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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. [ABSTRACT FROM AUTHOR]

Objectives: To extend existing research on the US health disadvantage relative to Europe by studying the relationships of disability with age from midlife to old age in the US and four European regions (England/Northern and Western Europe/Southern Europe/Eastern Europe) including their wealth-related differences, using a flexible statistical approach to model the age-functions. Methods: We used data from three studies on aging, with nationally representative samples of adults aged 50 to 85 from 15 countries (N = 48225): the US-American Health and Retirement Study (HRS), the English Longitudinal Study of Ageing (ELSA) and the Survey of Health, Ageing and Retirement in Europe (SHARE). Outcomes were mobility limitations and limitations in instrumental activities of daily living. We applied fractional polynomials of age to determine best fitting functional forms for age on disability in each region, while controlling for socio-demographic characteristics and important risk factors (hypertension, diabetes, obesity, smoking, physical inactivity). Results: Findings showed high levels of disability in the US with small age-related changes between 50 and 85. Levels of disability were generally lower in Eastern Europe, followed by England and Southern Europe and lowest in Northern and Western Europe. In these latter countries age-related increases of disability, though, were steeper than in the US, especially in Eastern and Southern Europe. For all countries and at all ages, disability levels were higher among adults with low wealth compared to those with high wealth, with largest wealth-related differences among those in early old age in the USA. Conclusions: This paper illustrates considerable variations of disability and its relationship with age. It supports the hypothesis that less developed social policies and more pronounced socioeconomic inequalities are related to higher levels of disability and an earlier onset of disability. [ABSTRACT FROM AUTHOR]