The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review.

INTRODUCTION: Low-dose computed tomography tends to produce lower image quality than normal dose computed tomography (CT) although it can help to reduce radiation hazards of CT scanning. Research has shown that Artificial Intelligence (AI) technologies, especially deep learning can help enhance the image quality of low-dose CT by denoising images. This scoping review aims to create an overview on how AI technologies, especially deep learning, can be used in dose optimisation for low-dose CT. METHODS: Literature searches of ProQuest, PubMed, Cinahl, ScienceDirect, EbscoHost Ebook Collection and Ovid were carried out to find research articles published between the years 2015 and 2020. In addition, manual search was conducted in SweMed+, SwePub, NORA, Taylor & Francis Online and Medic. RESULTS: Following a systematic search process, the review comprised of 16 articles. Articles were organised according to the effects of the deep learning networks, e.g. image noise reduction, image restoration. Deep learning can be used in multiple ways to facilitate dose optimisation in low-dose CT. Most articles discuss image noise reduction in low-dose CT. CONCLUSION: Deep learning can be used in the optimisation of patients' radiation dose. Nevertheless, the image quality is normally lower in low-dose CT (LDCT) than in regular-dose CT scans because of smaller radiation doses. With the help of deep learning, the image quality can be improved to equate the regular-dose computed tomography image quality. IMPLICATIONS TO PRACTICE: Lower dose may decrease patients' radiation risk but may affect the image quality of CT scans. Artificial intelligence technologies can be used to improve image quality in low-dose CT scans. Radiologists and radiographers should have proper education and knowledge about the techniques used.

Neurologic symptoms are common during gestation and puerperium in CADASIL.

Based on a structured questionnaire and medical records, the authors found that 12 of 25 mothers with cerebral autosomal dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL) with the R133C NOTCH3 mutation had had neurologic symptoms in 17 of their 43 pregnancies, most commonly hemiparesthesia (76%), hemiparesis (36%), aphasia (65%), and visual disorders (47%). In 82% of the patients, the symptoms were the first manifestation of CADASIL. The symptoms were most common during puerperium and in patients older than age 30.

The persistence of psychiatric deviance from the age of 8 to the age of 15 years.

BACKGROUND: Earlier research has shown that psychiatric problems in children tend to persist over years. This investigation assessed the persistence of psychiatric deviance among children over a 7-year period from the age of 8 to the age of 15 years. We also explored the relationship between problems leading to special attention at the well-baby clinics before school age and future psychiatric deviance. METHODS: The study material consisted of three questionnaires filled out by the parents, teachers and children themselves (N = 1268) at three time points, together with data concerning the children gathered from the records of well-baby clinics. RESULTS: At the age of 15 years, girls scored higher than boys on the parental scale and on the Beck's Depression Inventory (BDI), while boys scored higher than girls on the teachers' scale. Parental ratings had a high correlation over 3 years and a moderate correlation over 7 years. Teachers' and children's ratings correlated moderately over 3 years, but did not correlate significantly over 7 years. The probability of being deviant at the age of 15 years was elevated if the child scored high on the parents' or teachers' scale at the age of 8 or 12 years, or on the Children's Depression Inventory (CDI) at the age of 12 years. Problems noted by health professionals (problems in growth, somatic diseases, emotional/behavioural problems of the child, psychosocial problems of the family) before school age were related to future deviance on the parental scale. Emotional/behavioural problems before school age elevated the probability of scoring high on the teachers' scale at the age of 15, and problems in psychomotor development elevated the probability of scoring high on the BDI. CONCLUSIONS: Psychiatric deviance is persistent over several years in children. Primary health care professionals can identify children who are at risk for future psychiatric problems.