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  1. Article ; Online: Contrast-enhanced to non-contrast-enhanced image translation to exploit a clinical data warehouse of T1-weighted brain MRI.

    Bottani, Simona / Thibeau-Sutre, Elina / Maire, Aurélien / Ströer, Sebastian / Dormont, Didier / Colliot, Olivier / Burgos, Ninon

    BMC medical imaging

    2024  Volume 24, Issue 1, Page(s) 67

    Abstract: Background: Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. ... ...

    Abstract Background: Clinical data warehouses provide access to massive amounts of medical images, but these images are often heterogeneous. They can for instance include images acquired both with or without the injection of a gadolinium-based contrast agent. Harmonizing such data sets is thus fundamental to guarantee unbiased results, for example when performing differential diagnosis. Furthermore, classical neuroimaging software tools for feature extraction are typically applied only to images without gadolinium. The objective of this work is to evaluate how image translation can be useful to exploit a highly heterogeneous data set containing both contrast-enhanced and non-contrast-enhanced images from a clinical data warehouse.
    Methods: We propose and compare different 3D U-Net and conditional GAN models to convert contrast-enhanced T1-weighted (T1ce) into non-contrast-enhanced (T1nce) brain MRI. These models were trained using 230 image pairs and tested on 77 image pairs from the clinical data warehouse of the Greater Paris area.
    Results: Validation using standard image similarity measures demonstrated that the similarity between real and synthetic T1nce images was higher than between real T1nce and T1ce images for all the models compared. The best performing models were further validated on a segmentation task. We showed that tissue volumes extracted from synthetic T1nce images were closer to those of real T1nce images than volumes extracted from T1ce images.
    Conclusion: We showed that deep learning models initially developed with research quality data could synthesize T1nce from T1ce images of clinical quality and that reliable features could be extracted from the synthetic images, thus demonstrating the ability of such methods to help exploit a data set coming from a clinical data warehouse.
    MeSH term(s) Humans ; Data Warehousing ; Gadolinium ; Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Neuroimaging/methods ; Image Processing, Computer-Assisted/methods
    Chemical Substances Gadolinium (AU0V1LM3JT)
    Language English
    Publishing date 2024-03-20
    Publishing country England
    Document type Journal Article
    ZDB-ID 2061975-3
    ISSN 1471-2342 ; 1471-2342
    ISSN (online) 1471-2342
    ISSN 1471-2342
    DOI 10.1186/s12880-024-01242-3
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Article ; Online: Evaluation of MRI-based machine learning approaches for computer-aided diagnosis of dementia in a clinical data warehouse.

    Bottani, Simona / Burgos, Ninon / Maire, Aurélien / Saracino, Dario / Ströer, Sebastian / Dormont, Didier / Colliot, Olivier

    Medical image analysis

    2023  Volume 89, Page(s) 102903

    Abstract: A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatomical brain MRI. These approaches achieve high accuracy when applied to research data sets but their performance on real-life clinical routine data has not been ...

    Abstract A variety of algorithms have been proposed for computer-aided diagnosis of dementia from anatomical brain MRI. These approaches achieve high accuracy when applied to research data sets but their performance on real-life clinical routine data has not been evaluated yet. The aim of this work was to study the performance of such approaches on clinical routine data, based on a hospital data warehouse, and to compare the results to those obtained on a research data set. The clinical data set was extracted from the hospital data warehouse of the Greater Paris area, which includes 39 different hospitals. The research set was composed of data from the Alzheimer's Disease Neuroimaging Initiative data set. In the clinical set, the population of interest was identified by exploiting the diagnostic codes from the 10th revision of the International Classification of Diseases that are assigned to each patient. We studied how the imbalance of the training sets, in terms of contrast agent injection and image quality, may bias the results. We demonstrated that computer-aided diagnosis performance was strongly biased upwards (over 17 percent points of balanced accuracy) by the confounders of image quality and contrast agent injection, a phenomenon known as the Clever Hans effect or shortcut learning. When these biases were removed, the performance was very poor. In any case, the performance was considerably lower than on the research data set. Our study highlights that there are still considerable challenges for translating dementia computer-aided diagnosis systems to clinical routine.
    MeSH term(s) Humans ; Contrast Media ; Data Warehousing ; Brain/diagnostic imaging ; Magnetic Resonance Imaging/methods ; Alzheimer Disease/diagnostic imaging ; Machine Learning ; Computers
    Chemical Substances Contrast Media
    Language English
    Publishing date 2023-07-17
    Publishing country Netherlands
    Document type Journal Article ; Research Support, U.S. Gov't, Non-P.H.S. ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2023.102903
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 5206: Show issues Location:
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    Jg. 1995 - 2021: Lesesall (2.OG)
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  3. Article ; Online: Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data.

    Loizillon, Sophie / Bottani, Simona / Maire, Aurélien / Ströer, Sebastian / Dormont, Didier / Colliot, Olivier / Burgos, Ninon

    Medical image analysis

    2023  Volume 93, Page(s) 103073

    Abstract: Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, ... ...

    Abstract Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable. Since their manual detection is impossible due to the large number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method from research to clinical data for the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80 %). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on research data.
    MeSH term(s) Humans ; Artifacts ; Data Warehousing ; Motion ; Brain/diagnostic imaging ; Magnetic Resonance Imaging
    Language English
    Publishing date 2023-12-23
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2023.103073
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  4. Article ; Online: Automatic quality control of brain T1-weighted magnetic resonance images for a clinical data warehouse.

    Bottani, Simona / Burgos, Ninon / Maire, Aurélien / Wild, Adam / Ströer, Sebastian / Dormont, Didier / Colliot, Olivier

    Medical image analysis

    2021  Volume 75, Page(s) 102219

    Abstract: Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation ...

    Abstract Many studies on machine learning (ML) for computer-aided diagnosis have so far been mostly restricted to high-quality research data. Clinical data warehouses, gathering routine examinations from hospitals, offer great promises for training and validation of ML models in a realistic setting. However, the use of such clinical data warehouses requires quality control (QC) tools. Visual QC by experts is time-consuming and does not scale to large datasets. In this paper, we propose a convolutional neural network (CNN) for the automatic QC of 3D T1-weighted brain MRI for a large heterogeneous clinical data warehouse. To that purpose, we used the data warehouse of the hospitals of the Greater Paris area (Assistance Publique-Hôpitaux de Paris [AP-HP]). Specifically, the objectives were: 1) to identify images which are not proper T1-weighted brain MRIs; 2) to identify acquisitions for which gadolinium was injected; 3) to rate the overall image quality. We used 5000 images for training and validation and a separate set of 500 images for testing. In order to train/validate the CNN, the data were annotated by two trained raters according to a visual QC protocol that we specifically designed for application in the setting of a data warehouse. For objectives 1 and 2, our approach achieved excellent accuracy (balanced accuracy and F1-score >90%), similar to the human raters. For objective 3, the performance was good but substantially lower than that of human raters. Nevertheless, the automatic approach accurately identified (balanced accuracy and F1-score >80%) low quality images, which would typically need to be excluded. Overall, our approach shall be useful for exploiting hospital data warehouses in medical image computing.
    MeSH term(s) Brain/diagnostic imaging ; Data Warehousing ; Humans ; Magnetic Resonance Imaging ; Neural Networks, Computer ; Quality Control
    Language English
    Publishing date 2021-09-03
    Publishing country Netherlands
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1356436-5
    ISSN 1361-8423 ; 1361-8431 ; 1361-8415
    ISSN (online) 1361-8423 ; 1361-8431
    ISSN 1361-8415
    DOI 10.1016/j.media.2021.102219
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

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  5. Article ; Online: Fully automated opportunistic screening of vertebral fractures and osteoporosis on more than 150 000 routine computed tomography scans.

    Roux, Christian / Rozes, Antoine / Reizine, Daniel / Hajage, David / Daniel, Christel / Maire, Aurélien / Bréant, Stéphane / Taright, Namik / Gordon, Ronen / Fechtenbaum, Jacques / Kolta, Sami / Feydy, Antoine / Briot, Karine / Tubach, Florence

    Rheumatology (Oxford, England)

    2021  Volume 61, Issue 8, Page(s) 3269–3278

    Abstract: Objective: Osteoporosis is underdiagnosed and undertreated, although severe complications of osteoporotic fractures, including vertebral fractures, are well known. This study sought to assess the feasibility and results of an opportunistic screening of ... ...

    Abstract Objective: Osteoporosis is underdiagnosed and undertreated, although severe complications of osteoporotic fractures, including vertebral fractures, are well known. This study sought to assess the feasibility and results of an opportunistic screening of vertebral fractures and osteoporosis in a large database of lumbar or abdominal CT scans.
    Material and methods: Data were analysed from CT scans obtained in 35 hospitals from patients aged 60 years or older and stored in a Picture Archiving and Communication System in Assistance-Publique-Hôpitaux de Paris, from 2007 to 2013. Dedicated software was used to analyse the presence or absence of at least 1 vertebral fracture (VF), and the radiodensity of the lumbar vertebrae was measured Hounsfield Units (HUs). A simulated T-score was calculated.
    Results: Data were analysed from 152 268 patients [mean age (S.D.) = 73.2 (9.07) years]. Success rates for VF assessment and HUs measurements were 82 and 87%, respectively. The prevalence of VFs was 24.5% and increased with age. Areas under the receiver operating characteristic curves for the detection of VFs were 0.61 and 0.62 for the mean HUs of the lumbar vertebrae and the L1 HUs, respectively. In patients without VFs, HUs decreased with age, similarly in males and females. The prevalence of osteoporosis (sT-score ≤ -2.5) was 23.8% and 36.5% in patients without and with VFs, respectively.
    Conclusion: It is feasible on a large scale to screen for VFs and osteoporosis during opportunistic screening in patients 60 years or older having lumbar or abdominal CT.
    MeSH term(s) Absorptiometry, Photon/methods ; Aged ; Bone Density ; Female ; Humans ; Lumbar Vertebrae/diagnostic imaging ; Male ; Osteoporosis/complications ; Osteoporosis/diagnostic imaging ; Osteoporosis/epidemiology ; Osteoporotic Fractures/diagnostic imaging ; Osteoporotic Fractures/epidemiology ; Osteoporotic Fractures/etiology ; Spinal Fractures/diagnostic imaging ; Spinal Fractures/epidemiology ; Spinal Fractures/etiology ; Tomography, X-Ray Computed/methods
    Language English
    Publishing date 2021-12-01
    Publishing country England
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1464822-2
    ISSN 1462-0332 ; 1462-0324
    ISSN (online) 1462-0332
    ISSN 1462-0324
    DOI 10.1093/rheumatology/keab878
    Database MEDical Literature Analysis and Retrieval System OnLINE

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