Evidence map›Paper›PMID 37510967›Full record

ArticleJournal of clinical medicine2023

Performance of Fully Automated Algorithm Detecting Bone Marrow Edema in Sacroiliac Joints.

Joanna Ożga, Michał Wyka, Agata Raczko, Zbisław Tabor, Zuzanna Oleniacz, Michał Korman, Wadim Wojciechowski

Open access · goldAbstract read
In one paragraph

Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
2.7field-weighted citation impact, top 9% of its field
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

7 citing papers in PubMed, 11 citations in OpenAlex.

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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

7 authors at 2 institutions in 1 country.

Joanna OżgaDepartment of Radiology, Jagiellonian University Medical College, ul. Botaniczna 3, 31-503 Krakow, Poland.ORCID 0000-0002-2675-6848
Michał WykaDepartment of Radiology, Jagiellonian University Medical College, ul. Botaniczna 3, 31-503 Krakow, Poland.ORCID 0009-0001-9561-4640
Agata RaczkoDepartment of Radiology, Jagiellonian University Medical College, ul. Botaniczna 3, 31-503 Krakow, Poland.
Zbisław TaborDepartment of Biocybernetics and Biomedical Engineering, AGH University of Science and Technology, al. Adama Mickiewicza 30, 30-059 Krakow, Poland.
Zuzanna OleniaczDepartment of Radiology, Jagiellonian University Medical College, ul. Botaniczna 3, 31-503 Krakow, Poland.ORCID 0009-0007-6199-4802
Michał KormanDepartment of Radiology, Jagiellonian University Medical College, ul. Botaniczna 3, 31-503 Krakow, Poland.ORCID 0000-0003-1511-0774
Wadim WojciechowskiDepartment of Radiology, Jagiellonian University Medical College, ul. Botaniczna 3, 31-503 Krakow, Poland.ORCID 0000-0002-0748-3995
Jagiellonian University · PLAGH University of Krakow · PL

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study evaluates the performance of a fully automated algorithm to detect active inflammation in the form of bone marrow edema (BME) in iliac and sacral bones, depending on the quality of the coronal oblique plane in patients with axial spondyloarthritis (axSpA). The results were assessed based on the technical correctness of MRI examination of the sacroiliac joints (SIJs). A total of 173 patients with suspected axSpA were included in the study. In order to verify the correctness of the MRI, a deviation angle was measured on the slice acquired in the sagittal plane in the T2-weighted sequence. This angle was located between the line drawn between the posterior edges of S1 and S2 vertebrae and the line that marks the actual plane in which the slices were acquired in T1 and STIR sequences. All examinations were divided into quartiles according to the deviation angle measured in degrees as follows: 1st group [0; 2.2], 2nd group (2.2; 5.7], 3rd group (5.7; 10] and 4th group (10; 29.2]. Segmentations of the sacral and iliac bones were acquired manually and automatically using the fully automated algorithm on the T1 sequence. The Dice coefficient for automated bone segmentations with respect to reference manual segmentations was 0.9820 (95% CI [0.9804, 0.9835]). Examinations of BME lesions were assessed using the SPARCC scale (in 68 cases SPARCC > 0). Manual and automatic segmentations of the lesions were performed on STIR sequences and compared. The sensitivity of detection of BME ranged from 0.58 (group 1) to 0.83 (group 2) versus 0.76 (total), while the specificity was equal to 0.97 in each group. The study indicates that the performance of the algorithm is satisfactory regardless of the deviation angle.

Indexed as

axial spondyloarthritiscoronal oblique planedeep learningfully automated algorithmsacroiliac joint

Identifiers

PMID37510967
PMCPMC10381124
OpenAlexW4385245019

What OpenQuestion holds

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Registered trials

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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.