| 1. |
- Kvist, Ola F. T., et al.
(författare)
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Comparison of reliability of magnetic resonance imaging using cartilage and T1-weighted sequences in the assessment of the closure of the growth plates at the knee
- 2020
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Ingår i: Acta Radiologica Open. - London : Sage Publications. - 2058-4601. ; 9:9, s. 1-9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Growth development is traditionally evaluated with plain radiographs of the hand and wrist to visualize bone structures using ionizing radiation. Meanwhile, MRI visualizes bone and cartilaginous tissue without radiation exposure. Purpose: To determine the state of growth plate closure of the knee in healthy adolescents and young adults and compare the reliability of staging using cartilage sequences and T1-weighted (T1W) sequence between pediatric and general radiologists. Material and Methods: A prospective, cross-sectional study of MRI of the knee with both cartilage and T1W sequences was performed in 395 male and female healthy subjects aged between 14.0 and 21.5 years old. The growth plate of the femur and the tibia were graded using a modified staging scale by two pediatric and two general radiologists. Femur and tibia were graded separately with both sequences. Results: The intraclass correlation was overall excellent. The inter- and intra-observer agreement for pediatric radiologists on T1W was 82% (kappa = 0.73) and 77% (kappa = 0.65) for the femur and 90% (kappa = 0.82) and 87% (kappa = 0.75) for the tibia. The inter-observer agreement for general radiologists on T1W was 69% (kappa = 0.56) for the femur and 56% (kappa = 0.34) for the tibia. Cohen's kappa coefficient showed a higher inter- and intra-observer agreement for cartilage sequences than for T1W: 93% (kappa = 0.86) and 89% (kappa = 0.79) for the femur and 95% (kappa = 0.90) and 91% (kappa = 0.81) for the tibia. Conclusion: Cartilage sequences are more reliable than T1W sequence in the assessment of the growth plate in adolescents and young adults. Pediatric radiology experience is preferable.
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| 2. |
- Moraes, Ana Luiza Dallora, et al.
(författare)
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Chronological Age Assessment in Young Individuals Using Bone Age Assessment Staging and Nonradiological Aspects : Machine Learning Multifactorial Approach
- 2020
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Ingår i: JMIR Medical Informatics. - : JMIR Publications. - 2291-9694. ; 8:9
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Tidskriftsartikel (refereegranskat)abstract
- Background: Bone age assessment (BAA) is used in numerous pediatric clinical settings, as well as in legal settings when entities need an estimate of chronological age (CA) when valid documents are lacking. The latter case presents itself as critical since the law is harsher for adults and granted rights along with imputability changes drastically if the individual is a minor. Traditional BAA methods suffer from drawbacks such as exposure of minors to radiation, do not consider factors that might affect the bone age and they mostly focus on a single region. Given the critical scenarios in which BAA can affect the lives of young individuals it is important to focus on the drawbacks of the traditional methods and investigate the potential of estimating CA through BAA.Objective: This paper aims to investigate CA estimation through BAA in young individuals of 14 to 21 years with machine learning methods, addressing the drawbacks in the research using magnetic resonance imaging (MRI), assessment of multiple ROIs and other factors that may affect the bone age.Methods: MRI examinations of the radius, distal tibia, proximal tibia, distal femur and calcaneus were carried out on 465 males and 473 females subjects (14-21 years). Measures of weight and height were taken from the subjects and a questionnaire was given for additional information (self-assessed Tanner Scale, physical activity level, parents' origin, type of residence during upbringing). Two pediatric radiologists assessed, independently, the MRI images as to their stage of bone development (blinded to age, gender and each other). All the gathered information was used in training machine learning models for chronological age estimation and minor versus adults classification (threshold of 18 years). Different machine learning methods were investigated.Results: The minor versus adults classification produced accuracies of 90% and 84%, for male and female subjects, respectively, with high recalls for the classification of minors. The chronological age estimation for the eight age groups (14-21 years) achieved mean absolute errors of 0.95 years and 1.24 years for male and female subjects, respectively. However, for the latter lower error occurred only for the ages of 14 and 15.Conclusions: This paper proposed to investigate the CA estimation through BAA using machine learning methods in two ways: minor versus adults classification and CA estimation in eight age groups (14-21 years), while addressing the drawbacks in the research on BAA. The first achieved good results, however, for the second case BAA showed not precise enough for the classification.
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| 3. |
- Moraes, Ana Luiza Dallora
(författare)
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Machine learning applications in healthcare
- 2020
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Healthcare is an important and high cost sector that involves many decision-making tasks based on the analysis of data, from its primary activities up till management itself. A technology that can be useful in an environment as data-intensive as healthcare is machine learning. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare.The two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. The diagnosis research context regards the age assessment of the young individuals, which aims to address the drawbacks in the bone age assessment research, investigating new age assessment methods. The prognosis research context regards the prognosis of dementia, which aims to investigate prognostic estimates for older individuals who came to develop the dementia disorder, in a time frame of 10 years. Machine learning applications were shown to be useful in both research contexts.In the diagnosis research context, study I summarized the state of the art evidence in the area of bone age assessment with the use of machine learning, identifying both automated and non-automated approaches for age assessment. Study II investigated a non-automated approach based on the radiologists' assessment and study III investigated an automated approach based on deep learning. Both studies used magnetic resonance imaging. The results showed that the radiologists' assessment as input was not precise enough for the estimation of age. However, the deep learning method was able to extract more useful features from the images and provided better diagnostic performance for the age assessment.In the research context of prognosis, study IV conducted a review on the relevant evidence in on the prognosis of dementia with machine learning techniques, identifying a focus on the research on neuroimaging studies dedicated to validating biomarkers for pharmaceutical research. Study V proposed a multifactorial decision tree approach for the prognosis of dementia in older individuals as to their development or not of dementia in 10 years. Achieving consistent performance results, it provided an interpretable prognostic model identifying possible modifiable and non-modifiable risk factors and possible patient subgroups of importance for the dementia research.
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| 4. |
- Moraes, Ana Luiza Dallora, et al.
(författare)
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Multifactorial 10-year prior diagnosis prediction model of dementia
- 2020
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Ingår i: International Journal of Environmental Research and Public Health. - : MDPI. - 1661-7827 .- 1660-4601. ; 17:18, s. 1-18
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Tidskriftsartikel (refereegranskat)abstract
- Dementia is a neurodegenerative disorder that affects the older adult population. To date, no cure or treatment to change its course is available. Since changes in the brains of affected individuals could be evidenced as early as 10 years before the onset of symptoms, prognosis research should consider this time frame. This study investigates a broad decision tree multifactorial approach for the prediction of dementia, considering 75 variables regarding demographic, social, lifestyle, medical history, biochemical tests, physical examination, psychological assessment and health instruments. Previous work on dementia prognoses with machine learning did not consider a broad range of factors in a large time frame. The proposed approach investigated predictive factors for dementia and possible prognostic subgroups. This study used data from the ongoing multipurpose Swedish National Study on Aging and Care, consisting of 726 subjects (91 presented dementia diagnosis in 10 years). The proposed approach achieved an AUC of 0.745 and Recall of 0.722 for the 10-year prognosis of dementia. Most of the variables selected by the tree are related to modifiable risk factors; physical strength was important across all ages. Also, there was a lack of variables related to health instruments routinely used for the dementia diagnosis.
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