Deep learning in medical imaging and radiation therapy

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/1/mp13264_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/146980/2/mp13264.pd

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Diseases of the Chest, Breast, Heart and Vessels 2019-2022

This open access book focuses on diagnostic and interventional imaging of the chest, breast, heart, and vessels. It consists of a remarkable collection of contributions authored by internationally respected experts, featuring the most recent diagnostic developments and technological advances with a highly didactical approach. The chapters are disease-oriented and cover all the relevant imaging modalities, including standard radiography, CT, nuclear medicine with PET, ultrasound and magnetic resonance imaging, as well as imaging-guided interventions. As such, it presents a comprehensive review of current knowledge on imaging of the heart and chest, as well as thoracic interventions and a selection of "hot topics". The book is intended for radiologists, however, it is also of interest to clinicians in oncology, cardiology, and pulmonology

Automated Quality Assessment of Printed Objects Using Subjective and Objective Methods Based on Imaging and Machine Learning Techniques.

Estimating the perceived quality of printed patterns is a complex task as quality is subjective. A study was conducted to evaluate how accurately a machine learning method can predict human judgment about printed pattern quality. The project was executed in two phases: a subjective test to evaluate the printed pattern quality and development of the machine learning classifier-based automated objective model. In the subjective experiment, human observers ranked overall visual quality. Object quality was compared based on a normalized scoring scale. There was a high correlation between subjective evaluation ratings of objects with similar defects. Observers found the contrast of the outer edge of the printed pattern to be the best distinguishing feature for determining the quality of object. In the second phase, the contrast of the outer print pattern was extracted by flat-fielding, cropping, segmentation, unwrapping and an affine transformation. Standard deviation and root mean square (RMS) metrics of the processed outer ring were selected as feature vectors to a Support Vector Machine classifier, which was then run with optimized parameters. The final objective model had an accuracy of 83%. The RMS metric was found to be more effective for object quality identification than the standard deviation. There was no appreciable difference in using RGB data of the pattern as a whole versus using red, green and blue separately in terms of classification accuracy. Although contrast of the printed patterns was found to be an important feature, other features may improve the prediction accuracy of the model. In addition, advanced deep learning techniques and larger subjective datasets may improve the accuracy of the current objective model

Deprescribing tool for STOPPFall (screening tool of older persons prescriptions in older adults with high fall risk) items

Background: Health care professionals are often reluctant to deprescribe fall-risk-increasing drugs (FRIDs). Lack of knowledge and skills form a significant barrier. To support clinicians in the management of FRIDs and to facilitate the deprescribing process, a deprescribing tool was developed by a European expert group for STOPPFall (Screening Tool of Older Persons Prescriptions in older adults with high fall risk) items. Methods: STOPPFall was created using an expert Delphi consensus process in 2019 and in 2020, 24 panellists from EuGMS SIG on Pharmacology and Task and Finish on FRIDs completed deprescribing tool questionnaire. To develop the questionnaire, a Medline literature search was performed. The panellists were asked to indicate for every medication class a possible need for stepwise withdrawal and strategy for withdrawal. They were asked in which situations withdrawal should be performed. Furthermore, panellists were requested to indicate those symptoms patients should be monitored for after deprescribing and a possible need for follow-ups. Results: Practical deprescribing guidance was developed for STOPPFall medication classes. For each medication class, a decision tree algorithm was developed including steps from medication review to symptom monitoring after medication withdrawal. Conclusion: STOPPFall was combined with a practical deprescribing tool designed to optimize medication review. This practical guide can help overcome current reluctance towards deprescribing in clinical practice by providing an up-to-date and straightforward source of expert knowledge

Association between number of medications and mortality in geriatric inpatients : a Danish nationwide register-based cohort study

Purpose: To explore the association between the number of medications and mortality in geriatric inpatients taking activities of daily living and comorbidities into account. Methods: A nationwide population-based cohort study was performed including all patients aged C65 years admitted to geriatric departments in Denmark during 2005-2014. The outcome of interest was mortality. Activities of daily living using Barthel-Index (BI) were measured at admission. National health registers were used to link data on an individual level extracting data on medications, and hospital diseases. Patients were followed to the end of study (31.12.2015), death, or emigration, which ever occurred first. Kaplan-Meier survival curves were used to estimate crude survival proportions. Univariable and multivariable analyses were performed using Cox regression. The multivariable analysis adjusted for age, marital status, period of hospital admission, BMI, and BI (model 1), and further adding either number of diseases (model 2) or Charlson comorbidity index (model 3). Results: We included 74603 patients (62.8% women), with a median age of 83 (interquartile range [IQR] 77-88) years. Patients used a median of 6 (IQR 4-9) medications. Increasing number of medications was associated with increased overall, 30-days, and 1-year mortality in all 3 multivariable models for both men and women. For each extra medication the mortality increased by 3% in women and 4% in men in the fully adjusted model. Conclusion: Increasing number of medications was associated with mortality in this nationwide cohort of geriatric inpatients. Our findings highlight the importance of polypharmacy in older patients with comorbidities

Physical activity and exercise in dementia : an umbrella review of intervention and observational studies

Background: Dementia is a common condition in older people. Among the potential risk factors, increasing attention has been focused on sedentary behaviour. However, synthesizing literature exploring whether physical activity/exercise can affect health outcomes in people with dementia or with mild cognitive impairment (MCI) is still limited. Therefore, the aim of this umbrella review, promoted by the European Geriatric Medicine Society (EuGMS), is to understand the importance of physical activity/exercise for improving cognitive and non-cognitive outcomes in people with dementia/MCI. Methods: Umbrella review of systematic reviews (SR) (with or without meta-analyses) of randomized controlled trials (RCTs) and observational (prospective and case-control in people with MCI) studies based on a systematic literature search in several databases. The certainty of evidence of statistically significant outcomes attributable to physical activity/exercise interventions was evaluated using Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. Results: Among 1,160 articles initially evaluated, 27 systematic reviews (4 without meta-analysis) for a total of 28,205 participants with dementia/MCI were included. No observational study on physical activity/exercise in MCI for preventing dementia was included. In SRs with MAs, physical activity/exercise was effective in improving global cognition in Alzheimer’s disease and in all types of dementia (very low/low certainty of evidence). Moreover, physical activity/ exercise significantly improved global cognition, attention, executive function, and memory in MCI, with a certainty of evidence varying from low to moderate. Finally, physical activity/exercise improved non-cognitive outcomes in people with dementia including falls and neuropsychiatric symptoms. SRs, without meta-analysis, corroborated these results. Conclusions: Supported by very low to moderate certainty of evidence, physical activity/exercise has a positive effect on several cognitive and non-cognitive outcomes in people with dementia and MCI, but RCTs, with low risk of bias/confounding, are still needed to confirm these findings

Women in Artificial intelligence (AI)

This Special Issue, entitled "Women in Artificial Intelligence" includes 17 papers from leading women scientists. The papers cover a broad scope of research areas within Artificial Intelligence, including machine learning, perception, reasoning or planning, among others. The papers have applications to relevant fields, such as human health, finance, or education. It is worth noting that the Issue includes three papers that deal with different aspects of gender bias in Artificial Intelligence. All the papers have a woman as the first author. We can proudly say that these women are from countries worldwide, such as France, Czech Republic, United Kingdom, Australia, Bangladesh, Yemen, Romania, India, Cuba, Bangladesh and Spain. In conclusion, apart from its intrinsic scientific value as a Special Issue, combining interesting research works, this Special Issue intends to increase the invisibility of women in AI, showing where they are, what they do, and how they contribute to developments in Artificial Intelligence from their different places, positions, research branches and application fields. We planned to issue this book on the on Ada Lovelace Day (11/10/2022), a date internationally dedicated to the first computer programmer, a woman who had to fight the gender difficulties of her times, in the XIX century. We also thank the publisher for making this possible, thus allowing for this book to become a part of the international activities dedicated to celebrating the value of women in ICT all over the world. With this book, we want to pay homage to all the women that contributed over the years to the field of AI