scholarly journals Artificial intelligence in paediatric radiology: Future opportunities

2021 ◽  
Vol 94 (1117) ◽  
pp. 20200975
Author(s):  
Natasha Davendralingam ◽  
Neil J Sebire ◽  
Owen J Arthurs ◽  
Susan C Shelmerdine
Keyword(s):  
Artificial Intelligence ◽  
High Performance ◽  
Early Stage ◽  
Image Interpretation ◽  
Paediatric Radiology ◽  
Radiology Service ◽  
Clinical Radiology ◽  
The Media ◽  
Performance Statistics ◽  
Image Pattern

Artificial intelligence (AI) has received widespread and growing interest in healthcare, as a method to save time, cost and improve efficiencies. The high-performance statistics and diagnostic accuracies reported by using AI algorithms (with respect to predefined reference standards), particularly from image pattern recognition studies, have resulted in extensive applications proposed for clinical radiology, especially for enhanced image interpretation. Whilst certain sub-speciality areas in radiology, such as those relating to cancer screening, have received wide-spread attention in the media and scientific community, children’s imaging has been hitherto neglected. In this article, we discuss a variety of possible ‘use cases’ in paediatric radiology from a patient pathway perspective where AI has either been implemented or shown early-stage feasibility, while also taking inspiration from the adult literature to propose potential areas for future development. We aim to demonstrate how a ‘future, enhanced paediatric radiology service’ could operate and to stimulate further discussion with avenues for research.

AUTISM IN THE DISCOURSE OF THE RUSSIAN MASS MEDIA: CONSTRUCTING OF THE IMAGE OF A HUMAN WITH ASD

2020 ◽  
pp. 37-43
Author(s):  
Saveleva Zh.V.
Keyword(s):  
Mass Media ◽  
Early Stage ◽  
Autism Spectrum ◽  
Media Discourse ◽  
Adults With Autism ◽  
Video Clips ◽  
The Media ◽  
Inner World ◽  
Spectrum Disorders ◽  
The Mass Media

The prevalence of autism is growing, the problems of stigmatization and discrimination of people with autism spectrum disorders in society are exacerbating. The mass media play an important role in enlightening and reducing stigmatizing effects, in connection with which the goal was formulated to study the construction of images of a person with ASD in the mass media by the method of qualitative and discourse analysis of video clips from the federal channel. According to the results of the study, it can be argued that the range of characteristics used to describe people with autism in media discourse is diverse, but in retrospect, dominant interpretation models can be identified. At an early stage, the prevailing image of a person with ASD was deprived of the quality’s characteristic of normotypical people who do not want to leave their world. People diagnosed with autism were referred to as the intolerant category of "autistic". Since 2013, there has been a discursive turn, within which the category “autist” is replaced by tolerant speech patterns, adults with autism get into the lens of the media, the topic of uncommunicability as a property of a person with autism is replaced by the intention of the lack of opportunities to communicate, one of the reasons for which is social exclusion. In television stories of recent years, the mass media are actively constructing the image of a person with autism spectrum disorder through his inner world, through the advantages that a person with ASD can have due to his characteristics. However, it cannot be said that there has been a complete change of the image: the old cliches, as a rule, manifest themselves at a more latent level of grammatical constructions and semiotic meanings.

МЕТОДЫ ДОСТИЖЕНИЯ МАКСИМАЛЬНОЙ ЭФФЕКТИВНОСТИ ПЛАТФОРМЫ ПРОТОТИПИРОВАНИЯ ВЫСОКОПРОИЗВОДИТЕЛЬНЫХ СИСТЕМ НА КРИСТАЛЛЕ НА ЗАДАЧАХ ИСКУССТВЕННОГО ИНТЕЛЛЕКТА

2020 ◽  
Vol 96 (3s) ◽  
pp. 585-588
Author(s):  
С.Е. Фролова ◽  
Е.С. Янакова
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Computer Vision ◽  
High Performance ◽  
Systems On Chip ◽  
High Performance Systems ◽  
On Chip ◽  
Network Technologies ◽  
Neural Network Technologies

Предлагаются методы построения платформ прототипирования высокопроизводительных систем на кристалле для задач искусственного интеллекта. Изложены требования к платформам подобного класса и принципы изменения проекта СнК для имплементации в прототип. Рассматриваются методы отладки проектов на платформе прототипирования. Приведены результаты работ алгоритмов компьютерного зрения с использованием нейросетевых технологий на FPGA-прототипе семантических ядер ELcore. Methods have been proposed for building prototyping platforms for high-performance systems-on-chip for artificial intelligence tasks. The requirements for platforms of this class and the principles for changing the design of the SoC for implementation in the prototype have been described as well as methods of debugging projects on the prototyping platform. The results of the work of computer vision algorithms using neural network technologies on the FPGA prototype of the ELcore semantic cores have been presented.

scholarly journals Intranasal administration of the chemotherapeutic perillyl alcohol results in selective delivery to the cerebrospinal fluid in rats

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Geetika Nehra ◽  
Shannon Andrews ◽  
Joan Rettig ◽  
Michael N. Gould ◽  
Jill D. Haag ◽  
...  
Keyword(s):  
Cerebrospinal Fluid ◽  
Brain Tumors ◽  
Nasal Mucosa ◽  
Brain Cancer ◽  
Intranasal Administration ◽  
High Performance ◽  
Early Stage ◽  
Plasma Concentrations ◽  
Perillyl Alcohol ◽  
Systemic Absorption

AbstractPerillyl alcohol (POH) has been extensively studied for the treatment of peripheral and primary brain tumors. The intranasal route of administration has been preferred for dosing POH in early-stage clinical trials associated with promising outcomes in primary brain cancer. However, it is unclear how intranasal POH targets brain tumors in these patients. Multiple studies indicate that intranasally applied large molecules may enter the brain and cerebrospinal fluid (CSF) through direct olfactory and trigeminal nerve-associated pathways originating in the nasal mucosa that bypass the blood–brain barrier. It is unknown whether POH, a small molecule subject to extensive nasal metabolism and systemic absorption, may also undergo direct transport to brain or CSF from the nasal mucosa. Here, we compared CSF and plasma concentrations of POH and its metabolite, perillic acid (PA), following intranasal or intravascular POH application. Samples were collected over 70 min and assayed by high-performance liquid chromatography. Intranasal administration resulted in tenfold higher CSF-to-plasma ratios for POH and tenfold higher CSF levels for PA compared to equal dose intravascular administration. Our preclinical results demonstrate POH undergoes direct transport from the nasal mucosa to the CSF, a finding with potential significance for its efficacy as an intranasal chemotherapeutic for brain cancer.

scholarly journals Neighborhood Energy Modeling and Monitoring: A Case Study

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3716
Author(s):  
Francesco Causone ◽  
Rossano Scoccia ◽  
Martina Pelle ◽  
Paola Colombo ◽  
Mario Motta ◽  
...  
Keyword(s):  
High Performance ◽  
High Efficiency ◽  
Early Stage ◽  
Energy Performance ◽  
Monitoring Plan ◽  
Performance Targets ◽  
Carrier Energy ◽  
Zero Carbon ◽  
Energy Grid

Cities and nations worldwide are pledging to energy and carbon neutral objectives that imply a huge contribution from buildings. High-performance targets, either zero energy or zero carbon, are typically difficult to be reached by single buildings, but groups of properly-managed buildings might reach these ambitious goals. For this purpose we need tools and experiences to model, monitor, manage and optimize buildings and their neighborhood-level systems. The paper describes the activities pursued for the deployment of an advanced energy management system for a multi-carrier energy grid of an existing neighborhood in the area of Milan. The activities included: (i) development of a detailed monitoring plan, (ii) deployment of the monitoring plan, (iii) development of a virtual model of the neighborhood and simulation of the energy performance. Comparisons against early-stage energy monitoring data proved promising and the generation system showed high efficiency (EER equal to 5.84), to be further exploited.

scholarly journals Artificial Intelligence-Assisted Colonoscopy for Detection of Colon Polyps: a Prospective, Randomized Cohort Study

2020 ◽  
Author(s):  
Yuchen Luo ◽  
Yi Zhang ◽  
Ming Liu ◽  
Yihong Lai ◽  
Panpan Liu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
High Performance ◽  
Detection System ◽  
Random Order ◽  
Colon Polyps ◽  
Clinical Environment ◽  
Polyp Detection ◽  
Link Type ◽  
Polyp Detection Rate

Abstract Background and aims Improving the rate of polyp detection is an important measure to prevent colorectal cancer (CRC). Real-time automatic polyp detection systems, through deep learning methods, can learn and perform specific endoscopic tasks previously performed by endoscopists. The purpose of this study was to explore whether a high-performance, real-time automatic polyp detection system could improve the polyp detection rate (PDR) in the actual clinical environment. Methods The selected patients underwent same-day, back-to-back colonoscopies in a random order, with either traditional colonoscopy or artificial intelligence (AI)-assisted colonoscopy performed first by different experienced endoscopists (> 3000 colonoscopies). The primary outcome was the PDR. It was registered with clinicaltrials.gov. (NCT047126265). Results In this study, we randomized 150 patients. The AI system significantly increased the PDR (34.0% vs 38.7%, p < 0.001). In addition, AI-assisted colonoscopy increased the detection of polyps smaller than 6 mm (69 vs 91, p < 0.001), but no difference was found with regard to larger lesions. Conclusions A real-time automatic polyp detection system can increase the PDR, primarily for diminutive polyps. However, a larger sample size is still needed in the follow-up study to further verify this conclusion. Trial Registration clinicaltrials.gov Identifier: NCT047126265

Pattern Recognition in Musculoskeletal Imaging Using Artificial Intelligence

2020 ◽  
Vol 24 (01) ◽  
pp. 38-49 ◽  
Author(s):  
Natalia Gorelik ◽  
Jaron Chong ◽  
Dana J. Lin
Keyword(s):  
Artificial Intelligence ◽  
Pattern Recognition ◽  
Bone Tumors ◽  
Image Interpretation ◽  
Bone Age ◽  
Musculoskeletal Imaging ◽  
Radiology Workflow ◽  
Disease Response ◽  
Osseous Metastases ◽  
Applications Of Ai

AbstractArtificial intelligence (AI) has the potential to affect every step of the radiology workflow, but the AI application that has received the most press in recent years is image interpretation, with numerous articles describing how AI can help detect and characterize abnormalities as well as monitor disease response. Many AI-based image interpretation tasks for musculoskeletal (MSK) pathologies have been studied, including the diagnosis of bone tumors, detection of osseous metastases, assessment of bone age, identification of fractures, and detection and grading of osteoarthritis. This article explores the applications of AI for image interpretation of MSK pathologies.

scholarly journals Corporate Governance of Artificial Intelligence in the Public Interest

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 275
Author(s):  
Peter Cihon ◽  
Jonas Schuett ◽  
Seth D. Baum
Keyword(s):  
Artificial Intelligence ◽  
Corporate Governance ◽  
Law Enforcement ◽  
Nonprofit Organizations ◽  
Public Interest ◽  
Department Of Defense ◽  
The Public ◽  
Wide Range ◽  
The Media ◽  
The U.S

Corporations play a major role in artificial intelligence (AI) research, development, and deployment, with profound consequences for society. This paper surveys opportunities to improve how corporations govern their AI activities so as to better advance the public interest. The paper focuses on the roles of and opportunities for a wide range of actors inside the corporation—managers, workers, and investors—and outside the corporation—corporate partners and competitors, industry consortia, nonprofit organizations, the public, the media, and governments. Whereas prior work on multistakeholder AI governance has proposed dedicated institutions to bring together diverse actors and stakeholders, this paper explores the opportunities they have even in the absence of dedicated multistakeholder institutions. The paper illustrates these opportunities with many cases, including the participation of Google in the U.S. Department of Defense Project Maven; the publication of potentially harmful AI research by OpenAI, with input from the Partnership on AI; and the sale of facial recognition technology to law enforcement by corporations including Amazon, IBM, and Microsoft. These and other cases demonstrate the wide range of mechanisms to advance AI corporate governance in the public interest, especially when diverse actors work together.

Machine learning of serum metabolic patterns encodes early-stage lung adenocarcinoma

2021 ◽  
Author(s):  
Lin Huang ◽  
Kun Qian
Keyword(s):  
Machine Learning ◽  
Lung Adenocarcinoma ◽  
Cancer Detection ◽  
High Performance ◽  
Large Scale ◽  
Early Cancer ◽  
Early Stage ◽  
Early Cancer Detection ◽  
Ionization Mass ◽  
Efficient Test

Abstract Early cancer detection greatly increases the chances for successful treatment, but available diagnostics for some tumours, including lung adenocarcinoma (LA), are limited. An ideal early-stage diagnosis of LA for large-scale clinical use must address quick detection, low invasiveness, and high performance. Here, we conduct machine learning of serum metabolic patterns to detect early-stage LA. We extract direct metabolic patterns by the optimized ferric particle-assisted laser desorption/ionization mass spectrometry within 1 second using only 50 nL of serum. We define a metabolic range of 100-400 Da with 143 m/z features. We diagnose early-stage LA with sensitivity~70-90% and specificity~90-93% through the sparse regression machine learning of patterns. We identify a biomarker panel of seven metabolites and relevant pathways to distinguish early-stage LA from controls (p < 0.05). Our approach advances the design of metabolic analysis for early cancer detection and holds promise as an efficient test for low-cost rollout to clinics.

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