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1.
J Digit Imaging ; 35(2): 335-339, 2022 04.
Artigo em Inglês | MEDLINE | ID: mdl-35018541

RESUMO

Preparing radiology examinations for interpretation requires prefetching relevant prior examinations and implementing hanging protocols to optimally display the examination along with comparisons. Body part is a critical piece of information to facilitate both prefetching and hanging protocols, but body part information encoded using the Digital Imaging and Communications in Medicine (DICOM) standard is widely variable, error-prone, not granular enough, or missing altogether. This results in inappropriate examinations being prefetched or relevant examinations left behind; hanging protocol optimization suffers as well. Modern artificial intelligence (AI) techniques, particularly when harnessing federated deep learning techniques, allow for highly accurate automatic detection of body part based on the image data within a radiological examination; this allows for much more reliable implementation of this categorization and workflow. Additionally, new avenues to further optimize examination viewing such as dynamic hanging protocol and image display can be implemented using these techniques.


Assuntos
Inteligência Artificial , Aprendizado Profundo , Corpo Humano , Humanos , Radiografia , Fluxo de Trabalho
2.
Radiology ; 299(1): E204-E213, 2021 04.
Artigo em Inglês | MEDLINE | ID: mdl-33399506

RESUMO

The coronavirus disease 2019 (COVID-19) pandemic is a global health care emergency. Although reverse-transcription polymerase chain reaction testing is the reference standard method to identify patients with COVID-19 infection, chest radiography and CT play a vital role in the detection and management of these patients. Prediction models for COVID-19 imaging are rapidly being developed to support medical decision making. However, inadequate availability of a diverse annotated data set has limited the performance and generalizability of existing models. To address this unmet need, the RSNA and Society of Thoracic Radiology collaborated to develop the RSNA International COVID-19 Open Radiology Database (RICORD). This database is the first multi-institutional, multinational, expert-annotated COVID-19 imaging data set. It is made freely available to the machine learning community as a research and educational resource for COVID-19 chest imaging. Pixel-level volumetric segmentation with clinical annotations was performed by thoracic radiology subspecialists for all COVID-19-positive thoracic CT scans. The labeling schema was coordinated with other international consensus panels and COVID-19 data annotation efforts, the European Society of Medical Imaging Informatics, the American College of Radiology, and the American Association of Physicists in Medicine. Study-level COVID-19 classification labels for chest radiographs were annotated by three radiologists, with majority vote adjudication by board-certified radiologists. RICORD consists of 240 thoracic CT scans and 1000 chest radiographs contributed from four international sites. It is anticipated that RICORD will ideally lead to prediction models that can demonstrate sustained performance across populations and health care systems.


Assuntos
COVID-19/diagnóstico por imagem , Bases de Dados Factuais/estatística & dados numéricos , Saúde Global/estatística & dados numéricos , Pulmão/diagnóstico por imagem , Tomografia Computadorizada por Raios X/métodos , Humanos , Internacionalidade , Radiografia Torácica , Radiologia , SARS-CoV-2 , Sociedades Médicas , Tomografia Computadorizada por Raios X/estatística & dados numéricos
3.
J Digit Imaging ; 33(2): 490-496, 2020 04.
Artigo em Inglês | MEDLINE | ID: mdl-31768897

RESUMO

Pneumothorax is a potentially life-threatening condition that requires prompt recognition and often urgent intervention. In the ICU setting, large numbers of chest radiographs are performed and must be interpreted on a daily basis which may delay diagnosis of this entity. Development of artificial intelligence (AI) techniques to detect pneumothorax could help expedite detection as well as localize and potentially quantify pneumothorax. Open image analysis competitions are useful in advancing state-of-the art AI algorithms but generally require large expert annotated datasets. We have annotated and adjudicated a large dataset of chest radiographs to be made public with the goal of sparking innovation in this space. Because of the cumbersome and time-consuming nature of image labeling, we explored the value of using AI models to generate annotations for review. Utilization of this machine learning annotation (MLA) technique appeared to expedite our annotation process with relatively high sensitivity at the expense of specificity. Further research is required to confirm and better characterize the value of MLAs. Our adjudicated dataset is now available for public consumption in the form of a challenge.


Assuntos
Crowdsourcing , Pneumotórax , Inteligência Artificial , Conjuntos de Dados como Assunto , Humanos , Aprendizado de Máquina , Pneumotórax/diagnóstico por imagem , Raios X
4.
Sci Data ; 8(1): 285, 2021 10 28.
Artigo em Inglês | MEDLINE | ID: mdl-34711836

RESUMO

Correct catheter position is crucial to ensuring appropriate function of the catheter and avoid complications. This paper describes a dataset consisting of 50,612 image level and 17,999 manually labelled annotations from 30,083 chest radiographs from the publicly available NIH ChestXRay14 dataset with manually annotated and segmented endotracheal tubes (ETT), nasoenteric tubes (NET) and central venous catheters (CVCs).


Assuntos
Cateterismo , Radiografia Torácica , Tórax/diagnóstico por imagem , Catéteres , Cateteres Venosos Centrais , Humanos , Intubação Gastrointestinal , Intubação Intratraqueal
8.
Radiol Artif Intell ; 1(1): e180041, 2019 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-33937785

RESUMO

This dataset is intended to be used for machine learning and is composed of annotations with bounding boxes for pulmonary opacity on chest radiographs which may represent pneumonia in the appropriate clinical setting.

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