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BackgroundBone 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. ObjectiveThe 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. MethodsThis 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. ResultsThe 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. ConclusionsThe 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.

Background Acute kidney injury (AKI) remains common among infants with hypothermia-treated hypoxic-ischaemic encephalopathy (HIE). Little is known about long-term kidney outcomes following hypothermia treatment. We recently reported that 21% of survivors of hypothermia-treated HIE had decreased estimated glomerular filtration rate (eGFR) based on plasma creatinine in early adolescence. Here, we assessed kidney functions more comprehensively in our population-based cohort of children born in Stockholm 2007-2009 with a history of hypothermia-treated HIE. Methods At 10-12 years of age, we measured cystatin C (cyst C) to estimate GFR. Children with decreased cyst C eGFR also underwent iohexol clearance examination. We measured urine-albumin/creatinine ratio, blood pressure (BP) and kidney volume on magnetic resonance imaging. Fibroblast growth factor 23 (FGF 23) levels in plasma were assessed by enzyme-linked immunosorbent assay (ELISA). Outcomes were compared between children with and without a history of neonatal AKI. Results Forty-seven children participated in the assessment. Two children (2/42) had decreased cyst C eGFR, for one of whom iohexol clearance confirmed mildly decreased GFR. One child (1/43) had Kidney Disease Improving Global Outcomes (KDIGO) category A2 albuminuria, and three (3/45) had elevated office BP. Subsequent ambulatory 24-h BP measurement confirmed high normal BP in one case only. No child had hypertension. Kidney volume and FGF 23 levels were normal in all children. There was no difference in any of the parameters between children with and without a history of neonatal AKI. Conclusion Renal sequelae were rare in early adolescence following hypothermia-treated HIE regardless of presence or absence of neonatal AKI. Graphical abstract, Author(s): Katarina Robertsson Grossmann [sup.1] [sup.2] [sup.3] , Liya Vishnevskaya [sup.4] , Sandra Diaz Ruiz [sup.5] [sup.6] [sup.7] , Karolina Kublickiene [sup.8] , Peter Bárány [sup.2] [sup.8] , Mats Blennow [...]