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

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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A deep learning framework for intracranial aneurysms automatic segmentation and detection on magnetic resonance T1 images

This study featured a design of a deep learning-based framework for the automatic segmentation of intracranial aneurysms (IAs) on MR T1 images while also testing the robustness and performance of the framework. The authors were able to conclude that their deep learning framework could effectively detect and segment IAs using clinical routine T1 sequences, which offers potential in improving the

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Evaluating Radiomics Research Reporting Assessment Tools to Improve Quality and Generalizability

As computational capabilities in healthcare continue to advance, the realm of texture analysis within medical imaging, known as radiomics, offers a promising avenue for uncovering novel imaging biomarkers aiding precision medicine [1].Nevertheless, clinical translation faces significant hurdles, primarily stemming from the heterogeneity of research questions and inconsistent quality of radiomics reporting, leading to a scarcity of studies that are comparable,

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Complexities of deep learning-based undersampled MR image reconstruction

Recent advances in AI have led to deep learning-based MR undersampled image reconstruction methods showing more speed-ups compared to traditional algorithms. MR undersampling is an excellent way to reduce scan time but can negatively impact image quality. Our literature review aims to inform a broader audience about this complex topic. This highly multidisciplinary science requires the informed input of many

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Unleashing the power of data in radiology: A look ahead

Peering into the future of radiology, we find ourselves at an exciting crossroads. The past year has seen language-based foundation models disrupt the status quo, offering a tantalising glimpse into new possibilities for data accessibility. Yet we still face a huge challenge: a wealth of valuable data remains locked away in free-text reports. But what if we could unlock this

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Predicting tertiary lymphoid structures status of ICC patients using CT radiomics

The authors of this study used preoperative CT radiomics in order to predict the tertiary lymphoid structures (TLSs) status and recurrence-free survival (RFS) of intrahepatic cholangiocarcinoma (ICC) patients. Enhanced CT images from a total of 116 ICC patients were included when using the radiomics model. The study results showed that the radiomics nomogram displayed better performance in predicting TLSs than

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