Evidence map›Paper›PMID 33877526›Full record

ArticleInternational journal of computer assisted radiology and surgery2021

Computer-assisted contralateral side comparison of the ankle joint using flat panel technology.

Sarina Thomas, Lisa Kausch, Holger Kunze, Maxim Privalov, André Klein, Jan El Barbari, Celia Martin Vicario, Jochen Franke, Klaus Maier-Hein

Open access · hybridAbstract read
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.7field-weighted citation impact, top 35% 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

3 citing papers in PubMed, 5 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

9 authors at 3 institutions in 1 country.

Sarina ThomasDivision of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany. s.thomas@dkfz-heidelberg.de.ORCID http://orcid.org/0000-0002-1202-0856
Lisa KauschDivision of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
Holger KunzeImaging and Therapy Systems Division, Siemens Healthineers, Erlangen, Germany.
Maxim PrivalovDivision of Medical Imaging and Navigation in Trauma & Orthopaedic Surgery, BG Unfallklinik Ludwigshafen, Ludwigshafen, Germany.
André KleinDivision of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
Jan El BarbariDivision of Medical Imaging and Navigation in Trauma & Orthopaedic Surgery, BG Unfallklinik Ludwigshafen, Ludwigshafen, Germany.
Celia Martin VicarioImaging and Therapy Systems Division, Siemens Healthineers, Erlangen, Germany.
Jochen FrankeDivision of Medical Imaging and Navigation in Trauma & Orthopaedic Surgery, BG Unfallklinik Ludwigshafen, Ludwigshafen, Germany.
Klaus Maier-HeinDivision of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany.
German Cancer Research Center · DEBerufsgenossenschaftliche Unfallklinik Ludwigshafen · DESiemens Healthineers (Germany) · DE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeReduction and osteosynthesis of ankle fractures is a challenging surgical procedure when it comes to the verification of the reduction result. Evaluation is conducted using intra-operative imaging of the injured ankle and depends on the expertise of the surgeon. Studies suggest that intra-individual variance of the ankle bone shape and pose is considerably lower than the inter-individual variance. It stands to reason that the information gain from the healthy contralateral side can help to improve the evaluation.

methodIn this paper, an assistance system is proposed that provides a side-to-side view of the two ankle joints for visual comparison and instant evaluation using only one 3D C-arm image. Two convolutional neural networks (CNN) are employed to extract the relevant image regions and pose information of each ankle so that they can be aligned with each other. A first U-Net uses a sliding window to predict the location of each ankle. The standard plane estimation is formulated as segmentation problem so that a second U-Net predicts the three viewing planes for alignment.

resultsExperiments were conducted to assess the accuracy of the individual steps on 218 unilateral ankle datasets as well as the overall performance on 7 bilateral ankle datasets. The experiments on unilateral ankles yield a median position-to-plane error of [Formula: see text] mm and a median angular error between 2.98[Formula: see text] and 3.71[Formula: see text] for the plane normals.

conclusionStandard plane estimation via segmentation outperforms direct pose regression. Furthermore, the complete pipeline was evaluated including ankle detection and subsequent plane estimation on bilateral datasets. The proposed pipeline enables a direct contralateral side comparison without additional radiation. This has the potential to ease and improve the intra-operative evaluation for the surgeons in the future and reduce the need for revision surgery.

Indexed as

AlgorithmsAnkle FracturesAnkle JointFracture Fixation, InternalHumansImage Processing, Computer-AssistedImaging, Three-DimensionalIntraoperative PeriodNeural Networks, ComputerReoperationReproducibility of ResultsComputer-assisted surgeryPlane estimationSegmentation

Identifiers

PMID33877526
PMCPMC8134308
OpenAlexW3154236554

What OpenQuestion holds

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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.