1
|
Loose R, Vaño E, Ammon J, Andersson J, Brat H, Brkljacic B, Caikovska K, Corridori R, Damilakis J, De Bondt T, Frija G, Granata C, Hoeschen C, Kotter E, Kralik I, McNulty J, Paulo G, Tsapaki V. The use of Dose Management Systems in Europe: Results of an ESR EuroSafe Imaging Questionnaire. Insights Imaging 2024; 15:201. [PMID: 39120665 PMCID: PMC11315857 DOI: 10.1186/s13244-024-01765-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2024] [Accepted: 05/26/2024] [Indexed: 08/10/2024] Open
Abstract
Dose management systems (DMS) are an essential tool for quality assurance and optimising patient radiation exposure. For radiologists and medical physicists, they are important for managing many radiation protection tasks. In addition, they help fulfil the requirements of Directive 2013/59/EURATOM regarding the electronic transmission of dosimetric data and the detection of unintended patient exposures. The EuroSafe Imaging Clinical Dosimetry and Dose Management Working Group launched a questionnaire on the use of DMS in European member states and analysed the results in terms of modalities, frequency of radiological procedures, involvement of medical physics experts (MPEs), legal requirements, and local issues (support by information technology (IT), modality interfaces, protocol mapping, clinical workflow, and associated costs). CRITICAL RELEVANCE STATEMENT: Despite the great advantages of dose management systems for optimising radiation protection, distribution remains insufficient. This questionnaire shows that reasons include: a lack of DICOM interfaces, insufficient harmonisation of procedure names, lack of medical physicist and IT support, and costs. KEY POINTS: Quantitative radiation dose information is essential for justification and optimisation in medical imaging. Guidelines are required to ensure radiation dose management systems quality and for acceptance testing. Verifying dose data management is crucial before dose management systems clinical implementation. Medical physics experts are professionals who have important responsibilities for the proper management of dose monitoring.
Collapse
Affiliation(s)
- Reinhard Loose
- Institute of Medical Physics, Paracelsus Medical School, Hospital Nuremberg, Nuremberg, Germany
| | - Eliseo Vaño
- Radiology Department, Complutense University, Madrid, Spain
| | - Josefin Ammon
- Institute of Medical Physics, Paracelsus Medical School, Hospital Nuremberg, Nuremberg, Germany
| | - Jonas Andersson
- Department of Diagnostics and Intervention, Radiation Physics, Umeå University, SE-091 87 Umeå, Sweden
| | | | - Boris Brkljacic
- University of Zagreb School of Medicine, Department of Diagnostic and Interventional Radiology, UH Dubrava, Zagreb, Croatia
| | | | - Riccardo Corridori
- European Coordination Committee of the Radiological, Electromedical and Healthcare IT Enterprises (COCIR), Brussels, Belgium
| | - John Damilakis
- University of Crete, School of Medicine, Iraklion, Crete, Greece
| | - Timo De Bondt
- Department of Medical Physics, VITAZ, Moerlandstraat 1, 9100 Sint-Niklaas, Belgium
| | - Guy Frija
- Paris Cité University, Paris, France
| | - Claudio Granata
- Department of Pediatric Radiology Institute for Maternal and Child Health—IRCCS “Burlo Garofolo”—Trieste (I), Trieste, Italy
| | | | - Elmar Kotter
- Department of Radiology, Medical Center—University of Freiburg, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Ivana Kralik
- University of Zagreb School of Medicine, Department of Diagnostic and Interventional Radiology, UH Dubrava, Zagreb, Croatia
| | | | - Graciano Paulo
- Health and Technology Research Center, Escola Superior de Tecnologia da Saúde de Coimbra, Instituto Politécnico de Coimbra, Coimbra, Portugal
| | | | | |
Collapse
|
2
|
Napravnik M, Hržić F, Tschauner S, Štajduhar I. Building RadiologyNET: an unsupervised approach to annotating a large-scale multimodal medical database. BioData Min 2024; 17:22. [PMID: 38997749 PMCID: PMC11245804 DOI: 10.1186/s13040-024-00373-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2023] [Accepted: 06/30/2024] [Indexed: 07/14/2024] Open
Abstract
BACKGROUND The use of machine learning in medical diagnosis and treatment has grown significantly in recent years with the development of computer-aided diagnosis systems, often based on annotated medical radiology images. However, the lack of large annotated image datasets remains a major obstacle, as the annotation process is time-consuming and costly. This study aims to overcome this challenge by proposing an automated method for annotating a large database of medical radiology images based on their semantic similarity. RESULTS An automated, unsupervised approach is used to create a large annotated dataset of medical radiology images originating from the Clinical Hospital Centre Rijeka, Croatia. The pipeline is built by data-mining three different types of medical data: images, DICOM metadata and narrative diagnoses. The optimal feature extractors are then integrated into a multimodal representation, which is then clustered to create an automated pipeline for labelling a precursor dataset of 1,337,926 medical images into 50 clusters of visually similar images. The quality of the clusters is assessed by examining their homogeneity and mutual information, taking into account the anatomical region and modality representation. CONCLUSIONS The results indicate that fusing the embeddings of all three data sources together provides the best results for the task of unsupervised clustering of large-scale medical data and leads to the most concise clusters. Hence, this work marks the initial step towards building a much larger and more fine-grained annotated dataset of medical radiology images.
Collapse
Affiliation(s)
- Mateja Napravnik
- Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka, 51000, Croatia
| | - Franko Hržić
- Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka, 51000, Croatia
- Center for Artificial Intelligence and Cybersecurity, Radmile Matejcic 2, Rijeka, 51000, Croatia
| | - Sebastian Tschauner
- Division of Pediatric Radiology, Department of Radiology, Medical University of Graz, Neue Stiftingtalstraße 6, Graz, 8010, Austria
| | - Ivan Štajduhar
- Faculty of Engineering, University of Rijeka, Vukovarska 58, Rijeka, 51000, Croatia.
- Center for Artificial Intelligence and Cybersecurity, Radmile Matejcic 2, Rijeka, 51000, Croatia.
| |
Collapse
|
3
|
Jeon K, Park WY, Kahn CE, Nagy P, You SC, Yoon SH. Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility. Invest Radiol 2024:00004424-990000000-00232. [PMID: 38985896 DOI: 10.1097/rli.0000000000001106] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/12/2024]
Abstract
ABSTRACT Artificial intelligence (AI) has made significant advances in radiology. Nonetheless, challenges in AI development, validation, and reproducibility persist, primarily due to the lack of high-quality, large-scale, standardized data across the world. Addressing these challenges requires comprehensive standardization of medical imaging data and seamless integration with structured medical data.Developed by the Observational Health Data Sciences and Informatics community, the OMOP Common Data Model enables large-scale international collaborations with structured medical data. It ensures syntactic and semantic interoperability, while supporting the privacy-protected distribution of research across borders. The recently proposed Medical Imaging Common Data Model is designed to encompass all DICOM-formatted medical imaging data and integrate imaging-derived features with clinical data, ensuring their provenance.The harmonization of medical imaging data and its seamless integration with structured clinical data at a global scale will pave the way for advanced AI research in radiology. This standardization will enable federated learning, ensuring privacy-preserving collaboration across institutions and promoting equitable AI through the inclusion of diverse patient populations. Moreover, it will facilitate the development of foundation models trained on large-scale, multimodal datasets, serving as powerful starting points for specialized AI applications. Objective and transparent algorithm validation on a standardized data infrastructure will enhance reproducibility and interoperability of AI systems, driving innovation and reliability in clinical applications.
Collapse
Affiliation(s)
- Kyulee Jeon
- From the Department of Biomedical Systems Informatics, Yonsei University, Seoul, South Korea (K.J., S.C.Y.); Institution for Innovation in Digital Healthcare, Yonsei University, Seoul, South Korea (K.J., S.C.Y.); Biomedical Informatics and Data Science, Johns Hopkins University, Baltimore, MD (W.Y.P., P.N.); Department of Radiology, University of Pennsylvania, Philadelphia, PA (C.E.K.); and Department of Radiology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, South Korea (S.H.Y.)
| | | | | | | | | | | |
Collapse
|
4
|
Cram D, Eid M, Goldburgh MM, Nagels J, Yudkovitch L, Towbin AJ. Report of the HIMSS-SIIM Enterprise Imaging Community Data Standards Evaluation Workgroup: Anatomic Ontology Assessment. JOURNAL OF IMAGING INFORMATICS IN MEDICINE 2024:10.1007/s10278-024-01118-6. [PMID: 38858261 DOI: 10.1007/s10278-024-01118-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Revised: 04/07/2024] [Accepted: 04/09/2024] [Indexed: 06/12/2024]
Abstract
Previously, the lack of a standard body part ontology has been identified as a critical deficiency needed to enable enterprise imaging. This whitepaper aims to provide a comprehensive assessment of anatomical ontologies with the aim of facilitating enterprise imaging. It offers an overview of the process undertaken by the Health Information Management Systems Society (HIMSS) and Society for Imaging Informatics in medicine (SIIM) Enterprise Imaging Community Data Standards Evaluation workgroup to assess the viability of existing ontologies for supporting cross-disciplinary medical imaging workflows. The report analyzes the responses received from representatives of three significant ontologies: SNOMED CT, LOINC, and ICD, and delves into their suitability for the complex landscape of enterprise imaging. It highlights the strengths and limitations of each ontology, ultimately concluding that SNOMED CT is the most viable solution for standardizing anatomy terminology across the medical imaging community.
Collapse
Affiliation(s)
- Dawn Cram
- PaxeraHealth, 85 Wells Street, Newton, MA, 02459, USA
| | - Monief Eid
- Ministry of Health, Riyadh, Saudi Arabia
| | | | | | | | - Alexander J Towbin
- Department of Radiology, Cincinnati Children's Hospital, Cincinnati, OH, USA.
- Department of Radiology, University of Cincinnati College of Medicine, Cincinnati, OH, USA.
| |
Collapse
|
5
|
Yagahara A, Ando D, Oda M. Demonstration of Japanese radiographic examination codes in establishing typical values for a wide variety of general radiography examinations. Sci Rep 2024; 14:2249. [PMID: 38278840 PMCID: PMC10817891 DOI: 10.1038/s41598-024-52294-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2023] [Accepted: 01/16/2024] [Indexed: 01/28/2024] Open
Abstract
The purpose of this study was to demonstrate Japanese radiographic examination codes JJ1017 in establishing typical values for a wide variety of general radiography. About 200,000 sets of examination data were collected, including exposure conditions, JJ1017 code applied, examination room numbers and patient information. Typical values for adults, children, and infants were calculated from the collected data, and the following items were examined: comparing typical values of general radiography in Japan DRLs 2015 and typical values in a facility; comparison of typical values between X-ray equipment for examinations of DRLs 2015; comparison of typical values for different procedures at the same anatomical site; identification of examination items associated with high radiation doses. The total numbers of JJ1017 codes applicable to the examinations were 45,372 for adults, 542 for children, and 2339 for infants. To calculate the typical values and compare these with the DRLs, we used a combination of JJ1017 anatomical codes, posture codes, and direction of radiation codes. The combination of these codes allowed the calculation of a typical value and comparison with DRLs 2015. Comparison between devices reveals differences in radiation doses and provides an opportunity to review the characteristics of the devices and their operation to suggest dose reductions. By calculating typical values for examination items for which the DRLs were not available, we were able to identify examination items with high doses in a facility and suggest items that should be audited in the facility.
Collapse
Affiliation(s)
- Ayako Yagahara
- Faculty of Health Sciences, Hokkaido University of Science, Sapporo, Japan.
| | - Daisuke Ando
- Department of Radiology, Southern TOHOKU Proton Therapy Center, Koriyama, Japan
| | - Makoto Oda
- Department of Radiological Technology, Hokkaido University Hospital, Sapporo, Japan
| |
Collapse
|
6
|
Kalokyri V, Kondylakis H, Sfakianakis S, Nikiforaki K, Karatzanis I, Mazzetti S, Tachos N, Regge D, Fotiadis DI, Marias K, Tsiknakis M. MI-Common Data Model: Extending Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM) for Registering Medical Imaging Metadata and Subsequent Curation Processes. JCO Clin Cancer Inform 2023; 7:e2300101. [PMID: 38061012 PMCID: PMC10715775 DOI: 10.1200/cci.23.00101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 08/21/2023] [Accepted: 09/29/2023] [Indexed: 12/18/2023] Open
Abstract
PURPOSE The explosion of big data and artificial intelligence has rapidly increased the need for integrated, homogenized, and harmonized health data. Many common data models (CDMs) and standard vocabularies have appeared in an attempt to offer harmonized access to the available information, with Observational Medical Outcomes Partnership (OMOP)-CDM being one of the most prominent ones, allowing the standardization and harmonization of health care information. However, despite its flexibility, still capturing imaging metadata along with the corresponding clinical data continues to pose a challenge. This challenge arises from the absence of a comprehensive standard representation for image-related information and subsequent image curation processes and their interlinkage with the respective clinical information. Successful resolution of this challenge holds the potential to enable imaging and clinical data to become harmonized, quality-checked, annotated, and ready to be used in conjunction, in the development of artificial intelligence models and other data-dependent use cases. METHODS To address this challenge, we introduce medical imaging (MI)-CDM-an extension of the OMOP-CDM specifically designed for registering medical imaging data and curation-related processes. Our modeling choices were the result of iterative numerous discussions among clinical and AI experts to enable the integration of imaging and clinical data in the context of the ProCAncer-I project, for answering a set of clinical questions across the prostate cancer's continuum. RESULTS Our MI-CDM extension has been successfully implemented for the use case of prostate cancer for integrating imaging and curation metadata along with clinical information by using the OMOP-CDM and its oncology extension. CONCLUSION By using our proposed terminologies and standardized attributes, we demonstrate how diverse imaging modalities can be seamlessly integrated in the future.
Collapse
Affiliation(s)
- Varvara Kalokyri
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Haridimos Kondylakis
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Stelios Sfakianakis
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Katerina Nikiforaki
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Ioannis Karatzanis
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Simone Mazzetti
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
- Department of Surgical Sciences, University of Turin, Turin, Italy
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Nikolaos Tachos
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
- Biomedical Research Institute, Foundation of Research and Technology Hellas, University Campus of Ioannina, Ioannina, Greece
| | - Daniele Regge
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
- Radiology Unit, Candiolo Cancer Institute, FPO-IRCCS, Candiolo, Italy
| | - Dimitrios I. Fotiadis
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
- Biomedical Research Institute, Foundation of Research and Technology Hellas, University Campus of Ioannina, Ioannina, Greece
| | - Konstantinos Marias
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| | - Manolis Tsiknakis
- Institute of Computer Science, Foundation of Research and Technology Hellas, Heraklion, Greece
| |
Collapse
|
7
|
Wollschläger D, Jahnen A, Hermen J, Giussani A, Stamm G, Borowski M, Huisinga C, Mentzel HJ, Braun J, Sigmund G, Wagner J, Adolph J, Gunschera J, Koerber F, Schiefer A, Müller B, Lenzen H, Doering T, Entz K, Kunze C, Starck P, Staatz G, Mildenberger P, Pokora R. Pediatric computed tomography doses in Germany from 2016 to 2018 based on large-scale data collection. Eur J Radiol 2023; 163:110832. [PMID: 37059005 DOI: 10.1016/j.ejrad.2023.110832] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 04/03/2023] [Accepted: 04/07/2023] [Indexed: 04/16/2023]
Abstract
PURPOSE Accumulating evidence from epidemiological studies that pediatric computed tomography (CT) examinations can be associated with a small but non-zero excess risk for developing leukemia or brain tumor highlights the need to optimize doses of pediatric CT procedures. Mandatory dose reference levels (DRL) can support reduction of collective dose from CT imaging. Regular surveys of applied dose-related parameters are instrumental to decide when technological advances and optimized protocol design allow lower doses without sacrificing image quality. Our aim was to collect dosimetric data to support adapting current DRL to changing clinical practice. METHOD Dosimetric data and technical scan parameters from common pediatric CT examinations were retrospectively collected directly from Picture Archiving and Communication Systems (PACS), Dose Management Systems (DMS), and Radiological Information Systems (RIS). RESULTS We collected data from 17 institutions on 7746 CT series from the years 2016 to 2018 from examinations of the head, thorax, abdomen, cervical spine, temporal bone, paranasal sinuses and knee in patients below 18 years of age. Most of the age-stratified parameter distributions were lower than distributions from previously-analyzed data from before 2010. Most of the third quartiles were lower than German DRL at the time of the survey. CONCLUSIONS Directly interfacing PACS, DMS, and RIS installations allows large-scale data collection but relies on high data-quality at the documentation stage. Data should be validated by expert knowledge or guided questionnaires. Observed clinical practice in pediatric CT imaging suggests lowering some DRL in Germany is reasonable.
Collapse
Affiliation(s)
- Daniel Wollschläger
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany.
| | - Andreas Jahnen
- Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg
| | - Johannes Hermen
- Luxembourg Institute of Science and Technology (LIST), Esch-sur-Alzette, Luxembourg
| | | | - Georg Stamm
- Department of Radiology, University Medical Center Goettingen, Goettingen, Germany
| | - Markus Borowski
- Department of Radiology and Nuclear Medicine, Städtisches Klinikum Braunschweig, Braunschweig, Germany
| | - Carolin Huisinga
- Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany
| | - Hans-Joachim Mentzel
- Institute for Diagnostic and Interventional Radiology, University Hospital Jena, Jena, Germany
| | - Jochen Braun
- Diagnosticum Neuburg MVZ, Neuburg an der Donau, Germany
| | | | - Joachim Wagner
- Institute for Radiology and Interventional Therapy, Vivantes Klinikum im Friedrichshain, Berlin, Germany
| | - Juergen Adolph
- Department of Radiology, Klinikum Worms gGmbH, Worms, Germany
| | - Jana Gunschera
- Department of Radiology, Carl-Thiem-Klinikum Cottbus, Cottbus, Germany
| | - Friederike Koerber
- Institute for Diagnostic and Interventional Radiology, University Hospital of Cologne, Cologne, Germany
| | - Anna Schiefer
- Pediatric Radiology, Klinikum Nuremberg, Nuremberg, Germany
| | - Birgit Müller
- Institute of Medical Physics, Klinikum Nuremberg, Nuremberg, Germany
| | - Horst Lenzen
- Institute of Clinical Radiology, University Hospital Muenster, Muenster, Germany
| | | | - Kathrin Entz
- Department of Diagnostic and Interventional Radiology, University Hospital Frankfurt, Frankfurt, Germany
| | - Christian Kunze
- Clinic and Policlinic of Radiology, Martin-Luther University Halle-Wittenberg, Halle (Saale), Germany
| | - Peter Starck
- Institute for Diagnostic and Interventional Radiology, Städtisches Klinikum Karlsruhe gGmbH, Karlsruhe, Germany
| | - Gundula Staatz
- Department of Diagnostic and Interventional Radiology, Section of Pediatric Radiology. University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Peter Mildenberger
- Department of Diagnostic and Interventional Radiology. University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Roman Pokora
- Institute of Medical Biostatistics, Epidemiology and Informatics, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| |
Collapse
|
8
|
Chepelev LL, Kwan D, Kahn CE, Filice RW, Wang KC. Ontologies in the New Computational Age of Radiology: RadLex for Semantics and Interoperability in Imaging Workflows. Radiographics 2023; 43:e220098. [PMID: 36757882 DOI: 10.1148/rg.220098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
From basic research to the bedside, precise terminology is key to advancing medicine and ensuring optimal and appropriate patient care. However, the wide spectrum of diseases and their manifestations superimposed on medical team-specific and discipline-specific communication patterns often impairs shared understanding and the shared use of common medical terminology. Common terms are currently used in medicine to ensure interoperability and facilitate integration of biomedical information for clinical practice and emerging scientific and educational applications alike, from database integration to supporting basic clinical operations such as billing. Such common terminologies can be provided in ontologies, which are formalized representations of knowledge in a particular domain. Ontologies unambiguously specify common concepts and describe the relationships between those concepts by using a form that is mathematically precise and accessible to humans and machines alike. RadLex® is a key RSNA initiative that provides a shared domain model, or ontology, of radiology to facilitate integration of information in radiology education, clinical care, and research. As the contributions of the computational components of common radiologic workflows continue to increase with the ongoing development of big data, artificial intelligence, and novel image analysis and visualization tools, the use of common terminologies is becoming increasingly important for supporting seamless computational resource integration across medicine. This article introduces ontologies, outlines the fundamental semantic web technologies used to create and apply RadLex, and presents examples of RadLex applications in everyday radiology and research. It concludes with a discussion of emerging applications of RadLex, including artificial intelligence applications. © RSNA, 2023 Quiz questions for this article are available in the supplemental material.
Collapse
Affiliation(s)
- Leonid L Chepelev
- From the Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, 585 University Ave, 1-PMB 286, Toronto, ON, Canada M5G 2N2 (L.L.C.); Insygnia Consulting, Toronto, ON, Canada (D.K.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (C.E.K.); Department of Radiology, MedStar Georgetown University Hospital, Washington, DC (R.W.F.); and Imaging Service, Baltimore VA Medical Center, Baltimore, MD, and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD (K.C.W.)
| | - David Kwan
- From the Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, 585 University Ave, 1-PMB 286, Toronto, ON, Canada M5G 2N2 (L.L.C.); Insygnia Consulting, Toronto, ON, Canada (D.K.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (C.E.K.); Department of Radiology, MedStar Georgetown University Hospital, Washington, DC (R.W.F.); and Imaging Service, Baltimore VA Medical Center, Baltimore, MD, and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD (K.C.W.)
| | - Charles E Kahn
- From the Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, 585 University Ave, 1-PMB 286, Toronto, ON, Canada M5G 2N2 (L.L.C.); Insygnia Consulting, Toronto, ON, Canada (D.K.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (C.E.K.); Department of Radiology, MedStar Georgetown University Hospital, Washington, DC (R.W.F.); and Imaging Service, Baltimore VA Medical Center, Baltimore, MD, and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD (K.C.W.)
| | - Ross W Filice
- From the Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, 585 University Ave, 1-PMB 286, Toronto, ON, Canada M5G 2N2 (L.L.C.); Insygnia Consulting, Toronto, ON, Canada (D.K.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (C.E.K.); Department of Radiology, MedStar Georgetown University Hospital, Washington, DC (R.W.F.); and Imaging Service, Baltimore VA Medical Center, Baltimore, MD, and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD (K.C.W.)
| | - Kenneth C Wang
- From the Joint Department of Medical Imaging, University Health Network, University of Toronto, Toronto General Hospital, 585 University Ave, 1-PMB 286, Toronto, ON, Canada M5G 2N2 (L.L.C.); Insygnia Consulting, Toronto, ON, Canada (D.K.); Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA (C.E.K.); Department of Radiology, MedStar Georgetown University Hospital, Washington, DC (R.W.F.); and Imaging Service, Baltimore VA Medical Center, Baltimore, MD, and Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD (K.C.W.)
| |
Collapse
|
9
|
Kim TH, Noh S, Kim YR, Lee C, Kim JE, Jeong CW, Yoon KH. Development and validation of a management system and dataset quality assessment tool for the Radiology Common Data Model (R_CDM): A case study in liver disease. Int J Med Inform 2022; 162:104759. [PMID: 35390589 DOI: 10.1016/j.ijmedinf.2022.104759] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 03/17/2022] [Accepted: 03/29/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND The Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM), a distributed research network, has low clinical data coverage. Radiological data are valuable, but imaging metadata are often incomplete, and a standardized recording format in the OMOP-CDM is lacking. We developed a web-based management system and data quality assessment (RQA) tool for a radiology_CDM (R_CDM) and evaluated the feasibility of clinically applying this dataset. METHODS We designed an R_CDM with Radiology_Occurrence and Radiology_Image tables. This was seamlessly linked to the OMOP-CDM clinical data. We adopted the standardized terminology using the RadLex playbook and mapped 5,753 radiology protocol terms to the OMOP vocabulary. An extract, transform, and load (ETL) process was developed to extract detailed information that was difficult to extract from metadata and to compensate for missing values. Image-based quantification was performed to measure liver surface nodularity (LSN), using customized Wonkwang abdomen and liver total solution (WALTS) software. RESULTS On a PACS, 368,333,676 DICOM files (1,001,797 cases) were converted to R_CDM chronic liver disease (CLD) data (316,596 MR images, 228 cases; 926,753 CT images, 782 cases) and uploaded to a web-based management system. Acquisition date and resolution were extracted accurately, but other information, such as "contrast administration status" and "photography direction", could not be extracted from the metadata. Using WALTS, 9,609 pre-contrast axial-plane abdominal MR images (197 CLD cases) were assigned LSN scores by METAVIR fibrosis grades, which differed significantly by ANOVA (p < 0.001). The mean RQA score (83.5) indicated good quality. CONCLUSION This study developed a web-based system for management of the R_CDM dataset, RQA tool, and constructed a CLD R_CDM dataset, with good quality for clinical application. Our management system and R_CDM CLD dataset would be useful for multicentric and image-based quantification researches.
Collapse
Affiliation(s)
- Tae-Hoon Kim
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - SiHyeong Noh
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - Youe Ree Kim
- Department of Radiology, Wonkwang University School of Medicine and Wonkwang University Hospital, Iksan 54538, Republic of Korea
| | - ChungSub Lee
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - Ji Eon Kim
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea
| | - Chang-Won Jeong
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea.
| | - Kwon-Ha Yoon
- Medical Convergence Research Center, Wonkwang University, Iksan 54538, Republic of Korea; Department of Radiology, Wonkwang University School of Medicine and Wonkwang University Hospital, Iksan 54538, Republic of Korea.
| |
Collapse
|
10
|
Park C, You SC, Jeon H, Jeong CW, Choi JW, Park RW. Development and Validation of the Radiology Common Data Model (R-CDM) for the International Standardization of Medical Imaging Data. Yonsei Med J 2022; 63:S74-S83. [PMID: 35040608 PMCID: PMC8790584 DOI: 10.3349/ymj.2022.63.s74] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 10/28/2021] [Accepted: 10/31/2021] [Indexed: 12/02/2022] Open
Abstract
PURPOSE Digital Imaging and Communications in Medicine (DICOM), a standard file format for medical imaging data, contains metadata describing each file. However, metadata are often incomplete, and there is no standardized format for recording metadata, leading to inefficiency during the metadata-based data retrieval process. Here, we propose a novel standardization method for DICOM metadata termed the Radiology Common Data Model (R-CDM). MATERIALS AND METHODS R-CDM was designed to be compatible with Health Level Seven International (HL7)/Fast Healthcare Interoperability Resources (FHIR) and linked with the Observational Medical Outcomes Partnership (OMOP)-CDM to achieve a seamless link between clinical data and medical imaging data. The terminology system was standardized using the RadLex playbook, a comprehensive lexicon of radiology. As a proof of concept, the R-CDM conversion process was conducted with 41.7 TB of data from the Ajou University Hospital. The R-CDM database visualizer was developed to visualize the main characteristics of the R-CDM database. RESULTS Information from 2801360 cases and 87203226 DICOM files was organized into two tables constituting the R-CDM. Information on imaging device and image resolution was recorded with more than 99.9% accuracy. Furthermore, OMOP-CDM and R-CDM were linked to efficiently extract specific types of images from specific patient cohorts. CONCLUSION R-CDM standardizes the structure and terminology for recording medical imaging data to eliminate incomplete and unstandardized information. Successful standardization was achieved by the extract, transform, and load process and image classifier. We hope that the R-CDM will contribute to deep learning research in the medical imaging field by enabling the securement of large-scale medical imaging data from multinational institutions.
Collapse
Affiliation(s)
- ChulHyoung Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Seng Chan You
- Department of Preventive Medicine, Yonsei University College of Medicine, Seoul, Korea
| | - Hokyun Jeon
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
| | - Chang Won Jeong
- Medical Convergence Research Center, Wonkwang University, Iksan, Korea
| | - Jin Wook Choi
- Department of Radiology, Ajou University Medical Center, Suwon, Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Korea
- Department of Biomedical Sciences, Ajou University Graduate School of Medicine, Suwon, Korea.
| |
Collapse
|
11
|
Filice RW, Kahn CE. Biomedical Ontologies to Guide AI Development in Radiology. J Digit Imaging 2021; 34:1331-1341. [PMID: 34724143 PMCID: PMC8669056 DOI: 10.1007/s10278-021-00527-1] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 04/27/2021] [Accepted: 10/13/2021] [Indexed: 10/25/2022] Open
Abstract
The advent of deep learning has engendered renewed and rapidly growing interest in artificial intelligence (AI) in radiology to analyze images, manipulate textual reports, and plan interventions. Applications of deep learning and other AI approaches must be guided by sound medical knowledge to assure that they are developed successfully and that they address important problems in biomedical research or patient care. To date, AI has been applied to a limited number of real-world radiology applications. As AI systems become more pervasive and are applied more broadly, they will benefit from medical knowledge on a larger scale, such as that available through computer-based approaches. A key approach to represent computer-based knowledge in a particular domain is an ontology. As defined in informatics, an ontology defines a domain's terms through their relationships with other terms in the ontology. Those relationships, then, define the terms' semantics, or "meaning." Biomedical ontologies commonly define the relationships between terms and more general terms, and can express causal, part-whole, and anatomic relationships. Ontologies express knowledge in a form that is both human-readable and machine-computable. Some ontologies, such as RSNA's RadLex radiology lexicon, have been applied to applications in clinical practice and research, and may be familiar to many radiologists. This article describes how ontologies can support research and guide emerging applications of AI in radiology, including natural language processing, image-based machine learning, radiomics, and planning.
Collapse
Affiliation(s)
- Ross W Filice
- Department of Radiology, MedStar Georgetown University Hospital, Washington, DC, USA
| | - Charles E Kahn
- Department of Radiology and Institute for Biomedical Informatics, University of Pennsylvania, 3400 Spruce Street, Philadelphia, PA, 19104, USA.
| |
Collapse
|
12
|
Abstract
Artificial intelligence (AI) and informatics promise to improve the quality and efficiency of diagnostic radiology but will require substantially more standardization and operational coordination to realize and measure those improvements. As radiology steps into the AI-driven future we should work hard to identify the needs and desires of our customers and develop process controls to ensure we are meeting them. Rather than focusing on easy-to-measure turnaround times as surrogates for quality, AI and informatics can support more comprehensive quality metrics, such as ensuring that reports are accurate, readable, and useful to patients and health care providers.
Collapse
Affiliation(s)
- Thomas W Loehfelm
- UC Davis Medical Center, 4860 Y Street, Suite 3100, Sacramento, CA 95817, USA.
| |
Collapse
|
13
|
Maros ME, Cho CG, Junge AG, Kämpgen B, Saase V, Siegel F, Trinkmann F, Ganslandt T, Groden C, Wenz H. Comparative analysis of machine learning algorithms for computer-assisted reporting based on fully automated cross-lingual RadLex mappings. Sci Rep 2021; 11:5529. [PMID: 33750857 PMCID: PMC7970897 DOI: 10.1038/s41598-021-85016-9] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Accepted: 02/23/2021] [Indexed: 02/03/2023] Open
Abstract
Computer-assisted reporting (CAR) tools were suggested to improve radiology report quality by context-sensitively recommending key imaging biomarkers. However, studies evaluating machine learning (ML) algorithms on cross-lingual ontological (RadLex) mappings for developing embedded CAR algorithms are lacking. Therefore, we compared ML algorithms developed on human expert-annotated features against those developed on fully automated cross-lingual (German to English) RadLex mappings using 206 CT reports of suspected stroke. Target label was whether the Alberta Stroke Programme Early CT Score (ASPECTS) should have been provided (yes/no:154/52). We focused on probabilistic outputs of ML-algorithms including tree-based methods, elastic net, support vector machines (SVMs) and fastText (linear classifier), which were evaluated in the same 5 × fivefold nested cross-validation framework. This allowed for model stacking and classifier rankings. Performance was evaluated using calibration metrics (AUC, brier score, log loss) and -plots. Contextual ML-based assistance recommending ASPECTS was feasible. SVMs showed the highest accuracies both on human-extracted- (87%) and RadLex features (findings:82.5%; impressions:85.4%). FastText achieved the highest accuracy (89.3%) and AUC (92%) on impressions. Boosted trees fitted on findings had the best calibration profile. Our approach provides guidance for choosing ML classifiers for CAR tools in fully automated and language-agnostic fashion using bag-of-RadLex terms on limited expert-labelled training data.
Collapse
Affiliation(s)
- Máté E Maros
- Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68137, Mannheim, Germany.
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany.
| | - Chang Gyu Cho
- Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68137, Mannheim, Germany
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Andreas G Junge
- Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68137, Mannheim, Germany
| | | | - Victor Saase
- Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68137, Mannheim, Germany
| | - Fabian Siegel
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Frederik Trinkmann
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Thomas Ganslandt
- Department of Biomedical Informatics at the Center for Preventive Medicine and Digital Health (CPD-BW), Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Christoph Groden
- Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68137, Mannheim, Germany
| | - Holger Wenz
- Department of Neuroradiology, Medical Faculty Mannheim, Heidelberg University, Theodor-Kutzer-Ufer 1-3, 68137, Mannheim, Germany
| |
Collapse
|
14
|
The Importance of Body Part Labeling to Enable Enterprise Imaging: A HIMSS-SIIM Enterprise Imaging Community Collaborative White Paper. J Digit Imaging 2021; 34:1-15. [PMID: 33481143 PMCID: PMC7887098 DOI: 10.1007/s10278-020-00415-0] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/23/2020] [Indexed: 11/16/2022] Open
Abstract
In order for enterprise imaging to be successful across a multitude of specialties, systems, and sites, standards are essential to categorize and classify imaging data. The HIMSS-SIIM Enterprise Imaging Community believes that the Digital Imaging Communications in Medicine (DICOM) Anatomic Region Sequence, or its equivalent in other data standards, is a vital data element for this role, when populated with standard coded values. We believe that labeling images with standard Anatomic Region Sequence codes will enhance the user’s ability to consume data, facilitate interoperability, and allow greater control of privacy. Image consumption—when a user views a patient’s images, he or she often wants to see relevant comparison images of the same lesion or anatomic region for the same patient automatically presented. Relevant comparison images may have been acquired from a variety of modalities and specialties. The Anatomic Region Sequence data element provides a basis to allow for efficient comparison in both instances. Interoperability—as patients move between health care systems, it is important to minimize friction for data transfer. Health care providers and facilities need to be able to consume and review the increasingly large and complex volume of data efficiently. The use of Anatomic Region Sequence, or its equivalent, populated with standard values enables seamless interoperability of imaging data regardless of whether images are used within a site or across different sites and systems. Privacy—as more visible light photographs are integrated into electronic systems, it becomes apparent that some images may need to be sequestered. Although additional work is needed to protect sensitive images, standard coded values in Anatomic Region Sequence support the identification of potentially sensitive images, enable facilities to create access control policies, and can be used as an interim surrogate for more sophisticated rule-based or attribute-based access control mechanisms. To satisfy such use cases, the HIMSS-SIIM Enterprise Imaging Community encourages the use of a pre-existing body part ontology. Through this white paper, we will identify potential challenges in employing this standard and provide potential solutions for these challenges.
Collapse
|
15
|
Peng P, Beitia AO, Vreeman DJ, Loo GT, Delman BN, Thum F, Lowry T, Shapiro JS. Mapping of HIE CT terms to LOINC®: analysis of content-dependent coverage and coverage improvement through new term creation. J Am Med Inform Assoc 2019; 26:19-27. [PMID: 30445562 DOI: 10.1093/jamia/ocy135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2018] [Accepted: 09/22/2018] [Indexed: 11/12/2022] Open
Abstract
Objective We describe and evaluate the mapping of computerized tomography (CT) terms from 40 hospitals participating in a health information exchange (HIE) to a standard terminology. Methods Proprietary CT exam terms and corresponding exam frequency data were obtained from 40 participant HIE sites that transmitted radiology data to the HIE from January 2013 through October 2015. These terms were mapped to the Logical Observations Identifiers Names and Codes (LOINC®) terminology using the Regenstrief LOINC mapping assistant (RELMA) beginning in January 2016. Terms without initial LOINC match were submitted to LOINC as new term requests on an ongoing basis. After new LOINC terms were created, proprietary terms without an initial match were reviewed and mapped to these new LOINC terms where appropriate. Content type and token coverage were calculated for the LOINC version at the time of initial mapping (v2.54) and for the most recently released version at the time of our analysis (v2.63). Descriptive analysis was performed to assess for significant differences in content-dependent coverage between the 2 versions. Results LOINC's content type and token coverages of HIE CT exam terms for version 2.54 were 83% and 95%, respectively. Two-hundred-fifteen new LOINC CT terms were created in the interval between the releases of version 2.54 and 2.63, and content type and token coverages, respectively, increased to 93% and 99% (P < .001). Conclusion LOINC's content type coverage of proprietary CT terms across 40 HIE sites was 83% but improved significantly to 93% following new term creation.
Collapse
Affiliation(s)
- Paul Peng
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Anton Oscar Beitia
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Daniel J Vreeman
- Center for Biomedical Informatics, Regenstrief Institute, Inc., Indianapolis, Indiana, USA.,Department of Medicine, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - George T Loo
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Bradley N Delman
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Frederick Thum
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Tina Lowry
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Jason S Shapiro
- Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| |
Collapse
|
16
|
Chokshi FH, Flanders AE, Prevedello LM, Langlotz CP. Fostering a Healthy AI Ecosystem for Radiology: Conclusions of the 2018 RSNA Summit on AI in Radiology. Radiol Artif Intell 2019; 1:190021. [PMID: 33937789 PMCID: PMC8017423 DOI: 10.1148/ryai.2019190021] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2019] [Revised: 03/01/2019] [Accepted: 03/04/2019] [Indexed: 05/03/2023]
Abstract
The 2018 RSNA Summit on AI in Radiology brought together a diverse group of stakeholders to identify and prioritize areas of need related to artificial intelligence in radiology. This article presents the proceedings of the summit with emphasis on RSNA's role in leading, organizing, and catalyzing change during this important time in radiology. © RSNA, 2019.
Collapse
Affiliation(s)
- Falgun H. Chokshi
- From the Departments of Radiology and Imaging Sciences and Biomedical Informatics, Emory University School of Medicine, 1364 Clifton Rd NE, Atlanta, GA 30322 (F.H.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Departments of Radiology and Biomedical Informatics, Stanford University School of Medicine, Stanford, Calif (C.P.L.)
| | - Adam E. Flanders
- From the Departments of Radiology and Imaging Sciences and Biomedical Informatics, Emory University School of Medicine, 1364 Clifton Rd NE, Atlanta, GA 30322 (F.H.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Departments of Radiology and Biomedical Informatics, Stanford University School of Medicine, Stanford, Calif (C.P.L.)
| | - Luciano M. Prevedello
- From the Departments of Radiology and Imaging Sciences and Biomedical Informatics, Emory University School of Medicine, 1364 Clifton Rd NE, Atlanta, GA 30322 (F.H.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Departments of Radiology and Biomedical Informatics, Stanford University School of Medicine, Stanford, Calif (C.P.L.)
| | - Curtis P. Langlotz
- From the Departments of Radiology and Imaging Sciences and Biomedical Informatics, Emory University School of Medicine, 1364 Clifton Rd NE, Atlanta, GA 30322 (F.H.C.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (A.E.F.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); and Departments of Radiology and Biomedical Informatics, Stanford University School of Medicine, Stanford, Calif (C.P.L.)
| |
Collapse
|