Rationale and Objectives
Finding comparison to relevant prior studies is a requisite component of the radiology
workflow. The purpose of this study was to evaluate the impact of a deep learning
tool simplifying this time-consuming task by automatically identifying and displaying
the finding in relevant prior studies.
Materials and Methods
The algorithm pipeline used in this retrospective study, TimeLens (TL), is based on
natural language processing and descriptor-based image-matching algorithms. The dataset
used for testing comprised 3872 series of 246 radiology examinations from 75 patients
(189 CTs, 95 MRIs). To ensure a comprehensive testing, five finding types frequently
encountered in radiology practice were included: aortic aneurysm, intracranial aneurysm,
kidney lesion, meningioma, and pulmonary nodule. After a standardized training session,
nine radiologists from three university hospitals performed two reading sessions on
a cloud-based evaluation platform resembling a standard RIS/PACS. The task was to
measure the diameter of the finding-of-interest on two or more exams (a most recent
and at least one prior exam): first without use of TL, and a second session at an
interval of at least 21 days with the use of TL. All user actions were logged for
each round, including time needed to measure the finding at all timepoints, number
of mouse clicks, and mouse distance traveled. The effect of TL was evaluated in total,
per finding type, per reader, per experience (resident vs. board-certified radiologist),
and per modality. Mouse movement patterns were analyzed with heatmaps. To assess the
effect of habituation to the cases, a third round of readings was performed without
TL.
Results
Across scenarios, TL reduced the average time needed to assess a finding at all timepoints
by 40.1% (107 vs. 65 seconds; p < 0.001). Largest accelerations were demonstrated
for assessment of pulmonary nodules (−47.0%; p < 0.001). Less mouse clicks (−17.2%)
were needed for finding evaluation with TL, and mouse distance traveled was reduced
by 38.0%. Time needed to assess the findings increased from round 2 to round 3 (+27.6%;
p < 0.001). Readers were able to measure a given finding in 94.4% of cases on the
series initially proposed by TL as most relevant series for comparison. The heatmaps
showed consistently simplified mouse movement patterns with TL.
Conclusion
A deep learning tool significantly reduced both the amount of user interactions with
the radiology image viewer and the time needed to assess findings of interest on cross-sectional
imaging with relevant prior exams.
Key Words
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Article info
Publication history
Published online: May 18, 2023
Accepted:
March 29,
2023
Received in revised form:
March 28,
2023
Received:
February 8,
2023
Publication stage
In Press Corrected ProofIdentification
Copyright
© 2023 Published by Elsevier Inc. on behalf of The Association of University Radiologists
