Journal of the American College of Radiology最新文献

{"title":"The Potential Clinical Utility of an Artificial Intelligence Model for Identification of Vertebral Compression Fractures in Chest Radiographs","authors":"Ankita Ghatak MSc ,&nbsp;James M. Hillis MBBS, DPhil ,&nbsp;Sarah F. Mercaldo PhD ,&nbsp;Isabella Newbury-Chaet BSc ,&nbsp;John K. Chin MD ,&nbsp;Subba R. Digumarthy MBBS ,&nbsp;Karen Rodriguez MD ,&nbsp;Victorine V. Muse MD ,&nbsp;Katherine P. Andriole PhD ,&nbsp;Keith J. Dreyer DO, PhD ,&nbsp;Mannudeep K. Kalra MBBS, MD ,&nbsp;Bernardo C. Bizzo MD, PhD","doi":"10.1016/j.jacr.2024.08.026","DOIUrl":"10.1016/j.jacr.2024.08.026","url":null,"abstract":"<div><h3>Purpose</h3><div>To assess the ability of the Annalise Enterprise CXR Triage Trauma (Annalise AI Pty Ltd, Sydney, NSW, Australia) artificial intelligence model to identify vertebral compression fractures on chest radiographs and its potential to address undiagnosed osteoporosis and its treatment.</div></div><div><h3>Materials and methods</h3><div>This retrospective study used a consecutive cohort of 596 chest radiographs from four US hospitals between 2015 and 2021. Each radiograph included both frontal (anteroposterior or posteroanterior) and lateral projections. These radiographs were assessed for the presence of vertebral compression fracture in a consensus manner by up to three thoracic radiologists. The model then performed inference on the cases. A chart review was also performed for the presence of osteoporosis-related <em>International Classification of Diseases</em>, 10th revision diagnostic codes and medication use for the study period and an additional year of follow-up.</div></div><div><h3>Results</h3><div>The model successfully completed inference on 595 cases (99.8%); these cases included 272 positive cases and 323 negative cases. The model performed with area under the receiver operating characteristic curve of 0.955 (95% confidence interval [CI]: 0.939-0.968), sensitivity 89.3% (95% CI: 85.7%-92.7%) and specificity 89.2% (95% CI: 85.4%-92.3%). Out of the 236 true-positive cases (ie, correctly identified vertebral compression fractures by the model) with available chart information, only 86 (36.4%) had a diagnosis of vertebral compression fracture and 140 (59.3%) had a diagnosis of either osteoporosis or osteopenia; only 78 (33.1%) were receiving a disease-modifying medication for osteoporosis.</div></div><div><h3>Conclusion</h3><div>The model identified vertebral compression fracture accurately with a sensitivity 89.3% (95% CI: 85.7%-92.7%) and specificity of 89.2% (95% CI: 85.4%-92.3%). Its automated use could help identify patients who have undiagnosed osteoporosis and who may benefit from taking disease-modifying medications.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 2","pages":"Pages 220-229"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302715","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Life: Distributed and Open Source","authors":"Matthew Mullenweg ,&nbsp;Elliot K. Fishman MD ,&nbsp;Linda C. Chu MD ,&nbsp;Steven P. Rowe MD, PhD ,&nbsp;Ryan C. Rizk BS","doi":"10.1016/j.jacr.2024.08.005","DOIUrl":"10.1016/j.jacr.2024.08.005","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 2","pages":"Pages 240-242"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142001512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Hands-Free Approach With Voice to Text and Generative Artificial Intelligence: Streamlining Radiology Reporting","authors":"Austin Young BS ,&nbsp;Katherine E. Wang BA ,&nbsp;Michael X. Jin MD ,&nbsp;Kian Avilla BS ,&nbsp;Kevin Gilotra BS ,&nbsp;Pamela Nguyen MD ,&nbsp;Pablo R. Ros MD","doi":"10.1016/j.jacr.2024.10.004","DOIUrl":"10.1016/j.jacr.2024.10.004","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 2","pages":"Pages 200-203"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142482494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Deployment of an Emergency Department Radiology Dashboard to Improve Communication and Transparency of Radiologic Imaging and Report Status","authors":"Ali H. Dhanaliwala MD, PhD ,&nbsp;Amanda J. Deutsch MD ,&nbsp;Jeffrey Moon MD ,&nbsp;Darco Lalevic MCIT ,&nbsp;Charles Chambers MCIT, MHCI ,&nbsp;Tessa S. Cook MD, PhD","doi":"10.1016/j.jacr.2024.11.024","DOIUrl":"10.1016/j.jacr.2024.11.024","url":null,"abstract":"<div><h3>Purpose</h3><div>The status of radiology examinations affects the flow of patients through the emergency department (ED). Yet this information is not readily available to ED physicians, nurses, and staff members (collectively referred to as ED staff members) or patients. The aim of this study was to improve ED workflow by providing real-time information about the status of radiology reports to ED staff members.</div></div><div><h3>Methods</h3><div>A dashboard displaying real-time information on the status of pending radiology examinations as extracted from the electronic medical record and radiology information system was developed for display in the ED. An algorithm based on historical trends was developed for predicting expected turnaround times (TATs). Focus groups, surveys, and dashboard use data were used to gather feedback and understand utility.</div></div><div><h3>Results</h3><div>The ED radiology dashboard was successfully deployed to four EDs within the health system. The dashboard received an average of 9,397 unique views per week the first year and 802 views per week in the following 2 years after deployment. Most examinations had TATs better than the estimated time, and fewer than 1% had TATs greater than 2 hours from the estimated time. No differences were found between pre- and postsurvey opinion results.</div></div><div><h3>Conclusions</h3><div>A web-based dashboard that displays radiologic imaging study status is a low-cost, high-yield method to improve communication between radiology and ED staff members.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 2","pages":"Pages 191-199"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142775241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Planning for the Future: Modeling Growth and Attrition in the Radiologist Workforce","authors":"Matthew D. Phelps MD ,&nbsp;Diana L. Lam MD","doi":"10.1016/j.jacr.2024.11.002","DOIUrl":"10.1016/j.jacr.2024.11.002","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 2","pages":"Pages 170-171"},"PeriodicalIF":4.0,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Classification and Communication of Critical Findings in Emergency Radiology: A Scoping Review","authors":"Lucas Corallo HBSc ,&nbsp;D. Blair Macdonald MD, FRCPC ,&nbsp;Fatma Eldehimi MD ,&nbsp;Anirudh Venugopalan Nair MD, FRCR, MBA ,&nbsp;Simeon Mitchell MD, CM, MScHQ","doi":"10.1016/j.jacr.2024.09.006","DOIUrl":"10.1016/j.jacr.2024.09.006","url":null,"abstract":"<div><h3>Purpose</h3><div>To identify the published standards for the classification and communication of critical actionable findings in emergency radiology and the associated facilitators and barriers to communication and message management or dissemination of such findings.</div></div><div><h3>Materials and methods</h3><div>Search terms for resources pertaining to critical findings (CFs) in emergency radiology were applied to two databases (PubMed, Embase). Screening of hits using the following pre-established inclusion and exclusion criteria were performed by three analysts with subsequent consensus discussion for discrepancies: (1) the resources include any standards for the classification and communication of imaging findings as critical, <em>or</em> (2) the resource discusses any <em>facilitators</em> to the communication of CFs, <em>or</em> (3) the resource discusses any <em>barriers</em> to the communication of CFs. Resources with explicit focus on a pediatric population or predominant focus on artificial intelligence or natural language processing were omitted. Accompanying gray literature search was used to expand included resources. Data extraction included year, country, resource type, scope or purpose, participants, context, standards to identifying or communicating CFs, facilitators and barriers, method type, recommendations, applicability, and disclosures.</div></div><div><h3>Results</h3><div>Seventy-six resources were included in the final analysis, including 16 societal or commission guidelines. Among the guidelines, no standardized list of CFs was identified, with typical recommendations suggesting application of a local policy. Communication standards included direct closed-loop communication for high acuity findings, with more flexible communication channels for less acute findings. Applied interventions for CFs management most frequently fell into four categories: electronic (n = 10), hybrid (ie, electronic or administrative) (n = 3), feedback or education (n = 5), and administrative (n = 4).</div></div><div><h3>Conclusion</h3><div>There are published standards, policies, and interventions for the management of CFs in emergency radiology. Three-tier stratification (eg, critical, urgent, incidental) based on time sensitivity and severity is most common with most CFs necessitating closed-loop communication. Awareness of systemic facilitators and barriers should inform local policy development. Electronic and administrative communication pathways are useful adjuncts. Further research should offer comparative analyses of different CF interventions with regard to cost-effectiveness, notification time, and user feedback.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 1","pages":"Pages 44-55"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142336722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spaced Repetition Learning in Radiology Education: Exploring Its Potential and Practical Application","authors":"Kaelan Yao MS ,&nbsp;Jeffers Nguyen MD ,&nbsp;Mahan Mathur MD","doi":"10.1016/j.jacr.2024.11.020","DOIUrl":"10.1016/j.jacr.2024.11.020","url":null,"abstract":"<div><div>In today’s medical landscape, rapidly learning vast amounts of information requires innovative learning methods. Spaced repetition tools (like Anki) aid efficient knowledge absorption and retention among medical trainees. Yet, adoption of these tools in radiology medical student education lags despite proven effectiveness. This article highlights spaced repetition as a learning tool alongside other evidence-based educational practices, aiming to revolutionize radiology education among medical students. We (1) describe the educational theory and current application of spaced repetition in the setting of other learning techniques often found in undergraduate medical education; (2) underscore the underutilization of tools such as Anki in radiology education; and (3) offer practical guidance for educators interested in integrating spaced repetition into their teaching methodologies.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 1","pages":"Pages 15-21"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142755791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"JACR Annual Awards 2024","authors":"","doi":"10.1016/j.jacr.2024.11.001","DOIUrl":"10.1016/j.jacr.2024.11.001","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 1","pages":"Pages 1-2"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143153100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Preference Signaling in the Radiology Residency Match: National Survey of Applicants","authors":"Dhairya A. Lakhani MD ,&nbsp;Mahla Radmard MD ,&nbsp;Armin Tafazolimoghadam MD ,&nbsp;Sahil Patel MD ,&nbsp;Arun Murugesan MD ,&nbsp;Hammad Malik MD ,&nbsp;Jeffery P. Hogg MD ,&nbsp;Ziling Shen ScM ,&nbsp;David M. Yousem MD, MBA ,&nbsp;Francis Deng MD","doi":"10.1016/j.jacr.2024.08.009","DOIUrl":"10.1016/j.jacr.2024.08.009","url":null,"abstract":"<div><h3>Objective</h3><div>Two-tiered preference signaling has been implemented in the radiology residency application system to reduce congestion in the setting of high-volume applications. Signals are an indicator of strong interest that an applicant can transmit to a limited number of programs. This study assessed the impact of program signaling on interview invitations, how applicants strategically used signals based on their application’s competitiveness, and applicants’ attitudes toward the current signaling system.</div></div><div><h3>Methods</h3><div>A survey was sent to radiology residency applicants registered with TheRadRoom during the 2024 application cycle. We queried the applicants’ background, applications, signal distribution, and interview outcome depending on the type of signal sent. We also asked whether respondents received an interview invitation from a hypothetical “comparator nonsignaled program” if they had one additional signal to use. Group differences were assessed using nonparametric Wilcoxon signed rank test.</div></div><div><h3>Results</h3><div>A total of 202 applicants completed the survey (28% response rate). Most applied to diagnostic radiology (81%). Nearly all respondents used all six gold (98%) and six silver (96.5%) signals. Interview invitation rates were significantly higher for signaled programs (59.8% ± 27.4%) than nonsignaled (8.5% ± 8.5%); the invitation rate at the comparator nonsignaled programs was 37%. Gold-signaled programs had significantly higher interview rates (67.8% ± 29.3) than silver (51.8% ± 31.3%). Respondents used 49.2% (±21.7%) of their signals for “likely to match” programs, 33.1% (±20.9%) for “aspirational” programs, and 17.6% (±15.8%) for “safety” programs. Most respondents (146; 76%) supported continuing the signaling system for future cycles.</div></div><div><h3>Conclusion</h3><div>Signaling programs significantly enhanced interview invitation rates, with gold signals being more effective than silver. The applicants used about six total signals for “likely-to-match” programs, two for “aspirational” programs, and about four for “safety” programs.</div></div>","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 1","pages":"Pages 116-124"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142115723","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Examining the Effects of a Narrative-Based Educational Animation for Radiology Technologists About Discontinuing Gonadal Shielding","authors":"Ann Seliger MA ,&nbsp;Mahadevappa Mahesh MS, PhD ,&nbsp;Lydia Gregg MA, CMI","doi":"10.1016/j.jacr.2024.09.004","DOIUrl":"10.1016/j.jacr.2024.09.004","url":null,"abstract":"","PeriodicalId":49044,"journal":{"name":"Journal of the American College of Radiology","volume":"22 1","pages":"Pages 125-128"},"PeriodicalIF":4.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142302710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}