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Ashikyan O, Xia S, Chhabra A. Automatic Protocolling of Non-contrast Musculoskeletal MRIs Does Not Result in Increase in Patient Recall Rates for Contrast-Enhanced Studies. Acad Radiol 2024; 31:2872-2877. [PMID: 38184417 DOI: 10.1016/j.acra.2023.12.028] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2023] [Revised: 12/11/2023] [Accepted: 12/17/2023] [Indexed: 01/08/2024]
Abstract
RATIONALE AND OBJECTIVES Physicians spend large amounts of time on protocolling imaging studies, limiting their time spent on other essential clinical tasks. Most musculoskeletal (MSK) MRI studies are performed for the evaluation of joint pain and internal derangements and usually require no intravenous contrast. Contrast-enhanced MRI studies are performed for the evaluation of infection, suspected or established tumor, and rheumatological conditions. Protocolling all MSK MRI studies takes time away from other important tasks during the workday. Routine joint MRI scans have established set of sequences, and thus, could be scheduled and performed without special protocols by the radiologists. In a large tertiary care center like ours with multiple MRI magnets, we set up a process of automated protocoling and scheduling of non-contrast joint MRI scans ordered by referring doctors. This project's purpose was to assess the effect of this newly established process of 'automatic protocoling and scheduling' of MSK MRI scans on the rate of overlooked MRI exams that may have required contrast examinations, and on the patient recall-rate to obtain follow-up post-contrast sequences for further diagnostic characterization. METHODS All MSK reports of MRIs during the last two months of the years before and after the implementation of automatic protocolling (intervention) were searched for the presence of indications related to neoplasms, infections, and rheumatological conditions. For each of the three disease categories, we determined the number of MRIs obtained with and without contrast before and after the intervention. For each matching study obtained without contrast, the patient chart was reviewed for contraindications to contrast, positive final diagnosis, whether interpreting radiologist mentioned the exam being limited by lack of contrast, and recommendations for a follow-up contrast enhanced study. RESULTS A total of 846 MSK MRIs were performed prior to intervention and 822 MRIs were performed afterwards. Overall, 25% of the studies were performed without contrast prior to the intervention, and 31% of studies were performed without contrast afterwards (Chi square 0.07, p-value 0.79). No report contained a recommendation for contrast enhanced follow-up study before or after the intervention. CONCLUSION Automatic protocolling of routine MSK non-contrast MRI studies resulted in statistically insignificant, minimal increase in the overall number of non-contrast enhanced studies obtained for work up of neoplasms, infections, and rheumatological conditions. There was no increase in patient recall rate for additional post contrast sequences and the new process resulted in time savings to fellows and other physicians, being not distracted from other important tasks.
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Affiliation(s)
- Oganes Ashikyan
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, E230-C, Dallas, Texas, USA (O.A., S.X., A.C.).
| | - Shuda Xia
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, E230-C, Dallas, Texas, USA (O.A., S.X., A.C.)
| | - Avneesh Chhabra
- Department of Radiology, UT Southwestern Medical Center, 5323 Harry Hines Blvd, E230-C, Dallas, Texas, USA (O.A., S.X., A.C.); Department of Orthopedic Surgery, UT Southwestern Medical Center, Dallas, Texas, USA (A.C.)
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Knollmann FD. When the postman no longer rings. Acad Radiol 2024; 31:2878-2879. [PMID: 38704282 DOI: 10.1016/j.acra.2024.04.042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/24/2024] [Accepted: 04/24/2024] [Indexed: 05/06/2024]
Affiliation(s)
- Friedrich D Knollmann
- University of Pennsylvania, Penn Presbyterian Medical Center, Department of Medical Imaging, 39th & Market Streets, Philadelphia, PA 19104.
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Aromiwura AA, Cavalcante JL, Kwong RY, Ghazipour A, Amini A, Bax J, Raman S, Pontone G, Kalra DK. The role of artificial intelligence in cardiovascular magnetic resonance imaging. Prog Cardiovasc Dis 2024:S0033-0620(24)00092-6. [PMID: 38925255 DOI: 10.1016/j.pcad.2024.06.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/23/2024] [Accepted: 06/23/2024] [Indexed: 06/28/2024]
Abstract
Cardiovascular magnetic resonance (CMR) imaging is the gold standard test for myocardial tissue characterization and chamber volumetric and functional evaluation. However, manual CMR analysis can be time-consuming and is subject to intra- and inter-observer variability. Artificial intelligence (AI) is a field that permits automated task performance through the identification of high-level and complex data relationships. In this review, we review the rapidly growing role of AI in CMR, including image acquisition, sequence prescription, artifact detection, reconstruction, segmentation, and data reporting and analysis including quantification of volumes, function, myocardial infarction (MI) and scar detection, and prediction of outcomes. We conclude with a discussion of the emerging challenges to widespread adoption and solutions that will allow for successful, broader uptake of this powerful technology.
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Affiliation(s)
| | | | - Raymond Y Kwong
- Division of Cardiovascular Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Aryan Ghazipour
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Amir Amini
- Medical Imaging Laboratory, Department of Electrical and Computer Engineering, University of Louisville, Louisville, KY, USA
| | - Jeroen Bax
- Department of Cardiology, Leiden University, Leiden, the Netherlands
| | - Subha Raman
- Division of Cardiology, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Gianluca Pontone
- Department of Cardiology and Cardiovascular Imaging, Centro Cardiologico Monzino IRCCS, University of Milan, Milan, Italy
| | - Dinesh K Kalra
- Division of Cardiology, Department of Medicine, University of Louisville, Louisville, KY, USA; Center for Artificial Intelligence in Radiological Sciences (CAIRS), Department of Radiology, University of Louisville, Louisville, KY, USA.
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Chung R, Demers JP, Tiberio R, Savage CA, McNulty F, Stout M, Kambadakone A, Gilman MD, Sharma A, Alkasab TK. Implementation of an Institution-Wide Rules-Based Automated CT Protocoling System. AJR Am J Roentgenol 2024; 222:e2329806. [PMID: 38230904 DOI: 10.2214/ajr.23.29806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
BACKGROUND. Examination protocoling is a noninterpretive task that increases radiologists' workload and can cause workflow inefficiencies. OBJECTIVE. The purpose of this study was to evaluate effects of an automated CT protocoling system on examination process times and protocol error rates. METHODS. This retrospective study included 317,597 CT examinations (mean age, 61.8 ± 18.1 [SD] years; male, 161,125; female, 156,447; unspecified sex, 25) from July 2020 to June 2022. A rules-based automated protocoling system was implemented institution-wide; the system evaluated all CT orders in the EHR and assigned a protocol or directed the order for manual radiologist protocoling. The study period comprised pilot (July 2020 to December 2020), implementation (January 2021 to December 2021), and postimplementation (January 2022 to June 2022) phases. Proportions of automatically protocoled examinations were summarized. Process times were recorded. Protocol error rates were assessed by counts of quality improvement (QI) reports and examination recalls and comparison with retrospectively assigned protocols in 450 randomly selected examinations. RESULTS. Frequency of automatic protocoling was 19,366/70,780 (27.4%), 68,875/163,068 (42.2%), and 54,045/83,749 (64.5%) in pilot, implementation, and postimplementation phases, respectively (p < .001). Mean (± SD) times from order entry to protocol assignment for automatically and manually protocoled examinations for emergency department examinations were 0.2 ± 18.2 and 2.1 ± 69.7 hours, respectively; mean inpatient examination times were 0.5 ± 50.0 and 3.5 ± 105.5 hours; and mean outpatient examination times were 361.7 ± 1165.5 and 1289.9 ± 2050.9 hours (all p < .001). Mean (± SD) times from order entry to examination completion for automatically and manually protocoled examinations for emergency department examinations were 2.6 ± 38.6 and 4.2 ± 73.0 hours, respectively (p < .001); for inpatient examinations were 6.3 ± 74.6 and 8.7 ± 109.3 hours (p = .001); and for outpatient examinations were 1367.2 ± 1795.8 and 1471.8 ± 2118.3 hours (p < .001). In the three phases, there were three, 19, and 25 QI reports and zero, one, and three recalls, respectively, for automatically protocoled examinations, versus nine, 19, and five QI reports and one, seven, and zero recalls for manually protocoled examinations. Retrospectively assigned protocols were concordant with 212/214 (99.1%) of automatically protocoled versus 233/236 (98.7%) of manually protocoled examinations. CONCLUSION. The automated protocoling system substantially reduced radiologists' protocoling workload and decreased times from order entry to protocol assignment and examination completion; protocol errors and recalls were infrequent. CLINICAL IMPACT. The system represents a solution for reducing radiologists' time spent performing noninterpretive tasks and improving care efficiency.
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Affiliation(s)
- Ryan Chung
- Department of Radiology, Division of Abdominal Imaging, Massachusetts General Hospital, 55 Fruit St, White 270, Boston, MA 02114
| | - John P Demers
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Roberta Tiberio
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Cristy A Savage
- Department of Radiology, CT Operations, Massachusetts General Hospital, Boston, MA
| | - Frederick McNulty
- Department of Radiology, CT Operations, Massachusetts General Hospital, Boston, MA
| | - Markus Stout
- Department of Radiology, Massachusetts General Hospital, Boston, MA
| | - Avinash Kambadakone
- Department of Radiology, Division of Abdominal Imaging, Massachusetts General Hospital, 55 Fruit St, White 270, Boston, MA 02114
| | - Matthew D Gilman
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
| | - Amita Sharma
- Department of Radiology, Division of Thoracic Imaging and Intervention, Massachusetts General Hospital, Boston, MA
| | - Tarik K Alkasab
- Department of Radiology, Division of Emergency Imaging, Massachusetts General Hospital, Boston, MA
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Posselt C, Avci MY, Yigitsoy M, Schuenke P, Kolbitsch C, Schaeffter T, Remmele S. Simulation of acquisition shifts in T2 weighted fluid-attenuated inversion recovery magnetic resonance images to stress test artificial intelligence segmentation networks. J Med Imaging (Bellingham) 2024; 11:024013. [PMID: 38666039 PMCID: PMC11042016 DOI: 10.1117/1.jmi.11.2.024013] [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: 08/10/2023] [Revised: 03/01/2024] [Accepted: 03/29/2024] [Indexed: 04/28/2024] Open
Abstract
Purpose To provide a simulation framework for routine neuroimaging test data, which allows for "stress testing" of deep segmentation networks against acquisition shifts that commonly occur in clinical practice for T2 weighted (T2w) fluid-attenuated inversion recovery magnetic resonance imaging protocols. Approach The approach simulates "acquisition shift derivatives" of MR images based on MR signal equations. Experiments comprise the validation of the simulated images by real MR scans and example stress tests on state-of-the-art multiple sclerosis lesion segmentation networks to explore a generic model function to describe the F1 score in dependence of the contrast-affecting sequence parameters echo time (TE) and inversion time (TI). Results The differences between real and simulated images range up to 19% in gray and white matter for extreme parameter settings. For the segmentation networks under test, the F1 score dependency on TE and TI can be well described by quadratic model functions (R 2 > 0.9 ). The coefficients of the model functions indicate that changes of TE have more influence on the model performance than TI. Conclusions We show that these deviations are in the range of values as may be caused by erroneous or individual differences in relaxation times as described by literature. The coefficients of the F1 model function allow for a quantitative comparison of the influences of TE and TI. Limitations arise mainly from tissues with a low baseline signal (like cerebrospinal fluid) and when the protocol contains contrast-affecting measures that cannot be modeled due to missing information in the DICOM header.
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Affiliation(s)
- Christiane Posselt
- University of Applied Sciences, Faculty of Electrical and Industrial Engineering, Landshut, Germany
| | | | | | - Patrick Schuenke
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Christoph Kolbitsch
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
| | - Tobias Schaeffter
- Physikalisch‐Technische Bundesanstalt (PTB), Braunschweig and Berlin, Germany
- Technical University of Berlin, Department of Medical Engineering, Berlin, Germany
| | - Stefanie Remmele
- University of Applied Sciences, Faculty of Electrical and Industrial Engineering, Landshut, Germany
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