Purpose:To measure the apparent diffusion coefficient (ADC) of hyperpolarized (HP) 3 He gas using diffusion weighted MRI in healthy volunteers and patients with emphysema and examine the reproducibility and volume dependency. Materials and Methods:A total of eight healthy volunteers and 16 patients with emphysema were examined after inhalation of HP 3 He gas mixed with nitrogen (N 2 ) during breathhold starting from functional residual capacity (FRC) in supine position. Coronal diffusion-sensitized MR images were acquired. Each subject was imaged on three separate days over a seven-day period and received two different volumes (6% and 15% of total lung capacity [TLC]) of HP 3 He each day. ADC maps and histograms were calculated. The mean and standard deviation (SD) of the ADC at different days and volumes were compared. Results:The reproducibility of the mean ADC and SD over several days was good in both healthy volunteers and patients (SD range of 0.003-0.013 cm 2 /second and 0.001-0.009 cm 2 /second at 6% and 15% of TLC for healthy volunteers, and a SD range of 0.001-0.041 cm 2 /second and 0.001-0.011 cm 2 /second, respectively, for patients). A minor but significant increase in mean ADC with increased inhaled gas volume was observed in both groups. Conclusion:Mean ADC and SD of HP 3 He MRI is reproducible and discriminates well between healthy controls and patients with emphysema at the higher gas volume. This method is robust and may be useful to gain new insights into the pathophysiology and course of emphysema. THE HABIT OF SMOKING is increasing worldwide and so is one of its direct consequences, i.e., the chronic obstructive pulmonary disease (COPD). By the year 2020, COPD is expected to rank third as a cause of mortality and fifth of morbidity (1). A validated imaging technique that is sensitive to very early structural changes in the lungs could be helpful in the design of therapy and management of emphysema.MRI using hyperpolarized (HP) gases as helium ( 3 He) have emerged as a promising technique for studies of the structure and function of the lungs. Inhalation of HP 3 He can be used not only to derive information related to regional ventilation but also to the size of the alveoli. This is accomplished by measurement of the apparent diffusion coefficient (ADC) of inhaled HP 3 He. When HP 3 He is inhaled, diffusion of the gas is restricted by the boundaries of the alveoli and if restricted diffusion measurement conditions are met, the measured ADC will reflect the size of the peripheral airway spaces (2-4). It could thereby allow quantification of the structural changes (of the lungs) in emphysema. The ADC has been shown to increase in animals with elastase-induced emphysema (5). Studies have also been performed in small numbers of healthy adult subjects and subjects with lung disease (6 -12). In these studies, the ADC values in emphysematous lungs were increased relative to ADC values obtained in subjects with healthy lungs.

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.

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Alpha-1-antitrypsin (AAT) deficiency is a genetic risk factor for pulmonary emphysema. In 1972-74 all 200,000 Swedish new-born infants were screened for AAT deficiency. The aim of the present study was to investigate whether the PiZZ and PiSZ individuals identified by this screening have signs of emphysema and the role of smoking in this, compared with a random sample of control subjects at 35 years of age. The study participants underwent complete pulmonary function tests (PFT) and CT densitometry. The fifteenth percentile density (PD15) and the relative area below -910 HU (RA-910) were analyzed. Fifty-four PiZZ, 21 PiSZ and 66 PiMM control subjects participated in the study. No significant differences were found in lung function between the never-smoking AAT-deficient and control subjects. The 16 PiZZ ever-smokers had significantly lower carbon monoxide transfer coefficient (KCO) than the 20 PiSZ never-smokers (p = 0.014) and the 44 PiMM never-smokers (p = 0.005). After correction for the CT derived lung volume, the PiZZ ever-smokers had significantly lower PD15 (p = 0.046) than the ever-smoking controls. We conclude that 35-year-old PiZZ and PiSZ never-smokers have normal lung function when compared with never-smoking control subjects. The differences in KCO and CT densitometric parameters between the PiZZ ever-smokers and the control subjects may indicate early signs of emphysema.

COPD was a common finding in patients with pSS, even among never-smoking patients. An obstructive pattern was the predominant PFT finding in patients with pSS, although a superimposed restrictive lung disease could not be excluded. The results suggest that the disease per se is involved in the development of COPD in pSS.

The pSS patients showed signs of both obstructive and restrictive pulmonary disease and COPD commonly developed during follow-up. Respiratory symptoms and radiographic abnormalities were common but poorly associated with PFT in pSS patients.

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.

Airspace dimension assessment with nanoparticles (AiDA) is a novel method to measure distal airspace radius non-invasively. In this study, AiDA radii were measured in 618 individuals from the population-based Swedish CArdiopulmonary BioImaging Study, SCAPIS. Subjects with emphysema detected by computed tomography were compared to non-emphysematous subjects. The 47 individuals with mainly mild-to-moderate visually detected emphysema had significantly larger AiDA radii, compared with non-emphysematous subjects (326±48 µm vs 291±36 µm); OR for emphysema per 10 µm: 1.22 (1.13–1.30, p<0.0001). Emphysema according to CT densitometry was similarly associated with larger radii compared with non-emphysematous CT examinations (316±41 µm vs 291 µm±26 µm); OR per 10 µm: 1.16 (1.08–1.24, p<0.0001). The results are in line with comparable studies. The results show that AiDA is a potential biomarker for emphysema in individuals in the general population.