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  • 1.
    Moraes, Ana Louiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Eivazzadeh, Shahryar
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review2016In: Procedia Computer Science / [ed] Martinho R.,Rijo R.,Cruz-Cunha M.M.,Bjorn-Andersen N.,Quintela Varajao J.E., Elsevier, 2016, Vol. 100, p. 480-488Conference paper (Refereed)
    Abstract [en]

    OBJECTIVE: The objective of this paper is to investigate the goals and variables employed in the machine learning and microsimulation studies for the prognosis of dementia. METHOD: According to preset protocols, the Pubmed, Socups and Web of Science databases were searched to find studies that matched the defined inclusion/exclusion criteria, and then its references were checked for new studies. A quality checklist assessed the selected studies, and removed the low quality ones. The remaining ones (included set) had their data extracted and summarized. RESULTS: The summary of the data of the 37 included studies showed that the most common goal of the selected studies was the prediction of the conversion from mild cognitive impairment to Alzheimer's Disease, for studies that used machine learning, and cost estimation for the microsimulation ones. About the variables, neuroimaging was the most frequent used. CONCLUSIONS: The systematic literature review showed clear trends in prognosis of dementia research in what concerns machine learning techniques and microsimulation.

  • 2.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Supplementary Material of: “Prognosis of dementia with machine learning and microssimulation techniques: a systematic literature review”.2016Other (Other academic)
    Abstract [en]

     This document contains the supplementary material regarding the systematic literature review entitled: “Prognosis of dementia with machine learning and microssimulation techniques: a systematic literature review”.

  • 3.
    Moraes, Ana Luiza Dallora
    Blekinge Institute of Technology, Faculty of Computing, Department of Computer Science and Engineering.
    Systematic Literature Review Protocol:  Machine Learning and Microsimulation Techniques on the Prognosis of Dementia: A Systematic Literature Review2016Other (Other academic)
    Abstract [en]

     This document contains the protocol followed to conduct the systematic literature review entitled: “Machine Learning and Microsimulation Techniques on the Prognosis of Dementia: A Systematic Literature Review”.

  • 4.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Kvist, Ola
    KI, SWE.
    Mendes, Emilia
    Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
    Ruiz, Sandra
    KI, SWE.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis2019In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 7, article id e0220242Article, review/survey (Refereed)
    Abstract [en]

    Background The assessment of bone age and skeletal maturity and its comparison to chronological age is an important task in the medical environment for the diagnosis of pediatric endocrinology, orthodontics and orthopedic disorders, and legal environment in what concerns if an individual is a minor or not when there is a lack of documents. Being a time-consuming activity that can be prone to inter- and intra-rater variability, the use of methods which can automate it, like Machine Learning techniques, is of value. Objective The goal of this paper is to present the state of the art evidence, trends and gaps in the research related to bone age assessment studies that make use of Machine Learning techniques. Method A systematic literature review was carried out, starting with the writing of the protocol, followed by searches on three databases: Pubmed, Scopus and Web of Science to identify the relevant evidence related to bone age assessment using Machine Learning techniques. One round of backward snowballing was performed to find additional studies. A quality assessment was performed on the selected studies to check for bias and low quality studies, which were removed. Data was extracted from the included studies to build summary tables. Lastly, a meta-analysis was performed on the performances of the selected studies. Results 26 studies constituted the final set of included studies. Most of them proposed automatic systems for bone age assessment and investigated methods for bone age assessment based on hand and wrist radiographs. The samples used in the studies were mostly comprehensive or bordered the age of 18, and the data origin was in most of cases from United States and West Europe. Few studies explored ethnic differences. Conclusions There is a clear focus of the research on bone age assessment methods based on radiographs whilst other types of medical imaging without radiation exposure (e.g. magnetic resonance imaging) are not much explored in the literature. Also, socioeconomic and other aspects that could influence in bone age were not addressed in the literature. Finally, studies that make use of more than one region of interest for bone age assessment are scarce. Copyright: © 2019 Dallora et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

  • 5.
    Moraes, Ana Luiza Dallora
    et al.
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Sanmartin Berglund, Johan
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Brogren, Martin
    Optriva AB, SWE.
    Kvist, Ola
    Karolinska, SWE.
    Ruiz, Sandra Diaz
    Karolinska, SWE.
    Dübbel, André
    Optriva AB, SWE.
    Anderberg, Peter
    Blekinge Institute of Technology, Faculty of Engineering, Department of Health.
    Age assessment of youth and young adults using magnetic resonance imaging of the knee: A deep learning approach2019In: JMIR MEDICAL INFORMATICS, E-ISSN 2291-9694, Vol. 7, no 4, p. 419-436, article id e16291Article in journal (Refereed)
    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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