computer-assisted diagnosis

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

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

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)

Read More →

Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network

The aim of this study was to develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE) on chest radiographs. The authors determined that the DLCE-LAE was able to outperform and improve the performance of cardiothoracic radiologists in the detection of LAE, while also showing promise in screening individuals with moderate-to-severe LAE in a healthcare

Read More →

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

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 →

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 →

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

In this study, the authors retrospectively collected 2,088 abnormal and 352 normal chest radiographs from two institutions in order to investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. This resulted in the matrix size 896 as having the highest performance for various sizes of abnormalities using different convolutional neural

Read More →

MRI radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study

This study aimed to assess whether MRI radiomics can categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer patients. The authors evaluated the diagnostic performance of the signatures derived from MRI radiomics in 286 patients with proven adnexal tumor. The study results suggest a correlation between radiomics features extracted from MRI and

Read More →

Using machine learning to predict cervical lymph node metastasis on dual-energy CT

In this article, we extracted “hand-crafted” radiomic features from dual-energy CT (DECT) virtual monochromatic images (VMIs) reconstructed at different energies and used machine learning to construct prediction models that use the radiomic features of head and neck squamous cell carcinoma (HNSCC) to predict associated nodal metastases. This proof of concept study demonstrated that (1) HNSCC radiomic features can predict associated

Read More →

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

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)

Read More →

Automatic prediction of left cardiac chamber enlargement from chest radiographs using convolutional neural network

The aim of this study was to develop deep learning-based cardiac chamber enlargement-detection algorithms for left atrial (DLCE-LAE) and ventricular enlargement (DLCE-LVE) on chest radiographs. The authors determined that the DLCE-LAE was able to outperform and improve the performance of cardiothoracic radiologists in the detection of LAE, while also showing promise in screening individuals with moderate-to-severe LAE in a healthcare

Read More →

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

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 →

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 →

Optimal matrix size of chest radiographs for computer-aided detection on lung nodule or mass with deep learning

In this study, the authors retrospectively collected 2,088 abnormal and 352 normal chest radiographs from two institutions in order to investigate the optimal input matrix size for deep learning-based computer-aided detection (CAD) of nodules and masses on chest radiographs. This resulted in the matrix size 896 as having the highest performance for various sizes of abnormalities using different convolutional neural

Read More →

MRI radiomics in categorizing ovarian masses and predicting clinical outcome: a preliminary study

This study aimed to assess whether MRI radiomics can categorize ovarian masses and to determine the association between MRI radiomics and survival among ovarian epithelial cancer patients. The authors evaluated the diagnostic performance of the signatures derived from MRI radiomics in 286 patients with proven adnexal tumor. The study results suggest a correlation between radiomics features extracted from MRI and

Read More →

Using machine learning to predict cervical lymph node metastasis on dual-energy CT

In this article, we extracted “hand-crafted” radiomic features from dual-energy CT (DECT) virtual monochromatic images (VMIs) reconstructed at different energies and used machine learning to construct prediction models that use the radiomic features of head and neck squamous cell carcinoma (HNSCC) to predict associated nodal metastases. This proof of concept study demonstrated that (1) HNSCC radiomic features can predict associated

Read More →

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