deep learning

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Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm

European Radiology March 2023

This retrospective study evaluated deep learning algorithms for the detection of automatic rib fracture on thoracic CT scans. The authors also aimed to compare its performance with attending-level radiologists using an internal dataset of 12,208 ER trauma patients and an external dataset of 1,613 ER trauma patients taking chest CT scans. The study showed that the proposed deep learning model

Prediction of lipomatous soft tissue malignancy on MRI

European Radiology Experimental February 2023

This study, which included a cohort of 145 patients affected by lipomatous soft tissue tumours, aimed to compare the performances of MRI radiomic machine learning analysis with that of deep learning in order to predict malignancy in patients with lipomas oratypical lipomatous tumours. The authors were able to show that batch-effect corrected machine learning and radiomics approaches outperformed deep learning-based

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

European Radiology January 2023

The aim of this study was to investigate the effect of deep learning image reconstruction (DLIR) on the accuracy of iodine quantification and image quality of dual-energy CT (DECT) compared to other reconstruction algorithms. The authors showed that the DLIR algorithm reduced image noise and variability of iodine concentration values when compared with other reconstruction algorithms. Key points In the

Radiologists with and without deep learning–based computer-aided diagnosis

Shoji Kido October 2022

When radiologists encounter pulmonary nodules/masses in computed tomography (CT) images, they diagnose malignancy based on lesion characteristics (e.g., spiculation and calcification). However, accurate characterization requires careful observation and can be difficult, especially for inexperienced radiologists. In addition, the assessments may vary among radiologists, resulting in the low reproductivity of findings. We investigated if commercially-available deep learning (DL)-based computer-aided diagnosis (CAD)

Deep learning detection of ACL tear on knee MRI

European Radiology September 2022

The purpose of this study was to develop a deep-learning algorithm for tear detection in the anterior cruciate ligament (ACL), subsequently comparing its accuracy using two external datasets. The authors were able to conclude that their algorithm was capable of showing high performance in the detection of ACL tears. Key points An algorithm for detecting anterior cruciate ligament ruptures was

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies

European Radiology September 2022

Due to the rare nature of musculoskeletal malignancies and the lack of imaging data associated with this cancer, the authors of this study aimed to investigate whether machine learning (ML) is already having an impact on imaging-driven diagnosis of musculoskeletal malignancies. The authors concluded that because of the limited amounts of data and no established large-scale networks between multiple national

How well does a 3D convolutional neural network perform in detecting hypoperfusion?

European Radiology Experimental July 2022

Due to the life-threatening nature of chronic pulmonary embolism (CPE) and how easily it can be misdiagnosed on computed tomography, the authors of this study investigated a three-dimensional convolutional neural network (CNN) algorithm for detecting hypoperfusion in CPE from computed tomography pulmonary angiography (CTPA). This study demonstrated the feasibility of a deep learning algorithm for detecting hypoperfusion in CPE from

Clinical applications of AI and radiomics in neuro-oncology

Insights into Imaging May 2022

In this educational review, the authors take a comprehensive look at various aspects and applications of artificial intelligence (AI) in the field of neuro-oncology, including machine learning, deep learning, and radiomics. The merits and challenges of the deployment and use of AI tools in neuro-oncology are put under the microscope, with the authors concluding that AI has a promising future

A deep learning algorithm for VHD diagnosis and evaluation

European Radiology April 2022

This study aims to develop and validate a deep learning-based automatic chest radiograph (CXR) cardiovascular border (CB) analysis algorithm (CB_auto) in order to diagnose and quantitatively evaluate valvular heart disease (VHD). The authors found that the CB_auto system, in coordination with the deep learning algorithm, provided highly reliable CB measurements, which, in turn, can be useful, not long daily clinical

Deep learning radiomics in prediction of NAC response

European Radiology April 2022

For breast cancer, the standard of treatment for most patients is neoadjuvant chemotherapy (NAC), but response rates may vary among patients, causing delays in appropriate treatment. The authors of this prospective study aimed to investigate the feasibility of deep learning radiomics (DLR) in the prediction of NAC response at an early stage of breast cancer treatment. The authors found that

Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm

European Radiology March 2023

Prediction of lipomatous soft tissue malignancy on MRI

European Radiology Experimental February 2023

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

European Radiology January 2023

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

Latest posts

Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm

Prediction of lipomatous soft tissue malignancy on MRI

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

Radiologists with and without deep learning–based computer-aided diagnosis

Deep learning detection of ACL tear on knee MRI

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies

How well does a 3D convolutional neural network perform in detecting hypoperfusion?

Clinical applications of AI and radiomics in neuro-oncology

A deep learning algorithm for VHD diagnosis and evaluation

Deep learning radiomics in prediction of NAC response

Assessment of automatic rib fracture detection on chest CT using a deep learning algorithm

Prediction of lipomatous soft tissue malignancy on MRI

Deep learning image reconstruction to improve accuracy of iodine quantification and image quality in abdomen dual-energy CT

Radiologists with and without deep learning–based computer-aided diagnosis

Deep learning detection of ACL tear on knee MRI

Applications of machine learning for imaging-driven diagnosis of musculoskeletal malignancies

How well does a 3D convolutional neural network perform in detecting hypoperfusion?

Clinical applications of AI and radiomics in neuro-oncology

A deep learning algorithm for VHD diagnosis and evaluation

Deep learning radiomics in prediction of NAC response

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deep learning

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

Latest posts

Become A Member Today!

Check out our different membership options.

Membership

Free membership

ESR Friends

Footnotes:

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