missed injury rate was 530 of 5979 (8.8%). More than two injured body parts (odds ratio, 1.4 [95% confidence interval: 1.1, 1.8]; P = .01), patient age older than 30 years (odds ratio, 2.8 [95% confidence interval: 2.1, 3.8]; P < .001), and an initial clinical severity class of 1 (odds ratio, 1.9 [95% confidence interval: 1.3, 2.8]; P < .001) were independent predictive factors of missed injury. Conclusion Multiple traumas with more than two injured body parts, age older than 30 years, or an initial clinical severity class of 1 were associated with missed injury at whole-body CT. © RSNA, 2018 Online supplemental material is available for this article. See also the editorial by Novelline in this issue.

Evaluating the effectiveness of intensive versus non-intensive image interpretation education for radiographers: a randomised control trial study protocol

Radiographer commenting systems have not been successfully implemented in many Australian hospitals, despite evidence of their benefit and adoption elsewhere, such as the United Kingdom. An important contributor to the lack of widespread adoption of radiographer commenting in Australia (and likely elsewhere) is the limited availability of accessible education options for radiographers. The purpose of this randomised controlled trial is to compare the effectiveness of the same image interpretation education program delivered over an intensive 2-day period (intensive format) versus a series of shorter regular workshops (non-intensive format). The study design is a multicentre, stratified (by years of experience) two group parallel-arm single-blind (assessor blinded) randomised controlled trial. Participants will be allocated to one of the two groups: (1) intensive format of education or (2) non-intensive format of education in a 1:1 ratio. Participants will undergo assessments before education, at 1 week post-intervention completion and at 12 weeks post-intervention completion. Findings from this trial will be of relevance to radiographers seeking image interpretation training as well as organisations providing image interpretation education to prepare clinical staff for participation in a radiographer commenting system. A limitation of the trial is that the sample will be inclusive of radiographers, and findings may not be able to be directly extrapolated to other clinical disciplines (e.g. junior doctors, physiotherapists or nurse practitioners).

Calcaneal fractures: radiological and CT evaluation and classification systems

Background and aim of the work: The calcaneus, the more lower bone of the body, has the task of supporting the axial load from the weight of the body. Calcaneal fractures represent about 1-2% of all fractures and 60% of the tarsal bones fractures. The articular involvement has been associated with a poor functional outcome. The aim of this work is to describe the radiologic evaluation, the classification systems, the morphological preoperative diagnostic imaging features of calcaneal fractures, highlighting the correlation with the choice of treatment and predictive capacity for the fracture surgical outcome. Methods: A PubMed search was performed for the terms Imaging calcaneus fracture, selecting articles in English language, published in the last two years, where preoperatively diagnostic imaging of fractures of the calcaneus are described. Case reports have not been included. Results: We have collected a number of data that provide important help in preoperative evaluation of calcaneal fractures, such as the new classification system created by Harnroongroj et al, the association of calcaneal fractures with fractures of other bone structures or soft tissue impairment, the use of calcaneotalar ratio in assessing the length of heel. Conclusions: These data suggest an approach geared to the specific choice of treatment and to improving patient outcomes. (www.actabiomedica.it)

Imaging of metabolic bone disease

Osteoporosis is the most important metabolic bone disease, with a wide distribution among the elderly. It is characterized by low bone mass and micro architectural deterioration of bone tissue, leading to enhanced bone fragility and a consequent increase in fracture risk. Identify bone weakening with an appropriate and accurate use of diagnostic imaging is of critical importance in the diagnosis and follow-up of osteoporotic patients. The aim of this review is to evaluate the detection rates of the different imaging modalities in the evaluation of bone strength, in the assessment of fracture risk and in the management of fragility fractures. (www.actabiomedica.it)

Diagnostic imaging and intervention of the musculoskeletal system: state of the art

The study of the musculoskeletal system has always been one of the most important application for diagnostic imaging in radiology (1-4).

Traumatic fractures in adults: missed diagnosis on plain radiographs in the Emergency Department

Radiography remains the imaging standard for fracture detection after trauma. The radiographic diagnosis of most fractures and dislocations poses little difficulty to radiologists: however, occasionally these injuries are quite subtle or even impossible to detect on radiographs. Missed diagnoses of fracture potentially have important consequences for patients, clinicians, and radiologists. Radiologists play a pivot role in the diagnostic assessment of the trauma patients: emergency radiologists who are more practiced at seeking out and discerning traumatic fractures can provide an invaluable service to their clinical colleagues by ensuring that patients do not endure delayed diagnoses. This is a narrative review article aims to highlight the spectrum of fractures in adults potentially missed on plain radiographs, the causes of error in diagnosis of fractures in the emergency setting and the key elements to reduce misdiagnosis of fractures.

Acute and overuse elbow trauma: radio-orthopaedics overview

The correct management of acute, subacute and overuse-related elbow pathologies represents a challenging diagnostic and therapeutic problem. While major trauma frequently requires a rapid surgical intervention, subluxation and minor trauma allow taking more time for diagnostics and planning the correct elective treatment after careful clinical and radiological investigation. In these conditions, communication between orthopaedic surgeon and radiologist allow to create a detailed radiology report, tailored to the patient's and surgeon's needs and optimal to plan proper management. Imaging technique as X-Ray, CT, US, MRI, CTA and MRA all belong to the radiologist's portfolio in elbow diagnostics. Detailed knowledge of elbow pathology and its classification and of the possibilities and limits of each imaging technique is of crucial importance to reach the correct diagnosis efficiently. The aim of this review is to present the most frequent elbow pathologies and suggest a suitable diagnostic approach for each of them.