The hype over artificial intelligence (AI) has spawned claims that clinicians (particularly radiologists) will become redundant. It is still moot as to whether AI will replace radiologists in day-today clinical practice, but more AI applications are expected to be incorporated into the workflows in the foreseeable future. These applications could produce significant ethical and legal issues in healthcare if they cause abrupt disruptions to its contextual integrity and relational dynamics. Sustaining trust and trustworthiness is a key goal of governance, which is necessary to promote collaboration among all stakeholders and to ensure the responsible development and implementation of AI in radiology and other areas of clinical work. In this paper, the nature of AI governance in biomedicine is discussed along with its limitations. It is argued that radiologists must assume a more active role in propelling medicine into the digital age. In this respect, professional responsibilities include inquiring into the clinical and social value of AI, alleviating deficiencies in technical knowledge in order to facilitate ethical evaluation, supporting the recognition, and removal of biases, engaging the "black box" obstacle, and brokering a new social contract on informational use and security. In essence, a much closer integration of ethics, laws, and good practices is needed to ensure that AI governance achieves its normative goals.

Mastication parameters contribute significantly to GR. Eating slowly and having larger food boluses before swallowing (less chewing), both potentially modifiable, may be beneficial in glycemic control.

Indian and Malay neonates have a greater dSAT volume than do Chinese neonates. This finding supports the notion that in utero influences may contribute to higher cardiometabolic risk observed in Indian and Malay persons in our population. If such differences persist in the longitudinal tracking of adipose tissue growth, these differences may contribute to the ethnic disparities in risks of cardiometabolic diseases. This trial was registered at clinicaltrials.gov as NCT01174875.

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The term cancer predisposition syndrome (CPS) encompasses a multitude of familial cancers in which a clear mode of inheritance can be established, although a specific gene defect has not been described in all cases. Advances in genetics and the development of new imaging techniques have led to better understanding and early detection of these syndromes and offer the potential for preclinical diagnosis of any associated tumors. As a result, imaging has become an essential component of the clinical approach to management of CPSs and the care of children suspected of having a CPS or with a confirmed diagnosis. Common CPSs in children include neurofibromatosis type 1, Beckwith-Wiedemann syndrome, multiple endocrine neoplasia, Li-Fraumeni syndrome, von Hippel-Lindau syndrome, and familial adenomatous polyposis. Radiologists should be familiar with these syndromes, their common associated tumors, the new imaging techniques that are available, and current screening and surveillance recommendations to optimize the assessment of affected children.

Purpose To validate accuracy of diagnosis of developmental dysplasia of the hip (DDH) from geometric properties of acetabular shape extracted from three-dimensional (3D) ultrasonography (US). Materials and Methods In this retrospective multi-institutional study, 3D US was added to conventional two-dimensional (2D) US of 1728 infants (mean age, 67 days; age range, 3-238 days) evaluated for DDH from January 2013 to December 2016. Clinical diagnosis after more than 6 months follow-up was normal (n = 1347), borderline (Graf IIa, later normalizing spontaneously; n = 140) or dysplastic (Graf IIb or higher, n = 241). Custom software accessible through the institution's research portal automatically calculated indexes including 3D posterior and anterior alpha angle and osculating circle radius from hip surface models generated with less than 1 minute of user input. Logistic regression predicted clinical diagnosis (normal = 0, dysplastic = 1) from 3D indexes (ie, age and sex). Output represented probability of hip dysplasia from 0 to 1 (output: >0.9, dysplastic; 0.11-0.89, borderline; <0.1, normal). Software can be accessed through the research portal. Results Area under the receiver operating characteristic curve was equivalently high for 3D US indexes and 2D US alpha angle (0.996 vs 0.987). Three-dimensional US helped to correctly categorize 97.5% (235 of 241) dysplastic and 99.4% (1339 of 1347) normal hips. No dysplastic hips were categorized as normal. Correct diagnosis was provided at initial 3D US scan in 69.3% (97 of 140) of the studies diagnosed as borderline at initial 2D US scans. Conclusion Automatically calculated 3D indexes of acetabular shape performed equivalently to high-quality 2D US scans at tertiary medical centers to help diagnose DDH. Three-dimensional US reduced the number of borderline studies requiring follow-up imaging by over two-thirds.

ObjectivesLower vitamin D status has been associated with adiposity in children through adults. However, the evidence of the impact of maternal vitamin-D status during pregnancy on offspring’s adiposity is mixed. The objective of this study was to examine the associations between maternal vitamin-D [25(OH)D] status at mid-gestation and neonatal abdominal adipose tissue (AAT) compartments, particularly the deep subcutaneous adipose tissue linked with metabolic risk.MethodsParticipants (N = 292) were Asian mother-neonate pairs from the mother-offspring cohort, Growing Up in Singapore Towards healthy Outcomes. Neonates born at ≥34 weeks gestation with birth weight ≥2000 g had magnetic resonance imaging (MRI) within 2-weeks post-delivery. Maternal plasma glucose using an oral glucose tolerance test and 25(OH)D concentrations were measured. 25(OH)D status was categorized into inadequate (≤75.0 nmol/L) and sufficient (>75.0 nmol/L) groups. Neonatal AAT was classified into superficial (sSAT), deep subcutaneous (dSAT), and internal (IAT) adipose tissue compartments.ResultsInverse linear correlations were observed between maternal 25(OH)D and both sSAT (r = −0.190, P = 0.001) and dSAT (r = −0.206, P < 0.001). Each 1 nmol/L increase in 25(OH)D was significantly associated with reductions in sSAT (β = −0.14 (95% CI: −0.24, −0.04) ml, P = 0.006) and dSAT (β = −0.04 (−0.06, −0.01) ml, P = 0.006). Compared to neonates of mothers with 25(OH)D sufficiency, neonates with maternal 25(OH)D inadequacy had higher sSAT (7.3 (2.1, 12.4) ml, P = 0.006), and dSAT (2.0 (0.6, 3.4) ml, P = 0.005) volumes, despite similar birth weight. In the subset of mothers without gestational diabetes, neonatal dSAT was also greater (1.7 (0.3, 3.1) ml, P = 0.019) in neonates with maternal 25(OH)-inadequacy. The associations with sSAT and dSAT persisted even after accounting for maternal glycemia (fasting and 2-h plasma glucose).ConclusionsNeonates of Asian mothers with mid-gestation 25(OH)D inadequacy have a higher abdominal subcutaneous adipose tissue volume, especially dSAT (which is metabolically similar to visceral adipose tissue in adults), even after accounting for maternal glucose levels in pregnancy.

There is a crucial need for quick testing and diagnosis of patients during the COVID-19 pandemic. Lung ultrasound is an imaging modality that is cost-effective, widely accessible, and can be used to diagnose acute respiratory distress syndrome in patients with COVID-19. It can be used to find important characteristics in the images, including A-lines, B-lines, consolidation, and pleural effusion, which all inform the clinician in monitoring and diagnosing the disease. With the use of portable ultrasound transducers, lung ultrasound images can be easily acquired, however, the images are often of poor quality. They often require an expert clinician interpretation, which may be time-consuming and is highly subjective. We propose a method for fast and reliable interpretation of lung ultrasound images by use of deep learning, based on the Kinetics-I3D network. Our learned model can classify an entire lung ultrasound scan obtained at point-of-care, without requiring the use of preprocessing or a frame-by-frame analysis. We compare our video classifier against ground truth classification annotations provided by a set of expert radiologists and clinicians, which include A-lines, B-lines, consolidation, and pleural effusion. Our classification method achieves an accuracy of 90% and an average precision score of 95% with the use of 5-fold cross-validation. The results indicate the potential use of automated analysis of portable lung ultrasound images to assist clinicians in screening and diagnosing patients.