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Back to main AI Blog

deep learning

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.

Most used hashtags:

machine learningdeep learningradiomicsmrix-ray computed tomographyai newsCOVID-19interviewmammographybreast cancerneural networksdiagnostic imagingtomographyMDCTradiographyworkflow

Latest posts

Deep learning assesses additional radiation dose in overscanning

Yazdan Salimi March 2022

Following the COVID-19 pandemic, the number of chest CT examinations has dramatically increased, which will undeniably impact public medical exposure. Overscanning, i.e., scanning unnecessary regions in the axial field-of-view, causes noticeable excessive radiation dose to patients undergoing chest CT examinations. The manual procedure of selecting the scan range based on anterior-posterior or lateral localizers is prone to human error in

Read More →

Integrated AI model aids ultrasonographers

European Radiology February 2022

In this study, the authors aimed to develop an explainable ultrasound (US) computer-assisted diagnostic (CAD) model for suspicious thyroid nodules by retrospectively analyzing over 2,900 solid or almost-solid thyroid nodules. A deep learning model and a multiple risk features learning ensemble model were then used to train the US images of 2,794 thyroid nodules. An integrated AI model was generated

Read More →

Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI

Zheng-Han Yang February 2022

We investigated a saliency-based 3D convolutional neural network (CNN) to classify seven categories of common focal liver lesions and validated the model performance. This retrospective study included 557 lesions examined by multisequence MRI. We found that this interpretable deep learning model showed high diagnostic performance in the differentiation of common liver masses on multisequence MRI. A few important notes on

Read More →

Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Henkjan Huisman January 2022

Prostate MRI can be a game-changer for many men with elevated prostate-specific antigen (PSA). For decades these many men underwent biopsies while never developing prostate cancer. Expert prostate MRI can help avoid these unnecessary biopsies and better target any biopsies. Unfortunately, reading prostate MRI is challenging and time-consuming. Like other medical imaging modalities, AI is explored for helping read prostate

Read More →

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

European Radiology January 2022

The authors of this study aimed to develop an artificial intelligence (AI)-based fully automated CT image analysis system in order to detect and diagnose pulmonary tuberculosis (TB). This was achieved through the retrospective use of 892 chest CT scans from pathogen-confirmed TB patients. It was found that the end-to-end AI system based on chest CT is able to achieve human-level

Read More →

Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke

European Radiology Experimental December 2021

The authors of this study aimed to determine the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from computed tomography angiography (CTA), subsequently comparing the results to a CT perfusion (CTP)-based commercially available software. The stroke cases treated with thrombolytic therapy or receiving supportive care were retrospectively selected by the authors. The study found that a

Read More →

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

European Radiology December 2021

This retrospective, multi-institutional study investigated machine learning classifiers and interpretable models using chest CT for the detection of COVID-19 and to differentiate this from types of pneumonia, interstitial lung disease (ILD), and normal CTs. The study included 2,446 chest CTs from across 16 different institutions and the authors’ method was found to accurately differentiate COVID-19 from other types of pneumonia,

Read More →

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest

European Radiology November 2021

The aim of this study was to compare the performance of a deep learning (DL)-based method used for diagnosing pulmonary nodules compared with the diagnostic approach of the radiologist in computed tomography (CT) of the chest. The authors included a total of 150 pathologically confirmed pulmonary nodules that were assessed and reported by radiologists. The study found that the DL-based

Read More →

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

Anwar Padhani October 2021

The clinical promise of artificial intelligence (AI) in prostate cancer diagnosis has yet to materialize. Any AI application must reach an appropriate level of maturity and robustness for such developments to be accepted by its intended users. Our position paper, “Development of Artificial Intelligence for Precision Diagnosis of Prostate Cancer Using MRI”, co-authored by experts from ESUR and ESUI, elaborated

Read More →

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning

European Radiology October 2021

The purpose of this study was to evaluate the performance of deep learning using ResNet50 in the differentiation of benign and malignant vertebral fracture on computed tomography (CT). The study used a dataset of 433 patients, which was retrospectively selected from the authors’ spinal CT image database. The authors concluded that ResNet50 achieved good accuracy, which can be further improved

Read More →

Deep learning assesses additional radiation dose in overscanning

Yazdan Salimi March 2022

Following the COVID-19 pandemic, the number of chest CT examinations has dramatically increased, which will undeniably impact public medical exposure. Overscanning, i.e., scanning unnecessary regions in the axial field-of-view, causes noticeable excessive radiation dose to patients undergoing chest CT examinations. The manual procedure of selecting the scan range based on anterior-posterior or lateral localizers is prone to human error in

Read More →

Integrated AI model aids ultrasonographers

European Radiology February 2022

In this study, the authors aimed to develop an explainable ultrasound (US) computer-assisted diagnostic (CAD) model for suspicious thyroid nodules by retrospectively analyzing over 2,900 solid or almost-solid thyroid nodules. A deep learning model and a multiple risk features learning ensemble model were then used to train the US images of 2,794 thyroid nodules. An integrated AI model was generated

Read More →

Saliency-based 3D convolutional neural network for categorising common focal liver lesions on multisequence MRI

Zheng-Han Yang February 2022

We investigated a saliency-based 3D convolutional neural network (CNN) to classify seven categories of common focal liver lesions and validated the model performance. This retrospective study included 557 lesions examined by multisequence MRI. We found that this interpretable deep learning model showed high diagnostic performance in the differentiation of common liver masses on multisequence MRI. A few important notes on

Read More →

Deep learning–assisted prostate cancer detection on bi-parametric MRI: minimum training data size requirements and effect of prior knowledge

Henkjan Huisman January 2022

Prostate MRI can be a game-changer for many men with elevated prostate-specific antigen (PSA). For decades these many men underwent biopsies while never developing prostate cancer. Expert prostate MRI can help avoid these unnecessary biopsies and better target any biopsies. Unfortunately, reading prostate MRI is challenging and time-consuming. Like other medical imaging modalities, AI is explored for helping read prostate

Read More →

A fully automatic artificial intelligence–based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis

European Radiology January 2022

The authors of this study aimed to develop an artificial intelligence (AI)-based fully automated CT image analysis system in order to detect and diagnose pulmonary tuberculosis (TB). This was achieved through the retrospective use of 892 chest CT scans from pathogen-confirmed TB patients. It was found that the end-to-end AI system based on chest CT is able to achieve human-level

Read More →

Evaluation of a CTA-based convolutional neural network for infarct volume prediction in anterior cerebral circulation ischaemic stroke

European Radiology Experimental December 2021

The authors of this study aimed to determine the efficacy of a convolutional neural network (CNN) in final infarct volume prediction from computed tomography angiography (CTA), subsequently comparing the results to a CT perfusion (CTP)-based commercially available software. The stroke cases treated with thrombolytic therapy or receiving supportive care were retrospectively selected by the authors. The study found that a

Read More →

Machine learning automatically detects COVID-19 using chest CTs in a large multicenter cohort

European Radiology December 2021

This retrospective, multi-institutional study investigated machine learning classifiers and interpretable models using chest CT for the detection of COVID-19 and to differentiate this from types of pneumonia, interstitial lung disease (ILD), and normal CTs. The study included 2,446 chest CTs from across 16 different institutions and the authors’ method was found to accurately differentiate COVID-19 from other types of pneumonia,

Read More →

Proposing a deep learning-based method for improving the diagnostic certainty of pulmonary nodules in CT scan of chest

European Radiology November 2021

The aim of this study was to compare the performance of a deep learning (DL)-based method used for diagnosing pulmonary nodules compared with the diagnostic approach of the radiologist in computed tomography (CT) of the chest. The authors included a total of 150 pathologically confirmed pulmonary nodules that were assessed and reported by radiologists. The study found that the DL-based

Read More →

ESUR/ESUI position paper: developing artificial intelligence for precision diagnosis of prostate cancer using magnetic resonance imaging

Anwar Padhani, Tobias Penzkofer October 2021

The clinical promise of artificial intelligence (AI) in prostate cancer diagnosis has yet to materialize. Any AI application must reach an appropriate level of maturity and robustness for such developments to be accepted by its intended users. Our position paper, “Development of Artificial Intelligence for Precision Diagnosis of Prostate Cancer Using MRI”, co-authored by experts from ESUR and ESUI, elaborated

Read More →

Differential diagnosis of benign and malignant vertebral fracture on CT using deep learning

European Radiology October 2021

The purpose of this study was to evaluate the performance of deep learning using ResNet50 in the differentiation of benign and malignant vertebral fracture on computed tomography (CT). The study used a dataset of 433 patients, which was retrospectively selected from the authors’ spinal CT image database. The authors concluded that ResNet50 achieved good accuracy, which can be further improved

Read More →

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  • Exclusive access to the ESR feed in Juisci

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  • Reduced registration fees for ECR 1
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  • Content e-mails for all 3 ESR journals 4
  • Updates on offers & events through our newsletters
  • Exclusive access to the ESR feed in Juisci

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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.

Reduced registration fees for ECR 2025:
Provided that ESR 2024 membership is activated and approved by August 31, 2024.

02
Not all activities included
03
Examination based on the ESR European Training Curriculum (radiologists or radiology residents).
04
European Radiology, Insights into Imaging, European Radiology Experimental.