Evidence map›Paper›PMID 32754893›Full record

ArticleEJNMMI physics2020

RECOMIA-a cloud-based platform for artificial intelligence research in nuclear medicine and radiology.

Elin Trägårdh, Pablo Borrelli, Reza Kaboteh, Tony Gillberg, Johannes Ulén, Olof Enqvist, Lars Edenbrandt

Abstract read
In one paragraph

Article in EJNMMI physics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 53 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
53citing 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

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

  1. Pooled it
  2. Trial
  3. Application of an artificial intelligence-based tool in [European journal of nuclear medicine and molecular imaging · 2023
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  9. coPERCIST: AI-assisted PET-CT response assessment.European journal of nuclear medicine and molecular imaging · 2026
    Article
  10. The Prognostic Value of Biomarkers Identified by [Diagnostics (Basel, Switzerland) · 2025
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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.

Elin TrägårdhDepartment of Clinical Physiology and Nuclear Medicine, Skåne University Hospital, Carl Bertil Laurells gata 9, 205 02, Malmö, Sweden. elin.tragardh@med.lu.se.ORCID http://orcid.org/0000-0002-7116-303X
Pablo BorrelliDepartment of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Reza KabotehDepartment of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.
Tony GillbergRECOMIA, Malmö, Sweden.
Johannes UlénEigenvision AB, Malmö, Sweden.
Olof EnqvistEigenvision AB, Malmö, Sweden.
Lars EdenbrandtDepartment of Clinical Physiology, Sahlgrenska University Hospital, Gothenburg, Sweden.

Funding

Knut och Alice Wallenbergs Stiftelse -Medicinska Fakulteten, Lunds Universitet -Region Skåne -Sahlgrenska Akademin ALFGBG-720751
6 · The paper itself

Abstract

backgroundArtificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image.

resultsThe RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones).

conclusionThe paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at www.recomia.org for research purposes.

Indexed as

Artificial intelligenceCNNDeep learningPET-CTSegmentation

Identifiers

PMID32754893
PMCPMC7403290

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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.