Deep learning models for triaging hospital head MRI examinations

Wood, David A.; Kafiabadi, Sina; Busaidi, Ayisha Al; Guilhem, Emily; Montvila, Antanas; Lynch, Jeremy; Townend, Matthew; Agarwal, Siddharth; Mazumder, Asif; Barker, Gareth J.; Ourselin, Sébastien; Cole, James R.; Booth, Thomas C.

doi:10.1016/j.media.2022.102391

Cited by 16 publications

(23 citation statements)

References 25 publications

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“…While SNR per unit time is proportional to field strength, this may not be the best metric for determining how much value different MRI systems contribute to diagnostic accuracy, patient outcomes, and societal benefit 47 . Low‐field devices may allow patient triage and reduce scheduling demands on high‐field scanners, resulting in decreased diagnostic delays and increased patient satisfaction 48,49 …”

Section: Financial and Practical Considerationsmentioning

confidence: 99%

“…47 Low-field devices may allow patient triage and reduce scheduling demands on high-field scanners, resulting in decreased diagnostic delays and increased patient satisfaction. 48,49 While low-field devices may augment standard-of-care (SOC) imaging in HICs, they will likely play a more impactful role in LMICs. In 2016, an estimated 84 MRI units serviced West Africa, an area of over 370 million people.…”

Section: Financial and Practical Considerationsmentioning

confidence: 99%

“… 47 Low‐field devices may allow patient triage and reduce scheduling demands on high‐field scanners, resulting in decreased diagnostic delays and increased patient satisfaction. 48 , 49 …”

Section: Financial and Practical Considerationsmentioning

confidence: 99%

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Low‐field MRI: Clinical promise and challenges

Arnold

Freeman

Litt

et al. 2022

Magnetic Resonance Imaging

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Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and major population centers. Low-field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low-field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low-field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low-field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI-guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low-field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world.

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Section: Financial and Practical Considerationsmentioning

confidence: 99%

Section: Financial and Practical Considerationsmentioning

confidence: 99%

See 1 more Smart Citation

Low‐field MRI: Clinical promise and challenges

Arnold

Freeman

Litt

et al. 2022

Magnetic Resonance Imaging

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“…Titano et al 18 reported that their supervised model potentially raised the alarm 150 times faster than humans for urgent cases in brain CT scans. Wood et al 19 demonstrated that the supervised anomaly detection model significantly reduced the mean reporting time for abnormal MRI examinations from 28 days to 14 days and from 9 days to 5 days for two hospital networks. Notably, in the detailed subgroup analysis of our study, ADA implementation led to a significant reduction in the WT and TAT in immediate (more urgent) cases than in urgent cases.…”

Section: Discussionmentioning

confidence: 99%

Emergency triage of brain computed tomography via anomaly detection with a deep generative model

et al. 2022

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Triage is essential for the early diagnosis and reporting of neurologic emergencies. Herein, we report the development of an anomaly detection algorithm (ADA) with a deep generative model trained on brain computed tomography (CT) images of healthy individuals that reprioritizes radiology worklists and provides lesion attention maps for brain CT images with critical findings. In the internal and external validation datasets, the ADA achieved area under the curve values (95% confidence interval) of 0.85 (0.81–0.89) and 0.87 (0.85–0.89), respectively, for detecting emergency cases. In a clinical simulation test of an emergency cohort, the median wait time was significantly shorter post-ADA triage than pre-ADA triage by 294 s (422.5 s [interquartile range, IQR 299] to 70.5 s [IQR 168]), and the median radiology report turnaround time was significantly faster post-ADA triage than pre-ADA triage by 297.5 s (445.0 s [IQR 298] to 88.5 s [IQR 179]) (all p < 0.001).

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“…Scan interpretation workflows, on the other hand, could be supported by all algorithms in this review. In fact, some of the surveyed binary classification studies aimed explicitly to support interpretation workflows by doing worklist prioritization of critical findings [ 33 , 41 , 45 ] and, hence, ensure faster reporting times and improved patient outcomes. Multi-class classification and segmentation algorithms could extend this further by offering potential automated diagnosis reporting, biomarker quantification, and even disease progression predictions.…”

Section: Discussionmentioning

confidence: 99%

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

et al. 2022

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We conducted a systematic review of the current status of machine learning (ML) algorithms’ ability to identify multiple brain diseases, and we evaluated their applicability for improving existing scan acquisition and interpretation workflows. PubMed Medline, Ovid Embase, Scopus, Web of Science, and IEEE Xplore literature databases were searched for relevant studies published between January 2017 and February 2022. The quality of the included studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2 tool. The applicability of ML algorithms for successful workflow improvement was qualitatively assessed based on the satisfaction of three clinical requirements. A total of 19 studies were included for qualitative synthesis. The included studies performed classification tasks (n = 12) and segmentation tasks (n = 7). For classification algorithms, the area under the receiver operating characteristic curve (AUC) ranged from 0.765 to 0.997, while accuracy, sensitivity, and specificity ranged from 80% to 100%, 72% to 100%, and 65% to 100%, respectively. For segmentation algorithms, the Dice coefficient ranged from 0.300 to 0.912. No studies satisfied all clinical requirements for successful workflow improvements due to key limitations pertaining to the study’s design, study data, reference standards, and performance reporting. Standardized reporting guidelines tailored for ML in radiology, prospective study designs, and multi-site testing could help alleviate this.

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Deep learning models for triaging hospital head MRI examinations

Cited by 16 publications

References 25 publications

Low‐field MRI: Clinical promise and challenges

Low‐field MRI: Clinical promise and challenges

Emergency triage of brain computed tomography via anomaly detection with a deep generative model

Automated Identification of Multiple Findings on Brain MRI for Improving Scan Acquisition and Interpretation Workflows: A Systematic Review

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