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Improved Productivity Using Deep Learning-assisted Reporting for Lumbar Spine MRI.

Desmond Shi Wei Lim1, Andrew Makmur1, Lei Zhu1

  • 1From the Department of Diagnostic Imaging, National University Hospital, 5 Lower Kent Ridge Rd, Singapore 119074 (D.S.W.L., A.M., A.J.L.C., D.S.Y.S., S.E.E., H.Y.O., P.J., W.C.T., V.M.K., Y.M.W., Y.L.T., S.B., E.C.T., S.T.Q., J.T.P.D.H.); Department of Diagnostic Radiology (A.M., S.E.E., P.J., Y.L.T., S.T.Q., J.T.P.D.H.), NUS Graduate School, Integrative Sciences and Engineering Programme (L.Z.), Department of Computer Science, School of Computing (W.Z., B.C.O.), and Biostatistics Unit, Yong Loo Lin School of Medicine (Q.V.Y., Y.H.C.), National University of Singapore, Singapore; Department of Radiology, Qatif Central Hospital, Qatif, Saudi Arabia (D.A.R.A.); Department of Orthopaedic Surgery, National University Health System, Singapore (J.H.T., N.K.); and Department of Radiological Sciences, University of California, Irvine, Orange, Calif (H.Y.).

Radiology
|June 14, 2022
PubMed
Summary

Deep learning (DL) significantly speeds up lumbar spinal stenosis interpretation on MRI scans, reducing radiologist reporting time. DL assistance also improves interobserver agreement, leading to more consistent and reliable diagnoses for back pain assessment.

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Spinal Diagnostics

Background:

  • Lumbar spine magnetic resonance imaging (MRI) is crucial for diagnosing back pain.
  • Interpreting lumbar spinal stenosis is time-consuming and prone to variability.
  • Deep learning (DL) offers potential for enhanced efficiency and consistency in radiological interpretation.

Purpose of the Study:

  • To evaluate the impact of deep learning (DL) assistance on radiologist interpretation speed for lumbar spinal stenosis.
  • To assess the effect of DL assistance on interobserver agreement among radiologists reporting lumbar spinal stenosis.
  • To compare diagnostic performance with and without DL support against a reference standard.

Main Methods:

  • Retrospective analysis of lumbar spine MRI studies from patients with back pain.
  • Eight radiologists interpreted studies with and without DL model assistance for spinal canal, lateral recess, and neural foraminal stenosis.
  • Measured interpretation time and interobserver agreement (Gwet κ), comparing results to an expert reference standard.

Main Results:

  • DL-assisted interpretation significantly reduced mean reporting time per MRI study (124-274 seconds to 47-71 seconds, P < .001).
  • DL assistance resulted in superior or equivalent interobserver agreement across all stenosis gradings.
  • Notable improvement in interobserver agreement for neural foraminal stenosis with DL assistance (κ=0.71 vs. 0.39, P < .001).

Conclusions:

  • Deep learning assistance markedly decreases reporting time for lumbar spinal stenosis on MRI.
  • DL tools enhance interobserver agreement, improving diagnostic consistency among radiologists.
  • DL-assisted interpretation represents a significant advancement for efficient and reliable spinal stenosis assessment.