Emergency Radiology最新文献

{"title":"Deep learning-aided diagnosis of acute abdominal aortic dissection by ultrasound images.","authors":"Zhanye Lin, Jian Zheng, Yaohong Deng, Lingyue Du, Fan Liu, Zhengyi Li","doi":"10.1007/s10140-025-02311-y","DOIUrl":"https://doi.org/10.1007/s10140-025-02311-y","url":null,"abstract":"<p><strong>Purpose: </strong>Acute abdominal aortic dissection (AD) is a serious disease. Early detection based on ultrasound (US) can improve the prognosis of AD, especially in emergency settings. We explored the ability of deep learning (DL) to diagnose abdominal AD in US images, which may help the diagnosis of AD by novice radiologists or non-professionals.</p><p><strong>Methods: </strong>There were 374 US images from patients treated before June 30, 2022. The images were classified as AD-positive and AD-negative images. Among them, 90% of images were used as the training set, and 10% of images were used as the test set. A Densenet-169 model and a VGG-16 model were used in this study and compared with two human readers.</p><p><strong>Results: </strong>DL models demonstrated high sensitivity and AUC for diagnosing abdominal AD in US images, and DL models showed generally better performance than human readers.</p><p><strong>Conclusion: </strong>Our findings demonstrated the efficacy of DL-aided diagnosis of abdominal AD in US images, which can be helpful in emergency settings.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143002265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.","authors":"David Dreizin, Garvit Khatri, Pedro V Staziaki, Karen Buch, Mathias Unberath, Mohammed Mohammed, Aaron Sodickson, Bharti Khurana, Anjali Agrawal, James Stephen Spann, Nicholas Beckmann, Zachary DelProposto, Christina A LeBedis, Melissa Davis, Gabrielle Dickerson, Michael Lev","doi":"10.1007/s10140-025-02312-x","DOIUrl":"https://doi.org/10.1007/s10140-025-02312-x","url":null,"abstract":"","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143002263","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hematuria in the ER patient: optimizing detection of upper tract urothelial cancer - A pictorial essay.","authors":"Mohammad Yasrab, Charles K Crawford, Linda C Chu, Satomi Kawamoto, Elliot K Fishman","doi":"10.1007/s10140-024-02308-z","DOIUrl":"10.1007/s10140-024-02308-z","url":null,"abstract":"<p><p>Upper tract urothelial carcinoma (UTUC) is a rare and challenging subset of the more frequently encountered urothelial carcinomas (UCs), comprising roughly 5-7% of all UCs and less than 10% of all renal tumors. Hematuria is a common presenting symptom in the emergency setting, often prompting imaging to rule out serious etiologies, with UTUC especially posing as a diagnostic challenge. These UTUC lesions of the kidney and ureter are often small, mimicking other pathologies, and are more aggressive than typical UC of the bladder, emphasizing the importance of timely and accurate diagnosis. Multidetector computed tomography urography (CTU) is the standard imaging modality for diagnosis, tumor staging, and surgical planning. Various postprocessing techniques like multiplanar reconstructions, maximal intensity projection (MIP) images, and 3D volumetric rendering technique (VRT) are crucial for accurate detection. In addition, 3D cinematic rendering (CR) is a novel technique that employs advanced illumination models, producing images with realistic shadows and increased surface detail, outperforming traditional VRT. We will review the distinctive imaging features between UTUC and infiltrating mimicking lesions on CTU in patients who presented with hematuria, in conjunction with advanced postprocessing techniques, ultimately improving diagnostic confidence and preoperative planning in the emergency context.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983082","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The incidence of vascular injuries in patients with negative cervical computed tomography (CT) following blunt trauma.","authors":"Assala Aslan, Joseph Eskew, Spencer Zaheri, Ridge Arceneaux, Elizabeth Field, Elise Thibodeaux, Morgan Roque, Luis De Alba, Octavio Arevalo, Hugo Cuellar","doi":"10.1007/s10140-024-02310-5","DOIUrl":"https://doi.org/10.1007/s10140-024-02310-5","url":null,"abstract":"<p><strong>Introduction: </strong>Computed tomography (CT) angiography is commonly utilized to quickly identify vascular injuries caused by blunt cervical trauma. It is often conducted alongside a cervical spine CT, based on established criteria. This study assessed the prevalence of cervical vascular injuries identified via CT angiography (CTA) in patients who had negative findings on cervical CT scans.</p><p><strong>Materials and methods: </strong>A retrospective study was performed on patients who experienced blunt trauma from January 2020 to December 2022 and underwent both cervical CT and CTA. The sample size was determined using the formula: n = (Z^2 * P * (1 - P)) / E^2, assuming a 99% confidence interval, a 2% margin of error, and a proportion of 0.05.</p><p><strong>Results: </strong>A total of 1,165 patients presented with acute blunt trauma to the head and neck during the study period. Out of those, 800 patients (68.7%) had negative cervical CT scans and only 5 patients (0.6%) were found to have vascular injuries on CTA, with an average age of 44.2 years. Regarding the severity of the injuries, three were classified as grade I and two as grade II. On the other hand, of the 365 patients with positive cervical CT, 44 patients (12%) had vascular injury on CTA, including 16 patients (4.5%) with grades III and IV injuries.</p><p><strong>Conclusion: </strong>The findings of this study suggest that CTA in patients with negative cervical CT scans seldom reveals vascular injuries, with no injuries exceeding grade II. This highlights the selective utility of CTA in this patient group.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142977687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Radiology resident competency in orthopedic trauma detection in simulated on-call scenarios.","authors":"John Ramos Rivas, Kevin Pierre, Abheek Raviprasad, Arman Mahmood, Olivia Scheuermann, Bruce Steinberg, Roberta Slater, Christopher Sistrom, Otgonbayar Batmunh, Priya Sharma, Ivan Davis, Anthony Mancuso, Dhanashree Rajderkar","doi":"10.1007/s10140-024-02309-y","DOIUrl":"https://doi.org/10.1007/s10140-024-02309-y","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate radiology residents' ability to accurately identify three specific types of orthopedic trauma using radiographic imaging within a simulated on-call environment.</p><p><strong>Methods: </strong>We utilized the Wisdom in Diagnostic Imaging Emergent/Critical Care Radiology Simulation (WIDI SIM) to assess residents' preparedness for independent radiology call. The simulation included 65 cases, with three focusing on orthopedic trauma: sacral ala, femoral neck, and pediatric tibial/Toddler's fractures. Faculty graded residents' responses using a standardized 10-point rubric and categorized errors as observational (failing to identify key findings) or interpretive (incorrect conclusions despite correct identification of findings).</p><p><strong>Results: </strong>321 residents evaluated sacral ala fracture radiographs and received an average score of 1.29/10, with 8.71 points lost to observational errors. Only 6% produced effective reports (scores ≥ 7), while 80% made critical errors (scores < 2). For femoral neck fracture CT images (n = 316 residents), the average score was 2.48/10, with 6.71 points lost to observational errors. 25% produced effective reports, and 66% made critical errors. Pediatric tibial/Toddler's fracture radiographs (n = 197 residents) yielded an average score of 2.94/10, with 6.60 points lost to observational errors. 29% generated effective reports, while 71% made critical errors.</p><p><strong>Conclusion: </strong>Radiology residents demonstrated significant difficulty in identifying these orthopedic trauma cases, with errors primarily attributed to observational deficiencies. These findings suggest a need for targeted educational interventions in radiology residency programs to improve the identification of these fractures.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946562","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Outcome of follow-up computed tomography of suspected occult scaphoid fracture after normal radiography.","authors":"Mats Geijer, Eirikur Gunnlaugsson, Linnea Arvidsson, Elin Österhed, Magnus Tägil","doi":"10.1007/s10140-024-02307-0","DOIUrl":"https://doi.org/10.1007/s10140-024-02307-0","url":null,"abstract":"<p><strong>Purpose: </strong>To evaluate the rate of missed scaphoid fractures on follow-up computed tomography (CT) for suspected occult scaphoid fracture after normal radiography with residual radial-sided wrist pain.</p><p><strong>Methods: </strong>In a retrospective analysis, wrist CT during a five-year period was analyzed. The CT examinations and radiological reports were re-evaluated. Available clinical findings and radiologic follow-up performed during a period of a minimum of three years served as outcome reference.</p><p><strong>Results: </strong>In total, 178 examinations had been performed on 174 patients for suspect scaphoid fracture, 67 men and 107 women, showing 15 and 6 scaphoid fractures, respectively; a statistically significant sex difference (p = 0.0024). In 157 examinations, no scaphoid fracture was detected on CT, instead 29 other wrist or carpal bone fractures were found. On follow-up, no missed scaphoid fractures were found. Before CT, 124 of the 157 patients had been treated with a cast. After CT, 35 patients continued with cast treatment for a median of 14 days.</p><p><strong>Conclusions: </strong>CT appears to be a reliable method for evaluating suspect scaphoid fracture as part of a diagnosis-treatment regimen including pain immobilization with a plaster cast.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142946590","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence in emergency and trauma radiology: ASER AI/ML expert panel Delphi consensus statement on research guidelines, practices, and priorities.","authors":"David Dreizin, Garvit Khatri, Pedro V Staziaki, Karen Buch, Mathias Unberath, Mohammed Mohammed, Aaron Sodickson, Bharti Khurana, Anjali Agrawal, James Stephen Spann, Nicholas Beckmann, Zachary DelProposto, Christina A LeBedis, Melissa Davis, Gabrielle Dickerson, Michael Lev","doi":"10.1007/s10140-024-02306-1","DOIUrl":"10.1007/s10140-024-02306-1","url":null,"abstract":"<p><strong>Background: </strong>Emergency/trauma radiology artificial intelligence (AI) is maturing along all stages of technology readiness, with research and development (R&D) ranging from data curation and algorithm development to post-market monitoring and retraining.</p><p><strong>Purpose: </strong>To develop an expert consensus document on best research practices and methodological priorities for emergency/trauma radiology AI.</p><p><strong>Methods: </strong>A Delphi consensus exercise was conducted by the ASER AI/ML expert panel between 2022-2024. In phase 1, a steering committee (7 panelists) established key themes- curation; validity; human factors; workflow; barriers; future avenues; and ethics- and generated an edited, collated long-list of statements. In phase 2, two Delphi rounds using anonymous RAND/UCLA Likert grading were conducted with web-based data capture (round 1) and a bespoke excel document with literature hyperlinks (round 2). Between rounds, editing and knowledge synthesis helped maximize consensus. Statements reaching ≥80% agreement were included in the final document.</p><p><strong>Results: </strong>Delphi rounds 1 and 2 consisted of 81 and 78 items, respectively.18/21 expert panelists (86%) responded to round 1, and 15 to round 2 (17% drop-out). Consensus was reached for 65 statements. Observations were summarized and contextualized. Statements with unanimous consensus centered around transparent methodologic reporting; testing for generalizability and robustness with external data; and benchmarking performance with appropriate metrics and baselines. A manuscript draft was circulated to panelists for editing and final approval.</p><p><strong>Conclusions: </strong>The document is meant as a framework to foster best-practices and further discussion among researchers working on various aspects of emergency and trauma radiology AI.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142876674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparison of clinical and abdominal CT imaging findings in children evaluated for abusive and accidental abdominal trauma.","authors":"Boaz Karmazyn, Reid S Fisher, Doriann M Alcaide, Shannon L Thompson, Rebeca Santos, Gregory S Jennings, George J Eckert, Megan B Marine","doi":"10.1007/s10140-024-02305-2","DOIUrl":"https://doi.org/10.1007/s10140-024-02305-2","url":null,"abstract":"<p><strong>Background: </strong>Diagnosis of child abuse in children evaluated for a blunt abdominal trauma can be challenging due to overlapping types of injuries.</p><p><strong>Objective: </strong>Identify clinical characteristics and CT findings that differentiate children evaluated for accidental abdominal trauma (AcAT) and abusive abdominal trauma (AbAT).</p><p><strong>Materials and methods: </strong>Retrospective (1/2010 to 6/2024) study on children < 3 years-old who had an abdominal CT scan for AcAT or AbAT. Demographic, clinical, and imaging variables were compared between CT-positive child abuse, and accidental trauma.</p><p><strong>Results: </strong>Abdominal CT positive for trauma was found in 26.5% (82/309) children that were evaluated for AAT and in 28.8% (42/146) for AcAT. Children with positive CT for AbAT were significantly younger (average age 0.9 ± 0.9 versus 1.8 ± 0.9 years), and most (70.7%) were younger than one year old. Most children evaluated for AbAT with positive CT (70.7%) had an unknown cause of injury. The most common mechanism provided for abused children was low height fall (18/82, 22.0%) as compared with no low height fall in accidental trauma (p < 0.001). Rib fractures were identified on CT in 5049/82 children (61.059.8%) evaluated for AbAT as compared with 4/42 (9.5%) in children evaluated for AcAT (p < 0.001).</p><p><strong>Conclusion: </strong>In children evaluated for blunt abdominal trauma, presence of rib fractures should alert radiologists to the possibility of child abuse. Abused children were mostly younger than one year, with an unknown mechanism of injury or a fall from a low height.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142871663","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Diagnostic performance of neural network algorithms in skull fracture detection on CT scans: a systematic review and meta-analysis.","authors":"Guive Sharifi, Ramtin Hajibeygi, Seyed Ali Modares Zamani, Ahmed Mohamedbaqer Easa, Ashkan Bahrami, Reza Eshraghi, Maral Moafi, Mohammad Javad Ebrahimi, Mobina Fathi, Arshia Mirjafari, Janine S Chan, Irene Dixe de Oliveira Santo, Mahsa Asadi Anar, Omidvar Rezaei, Long H Tu","doi":"10.1007/s10140-024-02300-7","DOIUrl":"https://doi.org/10.1007/s10140-024-02300-7","url":null,"abstract":"<p><strong>Background and aim: </strong>The potential intricacy of skull fractures as well as the complexity of underlying anatomy poses diagnostic hurdles for radiologists evaluating computed tomography (CT) scans. The necessity for automated diagnostic tools has been brought to light by the shortage of radiologists and the growing demand for rapid and accurate fracture diagnosis. Convolutional Neural Networks (CNNs) are a potential new class of medical imaging technologies that use deep learning (DL) to improve diagnosis accuracy. The objective of this systematic review and meta-analysis is to assess how well CNN models diagnose skull fractures on CT images.</p><p><strong>Methods: </strong>PubMed, Scopus, and Web of Science were searched for studies published before February 2024 that used CNN models to detect skull fractures on CT scans. Meta-analyses were conducted for area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy. Egger's and Begg's tests were used to assess publication bias.</p><p><strong>Results: </strong>Meta-analysis was performed for 11 studies with 20,798 patients. Pooled average AUC for implementing pre-training for transfer learning in CNN models within their training model's architecture was 0.96 ± 0.02. The pooled averages of the studies' sensitivity and specificity were 1.0 and 0.93, respectively. The accuracy was obtained 0.92 ± 0.04. Studies showed heterogeneity, which was explained by differences in model topologies, training models, and validation techniques. There was no significant publication bias detected.</p><p><strong>Conclusion: </strong>CNN models perform well in identifying skull fractures on CT scans. Although there is considerable heterogeneity and possibly publication bias, the results suggest that CNNs have the potential to improve diagnostic accuracy in the imaging of acute skull trauma. To further enhance these models' practical applicability, future studies could concentrate on the utility of DL models in prospective clinical trials.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142827714","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Off-console automated artificial intelligence enhanced workflow enables improved emergency department CT capacity.","authors":"John McMenamy, Sergey Kochkine, Mark Bernstein, Anthony Lucero, Randy Miles, Adam Schwertner, Ashesh Thaker, David M Naeger","doi":"10.1007/s10140-024-02297-z","DOIUrl":"https://doi.org/10.1007/s10140-024-02297-z","url":null,"abstract":"<p><strong>Purpose: </strong>Increasing CT capacity to keep pace with rising ED demand is critical. The conventional process has inherent drawbacks. We evaluated an off-console automated AI enhanced workflow which moves all final series creation off-console. We hypothesized the off-console workflow would 1) decrease overall ED CT exam begin to end times and decrease length and variability of time CT is occupied at the individual exam level.</p><p><strong>Methods: </strong>Study population was identified retrospectively and included all CT exams done on all ED adult patients. 3 months of data was collected using the conventional workflow and 3 months of data was collected after implementation of the off-console workflow. Exam begin and the exam end timestamps were collected from the EMR. Additionally, 4 subgroups from the above conventional and off-console workflows were identified retrospectively with an Emergency Severity Index level 1, undergoing one of the four most common CT exam set(s) performed on ESI level 1 patients.</p><p><strong>Results: </strong>6,795 ED adult patients underwent ED CT in the 3 months immediately prior to implementation of the off-console workflow and 6,708 adult ED patients underwent CT in the 3 months after complete implementation. The off-console workflow demonstrated a 36% decrease in median exam begin to end times (P < 0.001). 4 subgroups demonstrated 56-75% decreases in median CT occupied time (P < 0.001) and decreases in variability in ¾ subgroups.</p><p><strong>Discussion: </strong>This off-console workflow enables increased CT capacity to meet rising ED demand. Similar improvements could be expected across most exam sets and imaging settings if broadly implemented.</p>","PeriodicalId":11623,"journal":{"name":"Emergency Radiology","volume":" ","pages":""},"PeriodicalIF":1.7,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142806480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}