mass screening

Welcome to the blog on Artificial Intelligence of
the European Society of Radiology

This blog aims at bringing educational and critical perspectives on AI to readers. It should help imaging professionals to learn and keep up to date with the technologies being developed in this rapidly evolving field.

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Latest posts

Possible strategies for use of AI in screen-reading of mammograms

After publishing the overall performance results of an AI system in Radiology (Artificial Intelligence Evaluation of 122,969 Mammography Examinations from a Population-based Screening Program), we explored different possible strategies for using AI in the screen-reading of mammograms. We presented estimated cancer detection rates for 11 different possible ways of implementing AI mammography screening. However, with different thresholds for selecting cases

Read More →

Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

In this multi-reader, multi-case study, the authors aimed to investigate whether an artificial intelligence (AI) support system could help to increase the accuracy of breast radiologists reading wide-angle digital breast tomosynthesis (DBT). The study was performed using 240 bilateral DBT exams, with the exams interpreted by 18 radiologists with and without AI support. The authors found that the radiologists improved

Read More →

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset

Our study included 519 screening chest radiographs (CXRs) from 294 patients enrolled in the National Lung Screening Trial (NLST) who either had proven to have lung cancer or did not have lung cancer over the duration of the trial. Five attending radiologists and three radiology residents from South Korea and the U.S. independently assessed all CXRs for the presence of

Read More →

The promising possibility of using AI in mammography screening

In two recent publications in European Radiology, we have addressed two, of several, challenges with mammography screening. Firstly, the vast majority of screen exams are normal, which is resource-demanding, especially in the double-reading setting; and secondly, we miss cancer that can be particularly aggressive, later appearing as interval cancer. To understand if artificial intelligence (AI) can identify normal exams, we

Read More →

Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals

Chest radiographs (CRs) have long been used as one of the screening tests for pulmonary tuberculosis (TB). However, the interpretation of a large number of CRs is time-consuming and labor-intensive. To overcome this difficulty, we developed the deep-learning-based automated detection (DLAD) for active pulmonary TB detection and performed out-of-sample testing in the consecutively collected 20.135 CRs from 19.686 servicepersons. As

Read More →

Possible strategies for use of AI in screen-reading of mammograms

After publishing the overall performance results of an AI system in Radiology (Artificial Intelligence Evaluation of 122,969 Mammography Examinations from a Population-based Screening Program), we explored different possible strategies for using AI in the screen-reading of mammograms. We presented estimated cancer detection rates for 11 different possible ways of implementing AI mammography screening. However, with different thresholds for selecting cases

Read More →

Impact of artificial intelligence support on accuracy and reading time in breast tomosynthesis image interpretation: a multi-reader multi-case study

In this multi-reader, multi-case study, the authors aimed to investigate whether an artificial intelligence (AI) support system could help to increase the accuracy of breast radiologists reading wide-angle digital breast tomosynthesis (DBT). The study was performed using 240 bilateral DBT exams, with the exams interpreted by 18 radiologists with and without AI support. The authors found that the radiologists improved

Read More →

AI-based improvement in lung cancer detection on chest radiographs: results of a multi-reader study in NLST dataset

Our study included 519 screening chest radiographs (CXRs) from 294 patients enrolled in the National Lung Screening Trial (NLST) who either had proven to have lung cancer or did not have lung cancer over the duration of the trial. Five attending radiologists and three radiology residents from South Korea and the U.S. independently assessed all CXRs for the presence of

Read More →

The promising possibility of using AI in mammography screening

In two recent publications in European Radiology, we have addressed two, of several, challenges with mammography screening. Firstly, the vast majority of screen exams are normal, which is resource-demanding, especially in the double-reading setting; and secondly, we miss cancer that can be particularly aggressive, later appearing as interval cancer. To understand if artificial intelligence (AI) can identify normal exams, we

Read More →

Deep learning–based automated detection algorithm for active pulmonary tuberculosis on chest radiographs: diagnostic performance in systematic screening of asymptomatic individuals

Chest radiographs (CRs) have long been used as one of the screening tests for pulmonary tuberculosis (TB). However, the interpretation of a large number of CRs is time-consuming and labor-intensive. To overcome this difficulty, we developed the deep-learning-based automated detection (DLAD) for active pulmonary TB detection and performed out-of-sample testing in the consecutively collected 20.135 CRs from 19.686 servicepersons. As

Read More →

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Footnotes:

01

Reduced registration fees for ECR 2024:
Provided that ESR 2023 membership is activated and approved by August 31, 2023.

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04
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