AI Blog

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

Shallow and deep learning classifiers in medical image analysis

The authors of this review aimed to give educational insight into the most accessible and widely employed classifiers in the field of radiology, distinguish between “shallow” learning algorithms, as well as look into “deep” learning architectures such as convolutional neural networks and vision transformers. This review found that machine learning classifiers offer vital information for the development of clinical decision

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Deep learning-based 3D cerebrovascular segmentation workflow on bright and black blood sequences MRA

Cerebrovascular diseases are seen as a significant threat to human life and health, and the segmentation of brain blood vessels has become a scientific challenge. Therefore, the authors of this study aimed to develop a fully automated deep learning workflow capable of accurate 3D segmentation of cerebral blood vessels using convolutional neural networks (CNNs) and transformer models. The study, conducted

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AI applications in musculoskeletal imaging

As artificial intelligence (AI) tools and technologies become more ubiquitous in radiology, the role they play in the day-to-day work of radiologists is gaining importance. This narrative review provides an overview of the clinical applications of AI in musculoskeletal imaging, diving into specific areas of musculoskeletal disorders including trauma, bone age estimation, bone and soft-tissue tumors, and orthopedic implant-related pathology.

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Novel reporting workflow for automated integration of AI results into structured radiology reports

Although artificial intelligence (AI) shows great potential to help radiologists in their daily clinical routine, integrating AI into the radiology workflow is often lacking, underutilizing its full potential. Therefore, this study aimed to develop a new reporting pipeline enabling automated pre-population of structured reports with results provided by commercially available AI tools. The authors successfully demonstrated that the AI to

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AI in immunotherapy PET/SPECT imaging

Molecular medical imaging, with technologies like PET and SPECT, plays a crucial role in oncology for diagnosis and treatment tracking. Yet, interpreting these images, especially in gauging immunotherapy responses, presents challenges due to new patterns of response and progression as well as inherent subjectivity in image interpretation. While there’s a clear drive to harness artificial intelligence (AI) for improved clinical

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Sharing is Caring – Promoting Radiomics Research Transparency and Sustainability

Radiomics, a rapidly growing and innovative field in medical imaging, extracts detailed features from medical scans that could play a pivotal future adjunct role in patient care. However, the clinical adoption of radiomics is stalled by significant research heterogeneity and reproducibility issues [1]. The complex radiomics pipeline, requiring multidisciplinary expertise, often lacks transparency regarding sharing crucial details like software tools

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Knee landmarks detection via deep learning

A deep learning-based approach was developed and validated in this study which aimed to automatically measure the patellofemoral instability (PFI) indices related to patellar height and trochlear dysplasia in knee MRI scans. The authors included a total of 763 knee MRI slices from 95 patients, annotating 3,393 anatomical landmarks. The results indicated that the developed models achieved good accuracy in

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Coronary CT angiographic detection of in-stent restenosis via deep learning reconstruction

This feasibility study used deep learning reconstruction, Precise IQ Engine (PIQE), to quantify stent strut thickness and lumen vessel diameter, subsequently comparing it with values obtained using conventional reconstruction methods. The authors conducting this study examined 166 stents in 85 consecutive patients who had undergone CT and invasive coronary angiography (ICA) within 3 months of each other. The results demonstrated

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Deep Learning-based framework shown as a valuable tool to determine the composition of thyroid nodules

The authors of this retrospective multicenter study proposed a deep learning-based framework to identify the composition of thyroid nodules while also assessing their malignancy risk. Their research demonstrated that convolutional neural networks (CNNs) were able to assist in the diagnosis of thyroid nodules and reduce the rate of unnecessary fine-needle aspiration. Key points: Article: Deep learning to assist composition classification

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Using PET/CT radiomics in the preoperative prediction of clinical and pathological stages for esophageal cancer patients

In this study, the feasibility and accuracy of PET-CT-based radiomics in preoperative prediction of clinical and pathological stages for esophageal cancer (EC) patients was investigated using a sample of 100 EC patients. The authors observed accurate prediction ability with combined PET and CT radiomics in the prediction of T stage, lymph node metastasis (LNM), and pstage, showing that PET-CT-based radiomics

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