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Moraes, Ana Luiza DalloraORCID iD iconorcid.org/0000-0002-6752-017X
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Publications (5 of 5) Show all publications
Moraes, A. L., Sanmartin Berglund, J., Brogren, M., Kvist, O., Ruiz, S. D., Dübbel, A. & Anderberg, P. (2019). Age assessment of youth and young adults using magnetic resonance imaging of the knee: A deep learning approach. JMIR MEDICAL INFORMATICS, 7(4), 419-436, Article ID e16291.
Open this publication in new window or tab >>Age assessment of youth and young adults using magnetic resonance imaging of the knee: A deep learning approach
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2019 (English)In: JMIR MEDICAL INFORMATICS, E-ISSN 2291-9694, Vol. 7, no 4, p. 419-436, article id e16291Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
JMIR PUBLICATIONS, 2019
Keywords
Age assessment, Bone age, Convolutional neural networks, Deep learning, Knee, Machine learning, Magnetic resonance imaging, Medical imaging, Skeletal maturity, Transfer learning, adolescent, adult, article, bone age determination, child, convolutional neural network, diagnostic imaging, employment, female, human, human experiment, juvenile, major clinical study, male, maturity, nuclear magnetic resonance imaging, transfer of learning, young adult
National Category
Radiology, Nuclear Medicine and Medical Imaging Orthopaedics
Identifiers
urn:nbn:se:bth-19085 (URN)10.2196/16291 (DOI)000510198100031 ()2-s2.0-85077013897 (Scopus ID)
Note

open access

Available from: 2020-01-09 Created: 2020-01-09 Last updated: 2020-02-20
Moraes, A. L., Anderberg, P., Kvist, O., Mendes, E., Ruiz, S. & Sanmartin Berglund, J. (2019). Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis. PLoS ONE, 14(7), Article ID e0220242.
Open this publication in new window or tab >>Bone age assessment with various machine learning techniques: A systematic literature review and meta-analysis
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2019 (English)In: PLoS ONE, ISSN 1932-6203, E-ISSN 1932-6203, Vol. 14, no 7, article id e0220242Article, review/survey (Refereed) Published
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.

Place, publisher, year, edition, pages
Public Library of Science, 2019
National Category
Other Medical Sciences not elsewhere specified
Identifiers
urn:nbn:se:bth-18620 (URN)10.1371/journal.pone.0220242 (DOI)000484977900073 ()2-s2.0-85069805545 (Scopus ID)
Note

open access

Available from: 2019-09-10 Created: 2019-09-10 Last updated: 2019-10-09Bibliographically approved
Moraes, A. L., Eivazzadeh, S., Mendes, E., Sanmartin Berglund, J. & Anderberg, P. (2016). Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review. In: Martinho R.,Rijo R.,Cruz-Cunha M.M.,Bjorn-Andersen N.,Quintela Varajao J.E. (Ed.), Procedia Computer Science: . Paper presented at Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist, Porto (pp. 480-488). Elsevier, 100
Open this publication in new window or tab >>Prognosis of Dementia Employing Machine Learning and Microsimulation Techniques: A Systematic Literature Review
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2016 (English)In: 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, Published 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.

Place, publisher, year, edition, pages
Elsevier, 2016
Series
Procedia Computer Science, ISSN 1877-0509
Keywords
dementia, machine learning, microsimulation, prognosis, Artificial intelligence, Cost estimating, Diagnosis, Information systems, Learning systems, Neurodegenerative diseases, Neuroimaging, Project management, Alzheimer's disease, Cost estimations, Machine learning techniques, Mild cognitive impairments, Systematic literature review, Information management
National Category
Geriatrics Other Computer and Information Science
Identifiers
urn:nbn:se:bth-13767 (URN)10.1016/j.procs.2016.09.185 (DOI)000392695900059 ()2-s2.0-85006952996 (Scopus ID)
Conference
Conference on ENTERprise Information Systems / International Conference on Project MANagement / Conference on Health and Social Care Information Systems and Technologies, CENTERIS / ProjMAN / HCist, Porto
Available from: 2017-01-16 Created: 2017-01-16 Last updated: 2018-01-13Bibliographically approved
Moraes, A. L. (2016). Supplementary Material of: “Prognosis of dementia with machine learning and microssimulation techniques: a systematic literature review”..
Open this publication in new window or tab >>Supplementary Material of: “Prognosis of dementia with machine learning and microssimulation techniques: a systematic literature review”.
2016 (English)Other (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”.

National Category
Computer Systems
Identifiers
urn:nbn:se:bth-11793 (URN)
Available from: 2016-04-04 Created: 2016-04-04 Last updated: 2017-01-09Bibliographically approved
Moraes, A. L. (2016). Systematic Literature Review Protocol:  Machine Learning and Microsimulation Techniques on the Prognosis of Dementia: A Systematic Literature Review.
Open this publication in new window or tab >>Systematic Literature Review Protocol:  Machine Learning and Microsimulation Techniques on the Prognosis of Dementia: A Systematic Literature Review
2016 (English)Other (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”.

National Category
Bioinformatics (Computational Biology)
Identifiers
urn:nbn:se:bth-13736 (URN)
Available from: 2017-01-09 Created: 2017-01-09 Last updated: 2018-01-13Bibliographically approved
Organisations
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ORCID iD: ORCID iD iconorcid.org/0000-0002-6752-017X

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