Machine learning applications were shown to be useful in both research contexts. 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. 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 two research contexts in this thesis regard two pivotal activities in the healthcare systems: diagnosis and prognosis. AHT refers to application of scientific methods for the development of interventions targeting practical problems related to health and healthcare. This thesis investigates the application of machine learning in healthcare contexts as an applied health technology (AHT). A technology that can be useful in an environment as data-intensive as healthcare is machine learning. 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. 8, no 9, article id e18846Ĭhronological age assessment, bone age, skeletal maturity, machine learning, magnetic resonance imaging, radius, distal tibia, proximal tibia, distal femur, calcaneus National Category Place, publisher, year, edition, pagesJMIR Publications Inc. The first achieved good results, however, for the second case BAA showed not precise enough for the classification. However, for the latter lower error occurred only for the ages of 14 and 15.Ĭonclusions: 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 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. 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. Different machine learning methods were investigated. All the gathered information was used in training machine learning models for chronological age estimation and minor versus adults classification (threshold of 18 years). Two pediatric radiologists assessed, independently, the MRI images as to their stage of bone development (blinded to age, gender and each other).
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). 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). 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. 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. 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. 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. 8, no 9, article id e18846 Article in journal (Refereed) Published Abstract īackground: 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. Show others and affiliations 2020 (English) In: JMIR Medical Informatics, E-ISSN 2291-9694, Vol.
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