Evidence map›Paper›PMID 40276937›Full record

Observational studyJournal of clinical ultrasound : JCU2025

Artificial Intelligence to Determine Correct Midsagittal Plane in Dynamic Transperineal Ultrasound.

José Antonio García-Mejido, Juan Galán-Paez, David Solis-Martín, Marina Martín-Morán, Carlota Borrero-Gonzalez, Alfonso Fernández-Gomez, Fernando Fernández-Palacín, José Antonio Sainz-Bueno

Abstract readObservational Study
In one paragraph

Observational study in Journal of clinical ultrasound : JCU, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Article
  2. Observational
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

8 authors.

José Antonio García-MejidoDepartment of Surgery, Faculty of Medicine, University of Seville, Seville, Spain.ORCID https://orcid.org/0000-0002-3079-5583
Juan Galán-PaezDepartment of Computer Science and Artificial Intelligence, Faculty of Mathematics, University of Seville, Seville, Spain.ORCID https://orcid.org/0000-0002-6622-271X
David Solis-MartínDepartment of Computer Science and Artificial Intelligence, Faculty of Mathematics, University of Seville, Seville, Spain.ORCID https://orcid.org/0000-0002-4625-4554
Marina Martín-MoránDepartment of Surgery, Faculty of Medicine, University of Seville, Seville, Spain.
Carlota Borrero-GonzalezDepartment of Surgery, Faculty of Medicine, University of Seville, Seville, Spain.
Alfonso Fernández-GomezDepartment of Surgery, Faculty of Medicine, University of Seville, Seville, Spain.
Fernando Fernández-PalacínDepartment of Statistics and Operational Research, University of Cadiz, Cadiz, Spain.ORCID https://orcid.org/0000-0002-3525-2924
José Antonio Sainz-BuenoDepartment of Surgery, Faculty of Medicine, University of Seville, Seville, Spain.ORCID https://orcid.org/0000-0002-9571-3219

Funding

FEDERMICIU/AEI/10.13 039/501100011033 (Agencia Estatal de Investigación) PID2023-147198NB-I00
6 · The paper itself

Abstract

purposeTo create and validate a machine learning(ML) model that allows for identifying the correct capture of the midsagittal plane in a dynamic ultrasound study, as well as establishing its concordance with a senior explorer and a junior explorer.

methodsObservational and prospective study with 90 patients without pelvic floor pathology. Each patient was given an ultrasound video where the midsagittal plane of the pelvic floor was recorded at rest and during the Valsalva maneuver. A segmentation model was used that was trained on a previously published article, generating the segmentations of the 90 new videos to create the model. The algorithm selected to build the model in this project was XGBoost(Gradient Boosting). To obtain a tabular dataset on which to train the model, feature engineering was carried out on the raw segmentation data. The concordance of the model, of a junior examiner and a senior examiner, with the expert examiner was studied using the kappa index.

resultsThe first 60 videos were used to train the model and the last 30 videos were reserved for the test set. The model presented a kappa index 0.930(p < 0.001) with very good agreement for detection of the correct midsagittal plane. The junior explorer presented a very good agreement (kappa index = 0.930(p < 0.001)). The senior explorer presented a kappa index 0.789(p < 0.001) (good agreement) for detection of the correct midsagittal plane.

conclusionWe have developed a model that allows determining the correct midsagittal plane captured through dynamic transperineal ultrasound with a level of agreement comparable to or greater than that of a junior or senior examiner, using expert examiner assessment as the gold standard.

Indexed as

Artificial IntelligenceMachine LearningPelvic FloorAdultAgedFemaleHumansMaleMiddle AgedPerineumProspective StudiesReproducibility of ResultsUltrasonographyValsalva ManeuverYoung Adultartificial intelligencegradient boostinglevator ani musclemachine learningpelvic floorultrasonographyXGBoost

Identifiers

PMID40276937
PMCPMC12413558

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.