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3.
Radiologe ; 60(1): 6-14, 2020 Jan.
Artículo en Alemán | MEDLINE | ID: mdl-31915840

RESUMEN

METHODICAL ISSUE: Machine learning (ML) algorithms have an increasingly relevant role in radiology tackling tasks such as the automatic detection and segmentation of diagnosis-relevant markers, the quantification of progression and response, and their prediction in individual patients. STANDARD RADIOLOGICAL METHODS: ML algorithms are relevant for all image acquisition techniques from computed tomography (CT) and magnetic resonance imaging (MRI) to ultrasound. However, different modalities result in different challenges with respect to standardization and variability. METHODICAL INNOVATIONS: ML algorithms are increasingly able to analyze longitudinal data for the training of prediction models. This is relevant since it enables the use of comprehensive information for predicting individual progression and response, and the associated support of treatment decisions by ML models. PERFORMANCE: The quality of detection and segmentation algorithms of lesions has reached an acceptable level in several areas. The accuracy of prediction models is still increasing, but is dependent on the availability of representative training data. ACHIEVEMENTS: The development of ML algorithms in radiology is progressing although many solutions are still at a validation stage. It is accompanied by a parallel and increasingly interlinked development of basic methods and techniques which will gradually be put into practice in radiology. PRACTICAL CONSIDERATIONS: Two factors will impact the relevance of ML in radiological practice: the thorough validation of algorithms and solutions, and the creation of representative diverse data for the training and validation in a realistic context.


Asunto(s)
Aprendizaje Automático , Radiología , Algoritmos , Humanos , Terminología como Asunto
4.
Br J Radiol ; 93(1106): 20190855, 2020 Feb 01.
Artículo en Inglés | MEDLINE | ID: mdl-31965813

RESUMEN

Advances in computing hardware and software platforms have led to the recent resurgence in artificial intelligence (AI) touching almost every aspect of our daily lives by its capability for automating complex tasks or providing superior predictive analytics. AI applications are currently spanning many diverse fields from economics to entertainment, to manufacturing, as well as medicine. Since modern AI's inception decades ago, practitioners in radiological sciences have been pioneering its development and implementation in medicine, particularly in areas related to diagnostic imaging and therapy. In this anniversary article, we embark on a journey to reflect on the learned lessons from past AI's chequered history. We further summarize the current status of AI in radiological sciences, highlighting, with examples, its impressive achievements and effect on re-shaping the practice of medical imaging and radiotherapy in the areas of computer-aided detection, diagnosis, prognosis, and decision support. Moving beyond the commercial hype of AI into reality, we discuss the current challenges to overcome, for AI to achieve its promised hope of providing better precision healthcare for each patient while reducing cost burden on their families and the society at large.


Asunto(s)
Inteligencia Artificial/tendencias , Radiología/tendencias , Algoritmos , Técnicas de Apoyo para la Decisión , Predicción , Humanos , Interpretación de Imagen Asistida por Computador , Neoplasias/radioterapia , Radioterapia/tendencias
7.
9.
World Neurosurg ; 133: e874-e892, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31541754

RESUMEN

OBJECTIVE: The increasing challenges facing the training of future neurosurgeons have led to continued development of simulation-based training, particularly for neurosurgical subspecialties. The simulators must be scientifically validated to fully assess their benefit and determine their educational effects. In this second part, we aim to identify the available simulators for spine, pediatric neurosurgery, interventional neuroradiology, and nontechnical skills, assess their validity, and determine their effectiveness. METHODS: Both Medline and Embase were searched for English language articles that validate simulation models for neurosurgery. Each study was screened according to the Messick validity framework, and rated in each domain. The McGaghie model of translational outcomes was then used to determine a level of effectiveness for each simulator or training course. RESULTS: Overall, 114 articles for 108 simulation-based training models or courses were identified. These articles included 24 for spine simulators, 3 for nontechnical skills, 10 for 9 pediatric neurosurgery simulators, and 12 for 11 interventional neuroradiology simulators. Achieving the highest rating for each validity domain were 3 models for content validity; 16 for response processes; 1 for internal structure; 2 for relations to other variables; and only 1 for consequences. For translational outcomes, 2 training courses achieved a level of effectiveness of >2, showing skills transfer beyond the simulator environment. CONCLUSIONS: With increasing simulators, there is a need for more validity studies and attempts to investigate translational outcomes to the operating theater when using these simulators. Nontechnical skills training is notably lacking, despite demand within the field.


Asunto(s)
Competencia Clínica , Procedimientos Neuroquirúrgicos/educación , Entrenamiento Simulado/métodos , Médula Espinal/cirugía , Columna Vertebral/cirugía , Niño , Humanos , Radiología/educación
10.
Radiologe ; 60(1): 64-69, 2020 Jan.
Artículo en Alemán | MEDLINE | ID: mdl-31828383

RESUMEN

CLINICAL/METHODOLOGICAL ISSUE: Artificial intelligence (AI) is being increasingly used in the field of radiology. The aim of this review is to illustrate the developments expected in the next 5 to 10 years as well as possible advantages and risks. STANDARD RADIOLOGICAL METHODS: Currently, all computed tomography (CT) images are reconstructed using programmed algorithms. Pathologies are detected by the radiologist with a high expenditure of time and evaluated using standardized procedures. METHODOLOGICAL INNOVATIONS: AI can potentially provide a significant improvement to all these standard procedures in the future. CT reconstructions can be significantly enhanced using generative adversarial networks (GAN). Histology can be evaluated using radiomics or deep learning (DL)-based image analysis and the prognosis of the patient can be predicted highly individualized. PERFORMANCE: The performance of the networks is strongly influenced by data quality and requires extensive validation. The ability and willingness of the manufacturers to integrate these into the existing RIS/PACS systems is also decisive. EVALUATION: AI will have a large impact on the daily clinical work of radiologists. However, publications on the risks of the technology and on adequate validation are still lacking. In addition to opening new fields of application, further research regarding possible risks is warranted. PRACTICAL RECOMMENDATIONS: In the next 5 to 10 years, AI will improve and facilitate work in clinical practice. The integration of the applications into the existing RIS/PACS systems is expected to take place via app stores and/or existing teleradiology networks.


Asunto(s)
Inteligencia Artificial , Radiología , Predicción , Humanos
11.
Radiologe ; 60(1): 32-41, 2020 Jan.
Artículo en Alemán | MEDLINE | ID: mdl-31820014

RESUMEN

CLINICAL ISSUE: The reproducible and exhaustive extraction of information from radiological images is a central task in the practice of radiology. Dynamic developments in the fields of artificial intelligence (AI) and machine learning are introducing new methods for this task. Radiomics is one such method and offers new opportunities and challenges for the future of radiology. METHODOLOGICAL INNOVATIONS: Radiomics describes the quantitative evaluation, interpretation, and clinical assessment of imaging markers in radiological data. Components of a radiomics analysis are data acquisition, data preprocessing, data management, segmentation of regions of interest, computation and selection of imaging markers, as well as the development of a radiomics model used for diagnosis and prognosis. This article explains these components and aims at providing an introduction to the field of radiomics while highlighting existing limitations. MATERIALS AND METHODS: This article is based on a selective literature search with the PubMed search engine. ASSESSMENT: Even though radiomics applications have yet to arrive in routine clinical practice, the quantification of radiological data in terms of radiomics is underway and will increase in the future. This holds the potential for lasting change in the discipline of radiology. Through the successful extraction and interpretation of all the information encoded in radiological images the next step in the direction of a more personalized, future-oriented form of medicine can be taken.


Asunto(s)
Radiología , Predicción , Humanos , Aprendizaje Automático
13.
Int J Med Inform ; 133: 104028, 2020 01.
Artículo en Inglés | MEDLINE | ID: mdl-31775085

RESUMEN

BACKGROUND: Guidelines from the Royal College of Radiologists and National Patient Safety Agency highlight the crucial importance of "fail-safe" alert systems for the communication of critical and significant clinically unexpected results between imaging departments and referring clinicians. Electronic alert systems are preferred, to minimise errors, increase workflow efficiency and improve auditability. To date there is a paucity of evidence on the utility of such systems. We investigated i) how often emailed radiology alerts were acknowledged by referring clinicians, ii) how frequently follow-up imaging was requested when indicated and iii) whether practise improved after an educational intervention. METHODS: 100 cases were randomly selected before and after an educational intervention at a tertiary referral centre in London, where the email-based 'RadAlert' system (Rivendale Systems, UK) has been in operation since May 2017. RESULTS: Following educational intervention, 'accepted' alerts increased from 39% to 56%, 'abandoned' alerts reduced from 55% to 37% and 'declined' alerts decreased from 5% to 3%. There was evidence to confirm that, when indicated, further imaging had been requested for 78% of all alerts, 78% of 'accepted' alerts and 76% of 'abandoned' alerts both before and after educational intervention. CONCLUSIONS: Acknowledgment of report alerts by referring clinicians increased after departmental education / governance meetings. However, a proportion of email alerts remained unacknowledged. It is incumbent on reporting radiologists to be aware that electronic alert systems cannot be solely relied upon and to take the necessary steps to ensure significant and clinically unsuspected findings are relayed to referring clinical teams in a timely manner.


Asunto(s)
Correo Electrónico , Comunicación , Radiología , Flujo de Trabajo
14.
Radiol Med ; 125(3): 296-305, 2020 Mar.
Artículo en Inglés | MEDLINE | ID: mdl-31845091

RESUMEN

The advances in technology have led to a growing trend in population exposure to radiation emerging from the invention of high-dose procedures. It is, for example, estimated that annually 1.2% of cancers are induced by radiological scans in Norway. This study aims to investigate and discuss the frequency and dose trends of radiological examinations in Europe. European Commission (EC) launched projects to gain information for medical exposures in 2004 and 2011. In this study, the European Commission Radiation Protection (RP) reports No. 154 and 180 have been reviewed. The RP 154 countries' data were extracted from both reports, and the average variation trend of the number of examinations and effective doses were studied. According to the results, plain radiography and fluoroscopy witnessed a reduction in the frequency and effective dose per examination. Nevertheless, European collective dose encountered an average increase of 23%, which resulted from a growing tendency for implementation of high-dose procedures such as CT scans and interventional examinations. It is worth noting that most of the CT procedures have undergone an increase in effective dose per examination. Although demand and dose per examination in some radiological procedures (such as intravenous urography (IVU) have been reduced, population collective dose is still rising due to the increasing demand for CT scan procedures. Even though the individual risks are not considerable, it can, in a large scale, threaten the health of the people at the present time. Due to this fact, better justification should be addressed so as to reduce population exposure.


Asunto(s)
Exposición a la Radiación/estadística & datos numéricos , Radiografía Intervencional/tendencias , Radiografía/tendencias , Tomografía Computarizada por Rayos X/tendencias , Europa (Continente)/epidemiología , Fluoroscopía/estadística & datos numéricos , Fluoroscopía/tendencias , Humanos , Neoplasias Inducidas por Radiación/epidemiología , Noruega/epidemiología , Dosis de Radiación , Protección Radiológica , Radiografía/estadística & datos numéricos , Radiografía Intervencional/estadística & datos numéricos , Radiología/tendencias , Tomografía Computarizada por Rayos X/estadística & datos numéricos
16.
Dentomaxillofac Radiol ; 49(1): 20190107, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31386555

RESUMEN

OBJECTIVES: To investigate the current clinical applications and diagnostic performance of artificial intelligence (AI) in dental and maxillofacial radiology (DMFR). METHODS: Studies using applications related to DMFR to develop or implement AI models were sought by searching five electronic databases and four selected core journals in the field of DMFR. The customized assessment criteria based on QUADAS-2 were adapted for quality analysis of the studies included. RESULTS: The initial electronic search yielded 1862 titles, and 50 studies were eventually included. Most studies focused on AI applications for an automated localization of cephalometric landmarks, diagnosis of osteoporosis, classification/segmentation of maxillofacial cysts and/or tumors, and identification of periodontitis/periapical disease. The performance of AI models varies among different algorithms. CONCLUSION: The AI models proposed in the studies included exhibited wide clinical applications in DMFR. Nevertheless, it is still necessary to further verify the reliability and applicability of the AI models prior to transferring these models into clinical practice.


Asunto(s)
Inteligencia Artificial , Radiografía Dental , Radiología , Algoritmos , Inteligencia Artificial/normas , Inteligencia Artificial/tendencias , Humanos , Radiografía Dental/métodos , Radiografía Dental/tendencias , Reproducibilidad de los Resultados
17.
Eur Radiol ; 30(1): 501-503, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31359123

RESUMEN

KEY POINTS: • Communication with patients in radiology is, in general, indirect using the referrer as a conduit. • Direct patient communication may be beneficial for radiology departments and radiologists to improve patient awareness about the nature of our role and also to provide correct and measured information about the nature and frequency of discrepancies in radiology.


Asunto(s)
Comunicación , Relaciones Médico-Paciente , Radiólogos/psicología , Radiología/organización & administración , Humanos , Servicio de Radiología en Hospital/organización & administración
18.
Eur Radiol ; 30(1): 482-486, 2020 Jan.
Artículo en Inglés | MEDLINE | ID: mdl-31428826

RESUMEN

OBJECTIVE: To investigate whether there is a difference in citation rate between open access and subscription access articles in the field of radiology. METHODS: This study included consecutive original articles published online in European Radiology. Pearson χ2, Fisher's exact, and Mann-Whitney U tests were used to assess for any differences between open access and subscription access articles. Linear regression analysis was performed to determine the association between open access publishing and citation rate, adjusted for continent of origin, subspeciality, study findings in article title, number of authors, number of references, length of the article, and number of days the article has been online. In a secondary analysis, we determined the association between open access and number of downloads and shares. RESULTS: A total of 500 original studies, of which 86 (17.2%) were open access and 414 (82.8%) were subscription access articles, were included. Articles from Europe or North America were significantly more frequently published open access (p = 0.024 and p = 0.001), while articles with corresponding authors from Asia were significantly less frequently published open access (p < 0.001). In adjusted linear regression analysis, open access articles were significantly more frequently cited (beta coefficient = 3.588, 95% confidence interval [CI] 0.668 to 6.508, p = 0.016), downloaded (beta coefficient = 759.801, 95% CI 630.917 to 888.685, p < 0.001), and shared (beta coefficient = 0.748, 95% CI 0.124 to 1.372, p = 0.019) than subscription access articles (beta coefficient = 3.94, 95% confidence interval 1.44 to 6.44, p = 0.002). CONCLUSION: Open access publishing is independently associated with an increased citation, download, and share rate in the field of radiology. KEY POINTS: • A minority of articles are currently published open access in European Radiology. • European and North American authors tend to publish more open access articles than Asian authors. • Open access publishing seems to offer an independent advantage in terms of citation, download, and share rate.


Asunto(s)
Factor de Impacto de la Revista , Publicación de Acceso Abierto/estadística & datos numéricos , Publicaciones Periódicas como Asunto/estadística & datos numéricos , Radiología/estadística & datos numéricos , Acceso a la Información , Asia , Bibliometría , Europa (Continente) , Humanos , Modelos Lineales , América del Norte , Edición/estadística & datos numéricos
19.
Harefuah ; 158(12): 807-811, 2019 Dec.
Artículo en Hebreo | MEDLINE | ID: mdl-31823536

RESUMEN

INTRODUCTION: Lumbar disc herniation is a medical condition in which obscurity exists in the relation between the clinical and the radiological definition. The following paper was written by both surgeons and a radiologist, who are engaged in the field of spine surgery. The aim is to provide clear definitions as to the different pathologies involving disc herniation. The secondary goal of this article is to differentiate between the radiological picture and the clinical syndrome which are not necessarily connected. We hope this review will illuminate these issues and simplify the definitions and make it easier for all to use, primary care practitioners, general orthopedics and trauma care providers.


Asunto(s)
Desplazamiento del Disco Intervertebral , Vértebras Lumbares , Humanos , Degeneración del Disco Intervertebral , Radiografía , Radiología
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