Evidence map›Paper›PMID 38265684›Full record

ArticleNeuroradiology2024

Evaluation of the quality and the productivity of neuroradiological reading of multiple sclerosis follow-up MRI scans using an intelligent automation software.

Christian Federau, Nicolin Hainc, Myriam Edjlali, Guangming Zhu, Milica Mastilovic, Nathalie Nierobisch, Jan-Philipp Uhlemann, Silvio Paganucci, Cristina Granziera, Olivier Heinzlef and 2 more

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Article in Neuroradiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Christian FederauAI Medical AG, Goldhaldenstr 22a, 8702, Zollikon, Switzerland. christian@ai-medical.ch.ORCID http://orcid.org/0000-0002-3803-6602
Nicolin HaincUniversity of Zürich, Zürich, Switzerland.
Myriam EdjlaliDepartment of Radiology, APHP, Hôpitaux Raymond-Poincaré & Ambroise Paré, Paris, France.
Guangming ZhuStanford University, Stanford, USA.
Milica MastilovicDepartment of Radiology, APHP, Hôpitaux Raymond-Poincaré & Ambroise Paré, Paris, France.
Nathalie NierobischUniversity of Zürich, Zürich, Switzerland.
Jan-Philipp UhlemannUniversity of Zürich, Zürich, Switzerland.
Silvio PaganucciAI Medical AG, Goldhaldenstr 22a, 8702, Zollikon, Switzerland.
Cristina GranzieraUniversity Hospital Basel and University of Basel, Basel, Switzerland.
Olivier HeinzlefDepartment of Neurology, Poissy-Saint-Germain-en-Laye Hospital, Poissy, France.
Lucas B KippDepartment of Neurology & Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA.
Max WintermarkStanford University, Stanford, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThe assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesions.

methodsIn three separate centers, 117 MS follow-up MRIs were blindly analyzed on fluid attenuated inversion recovery (FLAIR), pre- and post-gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.

resultsA total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33s ± 1min0s per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa = 0.5) and slight for SELs (0.08).

conclusionThe quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using the dedicated software Jazz.

Indexed as

Multiple SclerosisBrainFollow-Up StudiesGadoliniumHumansMagnetic Resonance ImagingGadoliniumArtificial intelligenceIntelligent automationMachine learningMultiple sclerosis

Identifiers

PMID38265684
PMCPMC10859335

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.