Browsing by Subject "Radiology"
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Item A Quality Improvement Initiative to Reduce Unnecessary Screening Chest Radiographs in a Pediatric ICU(American Association of Respiratory Care, 2023) Malin, Stefan W.; Maue, Danielle K.; Cater, Daniel T.; Ealy, Aimee R.; McCallister, Anne E.; Valentine, Kevin M.; Abu-Sultaneh, Samer M.; Pediatrics, School of MedicineBackground: The Critical Care Societies Collaborative included not ordering diagnostic tests at regular intervals as one of their Choosing Wisely initiatives. A reduction in unnecessary chest radiographs (CXRs) can help reduce exposure to radiation and eliminate health care waste. We aimed to reduce daily screening CXRs in a pediatric ICU (PICU) by 20% from baseline within 4 months of implementation of CXR criteria. Methods: All intubated patients in the PICU were included in this quality improvement project. Patients with tracheostomies were excluded. We developed criteria delineating which patients were most likely to benefit from a daily screening CXR, and these criteria were discussed for each patient on rounds. Patients on extracorporeal membrane oxygenation, on high-frequency oscillatory ventilation, or on high support on conventional mechanical ventilation were included as needing a daily screening CXR. We tracked the percentage of intubated subjects receiving a screening CXR as an outcome measure. Unplanned extubations and the number of non-screening CXRs per intubated subject were followed as balancing measures. Results: The percentage of intubated subjects receiving a daily screening CXR was reduced from 79% to 31%. There was no increase in frequency of unplanned extubations or number of non-screening CXRs. With an estimated subject charge of roughly $270 and hospital cost of $54 per CXR, this project led to an estimated $300,000 in patient charge savings and $60,000 in hospital cost savings. Conclusions: Adopting criteria to delineate which patients are most likely to benefit from screening CXRs can lead to a reduction in the percentage of intubated patients receiving screening CXRs without appearing to increase harm.Item Assessing Radiology and Radiation Therapy Needs for Cancer Care in Low-and-Middle-Income Countries: Insight From a Global Survey of Departmental and Institutional Leaders(Elsevier, 2024-08-29) Parker, Stephanie A.; Weygand, Joseph; Bernat, Beata Gontova; Jackson, Amanda M.; Mawlawi, Osama; Barreto, Izabella; Hao, Yao; Khan, Rao; Yorke, Afua A.; Swanson, William; Huq, Mohammed Saiful; Lief, Eugene; Biancia, Cesar Della; Njeh, Christopher F.; Al-Basheer, Ahmad; Chau, Oi Wai; Avery, Stephen; Ngwa, Wilfred; Sandwall, Peter A.; Radiation Oncology, School of MedicinePurpose: The global cancer burden and mortality rates are increasing, with significant disparities in access to care in low- and middle-income countries (LMICs). This study aimed to identify radiology and radiation therapy needs in LMICs from the perspective of departmental and institutional leaders. Methods and materials: A survey was developed and conducted by the American Association of Physicists in Medicine Global Needs Assessment Committee and the American Association of Physicists in Medicine International Council. The survey, organized into 5 sections (Introduction, Infrastructure Needs, Education Needs, Research Needs, and General Information), was open to respondents from March 1, to August 16, 2022. Results: A total of 175 responses were received from 6 global regions: Africa (31.4%), the Americas (17.7%), the Eastern Mediterranean (14.3%), Europe (9.1%), Southeast Asia (23.4%), and the Western Pacific (4.0%). The greatest reported need was for new or updated equipment, particularly positron emission tomography/computed tomography imaging technology. There was also a high demand for clinical and equipment training. Approximately 25% of institutions reported a lack of radiology-based cancer screening programs because of high health care costs and a shortage of specialized equipment. Many institutions that expressed interest in research face funding and grant challenges. Conclusions: The findings highlight critical areas where organizations can support LMICs in enhancing radiology and radiation therapy services to mitigate the growing cancer burden.Item DEEP MOVEMENT: Deep learning of movie files for management of endovascular thrombectomy(Springer, 2023) Kelly, Brendan; Martinez, Mesha; Do, Huy; Hayden, Joel; Huang, Yuhao; Yedavalli, Vivek; Ho, Chang; Keane, Pearse A.; Killeen, Ronan; Lawlor, Aonghus; Moseley, Michael E.; Yeom, Kristen W.; Lee, Edward H.; Radiology and Imaging Sciences, School of MedicineObjectives: Treatment and outcomes of acute stroke have been revolutionised by mechanical thrombectomy. Deep learning has shown great promise in diagnostics but applications in video and interventional radiology lag behind. We aimed to develop a model that takes as input digital subtraction angiography (DSA) videos and classifies the video according to (1) the presence of large vessel occlusion (LVO), (2) the location of the occlusion, and (3) the efficacy of reperfusion. Methods: All patients who underwent DSA for anterior circulation acute ischaemic stroke between 2012 and 2019 were included. Consecutive normal studies were included to balance classes. An external validation (EV) dataset was collected from another institution. The trained model was also used on DSA videos post mechanical thrombectomy to assess thrombectomy efficacy. Results: In total, 1024 videos comprising 287 patients were included (44 for EV). Occlusion identification was achieved with 100% sensitivity and 91.67% specificity (EV 91.30% and 81.82%). Accuracy of location classification was 71% for ICA, 84% for M1, and 78% for M2 occlusions (EV 73, 25, and 50%). For post-thrombectomy DSA (n = 194), the model identified successful reperfusion with 100%, 88%, and 35% for ICA, M1, and M2 occlusion (EV 89, 88, and 60%). The model could also perform classification of post-intervention videos as mTICI < 3 with an AUC of 0.71. Conclusions: Our model can successfully identify normal DSA studies from those with LVO and classify thrombectomy outcome and solve a clinical radiology problem with two temporal elements (dynamic video and pre and post intervention).Item Dental Radiology Portfolio(2023-02-21) Rader, Twyla S.The purpose of this Radiology Portfolio is to facilitate student learning in Dental Radiology. The portfolio will share a series of narrated presentations and videos to provide students with the foundational knowledge of principle radiographic techniques and clinical skills that are necessary to safely and competently take dental radiographs. Students have requested videos of information that was delivered in the classroom setting in order for them to review prior to lab rotations or patient treatment. The following pages are short, how-to videos on several different techniques utilized in Dental Radiology.Item Evaluating the Implementation of Deep Learning in LibreHealth Radiology on Chest X-Rays(Springer, 2019-04-25) Purkayastha, Saptarshi; Buddi, Surendra Babu; Yadav, Bhawana; Nuthakki, Siddhartha; Gichoya, Judy W.Respiratory diseases are the dominant cause of deaths worldwide. In the US, the number of deaths due to chronic lung infections (mostly pneumonia and tuberculosis), lung cancer and chronic obstructive pulmonary disease has increased. Timely and accurate diagnosis of the disease is highly imperative to diminish the deaths. Chest X-ray is a vital diagnostic tool used for diagnosing lung diseases. Delay in X-Ray diagnosis is run-of-the-mill milieu and the reasons for the impediment are mostly because the X-ray reports are arduous to interpret, due to the complex visual contents of radiographs containing superimposed anatomical structures. A shortage of trained radiologists is another cause of increased workload and thus delay. We integrated CheXNet, a neural network algorithm into the LibreHealth Radiology Information System, which allows physicians to upload Chest X-rays and identify diagnosis probabilities. The uploaded images are evaluated from labels for 14 thoracic diseases. The turnaround time for each evaluation is about 30 seconds, which does not affect clinical workflow. A Python Flask application hosted web service is used to upload radiographs into a GPU server containing the algorithm. Thus, the use of this system is not limited to clients having their GPU server, but instead, we provide a web service. To evaluate the model, we randomly split the dataset into training (70%), validation (10%) and test (20%) sets. With over 86% accuracy and turnaround time under 30 seconds, the application demonstrates the feasibility of a web service for machine learning based diagnosis of 14-lung pathologies from Chest X-rays.Item “Failing Up” on Social Media—Finding Opportunities in Moments of #Fail(Elsevier, 2020-10-17) Gadde, Judith Ann; Peterson, Ryan B.; Koontz, Nicholas A.; Radiology and Imaging Sciences, School of MedicineSocial media (SoMe) has been utilized for many years for medical education but has recently grown because of the increase in online learning during the coronavirus disease 2019 (COVID-19) pandemic. Several SoMe platforms are commonly used for online medical education (eg, Twitter [Twitter Inc, San Francisco, California], Instagram [Facebook, Inc, Menlo Park, California], Facebook [Facebook, Inc, Menlo Park, California]) [1, 2, 3]. Twitter has become popular among radiologists for medical education with obstacles occurring along the way. This article represents a collection of experiences from three neuroradiologists who use Twitter to disseminate case-based radiology education as part of institutionally approved curricula. In this article, we share advice for those interested in utilizing SoMe for medical education purposes, including experiences in which obstacles redefined our educational strategies, turning failures into opportunities for improvement.Item The LOINC RSNA radiology playbook - a unified terminology for radiology procedures(Oxford Academic, 2018-07-01) Vreeman, Daniel J.; Abhyankar, Swapna; Wang, Kenneth C.; Carr, Christopher; Collins, Beverly; Rubin, Daniel L.; Langlotz, Curtis P.; Medicine, School of MedicineObjective: This paper describes the unified LOINC/RSNA Radiology Playbook and the process by which it was produced. Methods: The Regenstrief Institute and the Radiological Society of North America (RSNA) developed a unification plan consisting of six objectives 1) develop a unified model for radiology procedure names that represents the attributes with an extensible set of values, 2) transform existing LOINC procedure codes into the unified model representation, 3) create a mapping between all the attribute values used in the unified model as coded in LOINC (ie, LOINC Parts) and their equivalent concepts in RadLex, 4) create a mapping between the existing procedure codes in the RadLex Core Playbook and the corresponding codes in LOINC, 5) develop a single integrated governance process for managing the unified terminology, and 6) publicly distribute the terminology artifacts. Results: We developed a unified model and instantiated it in a new LOINC release artifact that contains the LOINC codes and display name (ie LONG_COMMON_NAME) for each procedure, mappings between LOINC and the RSNA Playbook at the procedure code level, and connections between procedure terms and their attribute values that are expressed as LOINC Parts and RadLex IDs. We transformed all the existing LOINC content into the new model and publicly distributed it in standard releases. The organizations have also developed a joint governance process for ongoing maintenance of the terminology. Conclusions: The LOINC/RSNA Radiology Playbook provides a universal terminology standard for radiology orders and results.Item Look for the helpers(Springer, 2020-06-30) Gunderman, Richard B.; Radiology and Imaging Sciences, School of MedicineItem Radiologic Imaging of CAR T-Cell Therapy: Looking under the Hood to Move Us Forward(Radiological Society of North America, 2022) Langer, Mark P.; Radiation Oncology, School of MedicineItem Work-Related Stress: Lessons From the US Marine Corps(Elsevier, 2018-03-01) Sinsabaugh, Christopher A.; Brown, Brandon P.; Gunderman, Richard B.; Radiology and Imaging Sciences, School of Medicine