Journal of the American College of Radiology最新文献

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Using Machine Learning to Predict Adherence to Recommended Imaging Follow-Up 利用机器学习预测是否坚持建议的成像随访。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.03.001

引用次数: 0

Future of Interventional Radiology: Advocating for Independence With Careful Consideration 介入放射学的未来：倡导独立，慎重考虑。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.03.020

引用次数: 0

Rogues, Inertia, and the Dogma of Innovation in Health Care 流氓、惰性和医疗创新的教条。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.09.002

Marc D. Succi MD

引用次数: 0

Medical Extended Reality for Radiology Education and Training 用于放射学教育和培训的医学扩展现实。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.05.006

Medical extended reality (MXR), encompassing augmented reality, virtual reality, and mixed reality (MR), presents a novel paradigm in radiology training by offering immersive, interactive, and realistic learning experiences in health care. Although traditional educational tools in the field of radiology are essential, it is necessary to capitalize on the innovative and emerging educational applications of extended reality (XR) technologies. At the most basic level of learning anatomy, XR has been extensively used with an emphasis on its superiority over conventional learning methods, especially in spatial understanding and recall. For imaging interpretation, XR has fostered the concepts of virtual reading rooms by enabling collaborative learning environments and enhancing image analysis and understanding. Moreover, image-guided interventions in interventional radiology have witnessed an uptick in XR utilization, illustrating its effectiveness in procedural training and skill acquisition for medical students and residents in a safe and risk-free environment. However, there remain several challenges and limitations for XR in radiology education, including technological, economic, and ergonomic challenges and and integration into existing curricula. This review explores the transformative potential of MXR in radiology education and training along with insights on the future of XR in radiology education, forecasting advancements in immersive simulations, artificial intelligence integration for personalized learning, and the potential of cloud-based XR platforms for remote and collaborative training. In summation, MXR’s burgeoning role in reshaping radiology education offers a safer, scalable, and more efficient training model that aligns with the dynamic healthcare landscape.

医学扩展现实（MXR）包括增强现实（AR）、虚拟现实（VR）和混合现实（MR），通过在医疗保健领域提供身临其境、互动和逼真的学习体验，为放射学培训提供了一种新的模式。虽然放射学领域的传统教育工具必不可少，但有必要利用 XR 技术的创新和新兴教育应用。在学习解剖学的最基本层面，XR 已得到广泛应用，重点强调其优于传统学习方法，尤其是在空间理解和记忆方面。在影像解读方面，XR 通过提供协作学习环境和加强影像分析与理解，促进了虚拟阅览室概念的发展。此外，介入放射学中的图像引导介入治疗在 XR 的使用上也有所上升，这说明 XR 在安全、无风险的环境中为医学生和住院医师提供程序培训和技能学习方面非常有效。然而，XR 在放射学教育中仍存在一些挑战和限制，包括技术、经济、人体工程学以及与现有课程的整合。本综述探讨了 MXR 在放射学教育和培训中的变革潜力，以及 XR 在放射学教育中的未来发展前景，预测了身临其境模拟、用于个性化学习的人工智能集成以及基于云的 XR 平台在远程和协作培训方面的潜力。总之，MXR 在重塑放射学教育方面的蓬勃发展提供了一种更安全、可扩展和更高效的培训模式，与动态的医疗保健环境相一致。

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引用次数: 0

Improving Patient Understanding of Prostate Cancer Risks Associated With Prostate Imaging Reporting and Data System Lexicon 提高患者对与 PI-RADS 词典相关的前列腺癌风险的认识。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.06.002

引用次数: 0

Empowering Radiology’s Future: A Proposed Leadership and Innovation Framework for a Radiology Department 为放射科的未来赋能：放射科领导力与创新框架建议》。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.07.009

Marc D. Succi MD , Arya Rao , Michael S. Gee MD, PhD , Chris Coburn , James A. Brink MD

引用次数: 0

A Closer Look at the Biden Administration’s Executive Order on Artificial Intelligence: Implications for the Imaging Space 拜登政府关于人工智能的行政命令：对成像领域的影响

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.04.007

引用次数: 0

Global Health Collaboration: Unlocking a Healthier World, One Saturday at a Time 全球卫生合作：一个星期六，一次开启一个更健康的世界。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.09.001

Stephen Avery PhD , Farouk Dako MD, MPH , M. Saiful Huq PhD , Wilfred Ngwa PhD

引用次数: 0

Socio-Economic Factors and Clinical Context Can Predict Adherence to Incidental Pulmonary Nodule Follow-up via Machine Learning Models 社会经济因素和临床环境可通过机器学习模型预测偶然肺结节随访的依从性。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.02.031

Objective

To quantify the relative importance of demographic, contextual, socio-economic, and nodule-related factors that influence patient adherence to incidental pulmonary nodule (IPN) follow-up visits and evaluate the predictive performance of machine learning models utilizing these features.

Methods

We curated a 1,610-subject patient data set from electronic medical records consisting of 13 clinical and socio-economic predictors and IPN follow-up adherence status (timely, delayed, or never) as the outcome. Univariate analysis and multivariate logistic regression were performed to quantify the predictors’ contributions to follow-up adherence. Three additional machine learning models (random forests, neural network, and support vector machine) were fitted and cross-validated to examine prediction performance across different model architectures and evaluate intermodel concordance.

Results

On univariate basis, all 13 predictors except comorbidity were found to have a significant association with follow-up. In multiple logistic regression, inpatient or emergency clinical context (odds ratio favoring never following up: 7.28 and 8.56 versus outpatient, respectively) and high nodule risk (odds ratio: 0.25 versus low risk) are the most significant predictors of follow-up, and sex, race, and marital status become additionally significant if clinical context is removed from the model. Clinical context itself is associated with sex, race, insurance, employment, marriage, income, nodule risk, and smoking status, suggesting its role in mediating socio-economic inequities. On cross-validation, all four machine learning models demonstrated comparable and good predictive performances, with mean area under the curve ranging from 0.759 to 0.802, with sensitivity 0.641 to 0.660 and specificity 0.768 to 0.840.

Conclusion

Socio-economic factors and clinical context are predictive of IPN follow-up adherence, with clinical context being the most significant contributor and likely representing uncaptured socio-economic determinants.

目的量化影响患者坚持偶发肺结节（IPNs）随访的人口、环境、社会经济和结节相关因素的相对重要性，并评估利用这些特征的机器学习模型的预测性能：我们从电子病历（EHR）中收集了 1610 个受试者的患者数据集，其中包括 13 个临床和社会经济预测因素，并将 IPN 随访依从性状态（及时/延迟/从不）作为结果。通过单变量分析和多变量逻辑回归来量化预测因素对随访依从性的影响。另外还拟合了三个机器学习模型（随机森林、神经网络和支持向量机）并进行了交叉验证，以检验不同模型架构的预测性能，并评估模型间的一致性：在单变量基础上，除合并症外，其他 13 个预测因素均与随访有显著关联。在多元逻辑回归中，住院病人或急诊病人的临床背景（与门诊病人相比，从不随访的OR值分别为7.28和8.56）和高结节风险（与低风险相比，OR值为0.25）是随访的最重要预测因素，而如果将临床背景从模型中剔除，性别、种族、婚姻状况则变得更加重要。临床背景本身与性别、种族、保险、就业、婚姻、收入、结节风险和吸烟状况相关，这表明临床背景在调解社会经济不平等方面发挥了作用。在交叉验证中，所有四个机器学习模型都表现出了相当好的预测性能，平均AUC为0.759-0.802，灵敏度为0.641-0.660，特异性为0.768-0.840：社会经济因素和临床环境可预测 IPN 随访的依从性，其中临床环境的作用最大，可能代表了未捕捉到的社会经济决定因素。

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引用次数: 0

Understanding Provider Cost of MRI for Appendicitis in Children: A Time-Driven Activity-Based Costing Analysis 了解儿童阑尾炎核磁共振成像的供应商成本：基于时间驱动活动的成本核算分析。

IF 4 3区医学 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING

Journal of the American College of Radiology

Pub Date : 2024-10-01 DOI: 10.1016/j.jacr.2024.05.008

Objective

To use time driven activity-based costing to characterize the provider cost of rapid MRI for appendicitis compared to other MRI examinations billed with the same Current Procedural Terminology codes commonly used for MRI appendicitis examinations.

Methods

Rapid MRI appendicitis examination was compared with MRI pelvis without intravenous contrast, MRI abdomen/pelvis without intravenous contrast, and MRI abdomen/pelvis with intravenous contrast. Process maps for each examination were created through direct shadowing of patient procedures (n = 20) and feedback from relevant health care professionals. Additional data were collected from the electronic medical record for 327 MRI examinations. Practical capacity cost rates were calculated for personnel, equipment, and facilities. The cost of each step was calculated by multiplying the capacity cost rate with the mean duration of each step. Stepwise costs were summed to generate a total cost for each MRI examination.

Results

The mean duration and costs for MRI examination type were as follows: MRI appendicitis: 11 (range: 6-25) min, $20.03 (7.80-44.24); MRI pelvis without intravenous contrast: 55 (29-205) min, $105.99 (64.18-285.13); MRI abdomen/pelvis without intravenous contrast: 65 (26-173) min, $144.83 (61.16-196.50); MRI abdomen/pelvis with intravenous contrast: 128 (39-303) min, $236.99 (102.62-556.54).

Conclusion

The estimated cost of providing a rapid appendicitis MRI examination is significantly less than other MRI examinations billed using Current Procedural Terminology codes typically used for appendicitis MRI. Mechanisms to appropriately bill rapid MRI examinations with limited sequences are needed to improve cost efficiency for the patient and to enable wider use of limited MRI examinations in the pediatric population.

目的：采用基于时间驱动活动的成本计算（TDABC）方法，对快速磁共振成像阑尾炎检查与其他磁共振成像阑尾炎检查常用的当前程序技术（CPT）代码计费的提供商成本进行比较：将快速磁共振阑尾炎检查与不使用静脉注射造影剂的磁共振骨盆检查、不使用静脉注射造影剂的磁共振腹部/骨盆检查以及使用静脉注射造影剂的磁共振腹部/骨盆检查进行比较。通过直接观察患者的检查过程（20 人）和相关医护人员的反馈，绘制了每种检查的流程图。此外，还从 327 次核磁共振成像检查的电子病历中收集了其他数据。计算了人员、设备和设施的实际能力成本率。将能力成本率乘以每个步骤的平均持续时间，即可计算出每个步骤的成本。将每个步骤的成本相加，得出每次核磁共振成像检查的总成本：核磁共振成像检查类型的平均持续时间和费用如下：MRI 阑尾炎：11（范围：6-25）分钟，20.03 美元（7.80-44.24）；MRI 骨盆，无静脉注射造影剂：55（29-205）分钟，105.99 美元（64.18-285.13）；MRI 腹部/骨盆，无静脉注射造影剂：55（29-205）分钟，105.99 美元（64.18-285.13）。13）；不使用静脉造影剂的 MRI 腹部/骨盆：65（26-173）分钟，144.83 美元（61.16-196.50）；使用静脉造影剂的 MRI 腹部/骨盆：128（39-303）分钟，236.99 美元（102.62-556.54）：结论：提供快速阑尾炎 MRI 检查的估计成本明显低于使用阑尾炎 MRI 常用 CPT 代码计费的其他 MRI 检查。为了提高患者的成本效益，并使有限磁共振成像检查在儿科人群中得到更广泛的应用，需要建立适当的机制对有限序列的快速磁共振成像检查进行收费。

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