Evidence map›Paper›PMID 39082924›Full record

ArticleActa obstetricia et gynecologica Scandinavica2024

The educational game SonoQz improves diagnostic performance in ultrasound assessment of ovarian tumors.

Erica Smedberg, Måns Åkerlund, Mikael Andersson Franko, Elisabeth Epstein

Abstract read
In one paragraph

Article in Acta obstetricia et gynecologica Scandinavica, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

4 authors.

Erica SmedbergDepartment of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden.ORCID 0009-0004-0634-3933
Måns ÅkerlundHarvard Extension School, Harvard University, Cambridge, Massachusetts, USA.
Mikael Andersson FrankoDepartment of Clinical Science and Education, Södersjukhuset, Karolinska Institutet, Stockholm, Sweden.
Elisabeth EpsteinDepartment of Obstetrics and Gynecology, Södersjukhuset, Stockholm, Sweden.

Funding

ALF medicine 20190202Region Stockholm Innovation Fund 20180983Region Stockholm Innovation Fund 20191145The Swedish Research Council 2020-01702
6 · The paper itself

Abstract

introductionOur objective was to determine whether the educational game SonoQz can improve diagnostic performance in ultrasound assessment of ovarian tumors. MATERIAL AND

methodsThe SonoQz mobile application was developed as an educational tool for medical doctors to practice ultrasound assessment, based on still images of ovarian tumors. The game comprises images from 324 ovarian tumors, examined by an ultrasound expert prior to surgery. A training phase, where the participants assessed at least 200 cases in the SonoQz app, was preceded by a pretraining test, and followed by a posttraining test. Two equal tests (A and B), each consisting of 20 cases, were used as pre- and posttraining tests. Half the users took test A first, B second, and the remaining took the tests in the opposite order. Users were asked to classify the tumors (1) according to International Ovarian Tumor Analysis (IOTA) Simple Rules, (2) as benign or malignant, and (3) suggest a specific histological diagnosis. Logistic mixed models with fixed effects for pre- and posttraining tests, and crossed random effects for participants and cases, were used to determine any improvement in test scores, sensitivity, and specificity.

resultsFifty-eight doctors from 19 medical centers participated. Comparing the pre- and posttraining test, the median of correctly classified cases, in Simple Rules assessment increased from 72% to 83%, p < 0.001; in classifying the lesion as benign or malignant tumors from 86% to 95%, p < 0.001; and in making a specific diagnosis from 43% to 63%, p < 0.001. When classifying tumors as benign or malignant, at an unchanged level of sensitivity (98% vs. 97%, p = 0.157), the specificity increased from 70% to 89%, p < 0.001.

conclusionsOur results indicate that the educational game SonoQz is an effective tool that may improve diagnostic performance in assessing ovarian tumors, specifically by reducing the number of false positives while maintaining high sensitivity.

Indexed as

Mobile ApplicationsOvarian NeoplasmsUltrasonographyAdultClinical CompetenceFemaleHumansSensitivity and Specificitycontinuing medical educationdiagnostic accuracydiagnostic performanceeducational gamegynecological ultrasoundovarian neoplasmovarian tumorultrasonography

Identifiers

PMID39082924
PMCPMC11426211

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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.