Psychophysical Evaluation of Visual vs. Computer-Aided Detection of Brain Lesions on Magnetic Resonance Images

Chen Solomon, Omer Shmueli, Shai Shrot, Tamar Blumenfeld-Katzir, Dvir Radunsky, Noam Omer, Neta Stern, Dominique Ben Ami Reichman, Chen Hoffmann, Moti Salti, Hayit Greenspan, Noam Ben-Eliezer*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Magnetic resonance imaging (MRI) diagnosis is usually performed by analyzing contrast-weighted images, where pathology is detected once it reached a certain visual threshold. Computer-aided diagnosis (CAD) has been proposed as a way for achieving higher sensitivity to early pathology. Purpose: To compare conventional (i.e., visual) MRI assessment of artificially generated multiple sclerosis (MS) lesions in the brain's white matter to CAD based on a deep neural network. Study Type: Prospective. Population: A total of 25 neuroradiologists (15 males, age 39 ± 9, 9 ± 9.8 years of experience) independently assessed all synthetic lesions. Field Strength/Sequence: A 3.0 T, T2-weighted multi-echo spin-echo (MESE) sequence. Assessment: MS lesions of varying severity levels were artificially generated in healthy volunteer MRI scans by manipulating T2 values. Radiologists and a neural network were tasked with detecting these lesions in a series of 48 MR images. Sixteen images presented healthy anatomy and the rest contained a single lesion at eight increasing severity levels (6%, 9%, 12%, 15%, 18%, 21%, 25%, and 30% elevation in T2). True positive (TP) rates, false positive (FP) rates, and odds ratios (ORs) were compared between radiological diagnosis and CAD across the range lesion severity levels. Statistical Tests: Diagnostic performance of the two approaches was compared using z-tests on TP rates, FP rates, and the logarithm of ORs across severity levels. A P-value <0.05 was considered statistically significant. Results: ORs of identifying pathology were significantly higher for CAD vis-à-vis visual inspection for all lesions' severity levels. For a 6% change in T2 value (lowest severity), radiologists' TP and FP rates were not significantly different (P = 0.12), while the corresponding CAD results remained statistically significant. Data Conclusion: CAD is capable of detecting the presence or absence of more subtle lesions with greater precision than the representative group of 25 radiologists chosen in this study. Level of Evidence: 1. Technical Efficacy: Stage 3.

Original languageEnglish
Pages (from-to)642-649
Number of pages8
JournalJournal of Magnetic Resonance Imaging
Volume58
Issue number2
DOIs
StatePublished - Aug 2023

Funding

FundersFunder number
Israel Science Foundation2009/17

    Keywords

    • computer-aided diagnosis
    • deep learning
    • multiple sclerosis
    • psychophysics

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