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  • 1.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för hälsa.
    Sanmartin Berglund, Johan
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för hälsa.
    Brogren, Martin
    Optriva AB, SWE.
    Kvist, Ola
    Karolinska, SWE.
    Ruiz, Sandra Diaz
    Karolinska, SWE.
    Dübbel, André
    Optriva AB, SWE.
    Anderberg, Peter
    Blekinge Tekniska Högskola, Fakulteten för teknikvetenskaper, Institutionen för hälsa.
    Age assessment of youth and young adults using magnetic resonance imaging of the knee: A deep learning approach2019Ingår i: JMIR MEDICAL INFORMATICS, E-ISSN 2291-9694, Vol. 7, nr 4, s. 419-436, artikel-id e16291Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Background: Bone age assessment (BAA) is an important tool for diagnosis and in determining the time of treatment in a number of pediatric clinical scenarios, as well as in legal settings where it is used to estimate the chronological age of an individual where valid documents are lacking. Traditional methods for BAA suffer from drawbacks, such as exposing juveniles to radiation, intra- and interrater variability, and the time spent on the assessment. The employment of automated methods such as deep learning and the use of magnetic resonance imaging (MRI) can address these drawbacks and improve the assessment of age. Objective: The aim of this paper is to propose an automated approach for age assessment of youth and young adults in the age range when the length growth ceases and growth zones are closed (14-21 years of age) by employing deep learning using MRI of the knee. Methods: This study carried out MRI examinations of the knee of 402 volunteer subjects-221 males (55.0%) and 181 (45.0%) females-aged 14-21 years. The method comprised two convolutional neural network (CNN) models: the first one selected the most informative images of an MRI sequence, concerning age-assessment purposes; these were then used in the second module, which was responsible for the age estimation. Different CNN architectures were tested, both training from scratch and employing transfer learning. Results: The CNN architecture that provided the best results was GoogLeNet pretrained on the ImageNet database. The proposed method was able to assess the age of male subjects in the range of 14-20.5 years, with a mean absolute error (MAE) of 0.793 years, and of female subjects in the range of 14-19.5 years, with an MAE of 0.988 years. Regarding the classification of minors-with the threshold of 18 years of age-an accuracy of 98.1% for male subjects and 95.0% for female subjects was achieved. Conclusions: The proposed method was able to assess the age of youth and young adults from 14 to 20.5 years of age for male subjects and 14 to 19.5 years of age for female subjects in a fully automated manner, without the use of ionizing radiation, addressing the drawbacks of traditional methods. © 2019 Journal of Medical Internet Research. All rights reserved.

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    Age assessment of youth and young adults using magnetic resonance imaging of the knee
  • 2.
    Ola, Spjuth
    et al.
    Karolinska Institutet, SWE.
    Andreas, Karlsson
    Karolinska Institutet, SWE.
    Mark, Clements
    Karolinska Institutet, SWE.
    Keith, Humphreys
    Karolinska Institutet, SWE.
    Emma, Ivansson
    Karolinska Institutet, SWE.
    Jim, Dowling
    Royal Institute of Technology, SWE.
    Martin, Eklund
    Karolinska Institutet, SWE.
    Alexandra, Jauhiainen
    AstraZeneca AB R&D, SWE.
    Kamila, Czene
    Karolinska Institutet, SWE.
    Henrik, Grönberg
    Karolinska Institutet, SWE.
    Pär, Sparén
    Karolinska Institutet, SWE.
    Fredrik, Wiklund
    Karolinska Institutet, SWE.
    Abbas, Cheddad
    Blekinge Tekniska Högskola, Fakulteten för datavetenskaper, Institutionen för datalogi och datorsystemteknik.
    þorgerður, Pálsdóttir
    Nordic Information for Action e-Science Center, SWE.
    Mattias, Rantalainen
    Karolinska Institutet, SWE.
    Linda, Abrahamsson
    Karolinska Institutet, SWE.
    Erwin, Laure
    Royal Institute of Technology, SWE.
    Jan-Eric, Litton
    European Research Infrastructure Consortium, AUT.
    Juni, Palmgren
    Helsinki University, FIN.
    E-Science technologies in a workflow for personalized medicine using cancer screening as a case study2017Ingår i: JAMIA Journal of the American Medical Informatics Association, ISSN 1067-5027, E-ISSN 1527-974X, Vol. 24, nr 5, s. 950-957Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Objective: We provide an e-Science perspective on the workflow from risk factor discovery and classification of disease to evaluation of personalized intervention programs. As case studies, we use personalized prostate and breast cancer screenings.

    Materials and Methods: We describe an e-Science initiative in Sweden, e-Science for Cancer Prevention and Control (eCPC), which supports biomarker discovery and offers decision support for personalized intervention strategies. The generic eCPC contribution is a workflow with 4 nodes applied iteratively, and the concept of e-Science signifies systematic use of tools from the mathematical, statistical, data, and computer sciences.

    Results: The eCPC workflow is illustrated through 2 case studies. For prostate cancer, an in-house personalized screening tool, the Stockholm-3 model (S3M), is presented as an alternative to prostate-specific antigen testing alone. S3M is evaluated in a trial setting and plans for rollout in the population are discussed. For breast cancer, new biomarkers based on breast density and molecular profiles are developed and the US multicenter Women Informed to Screen Depending on Measures (WISDOM) trial is referred to for evaluation. While current eCPC data management uses a traditional data warehouse model, we discuss eCPC-developed features of a coherent data integration platform.

    Discussion and Conclusion: E-Science tools are a key part of an evidence-based process for personalized medicine. This paper provides a structured workflow from data and models to evaluation of new personalized intervention strategies. The importance of multidisciplinary collaboration is emphasized. Importantly, the generic concepts of the suggested eCPC workflow are transferrable to other disease domains, although each disease will require tailored solutions.

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