European radiology
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Comparative Study
Repeatability and reproducibility of FreeSurfer, FSL-SIENAX and SPM brain volumetric measurements and the effect of lesion filling in multiple sclerosis.
To compare the cross-sectional robustness of commonly used volumetric software and effects of lesion filling in multiple sclerosis (MS). ⋯ • The same scanner should be used for brain volumetry. If different scanners are used, the intracranial volume normalisation improves the FreeSurfer and SPM robustness (but not the FSL scaling factor). • FreeSurfer, FSL and SPM all provide robust measures of the whole brain volume on the same MRI scanner. SPM-based methods overall provide the most robust segmentations (except white matter segmentations on different scanners where FreeSurfer is more robust). • MS lesion filling with Lesion Segmentation Toolbox changes the output of FSL-SIENAX and SPM. FreeSurfer output is not affected by MS lesion filling since it already takes white matter hypointensities into account and is therefore particularly suitable for MS brain volumetry.
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Randomized Controlled Trial Multicenter Study
A randomized controlled trial of digital breast tomosynthesis versus digital mammography in population-based screening in Bergen: interim analysis of performance indicators from the To-Be trial.
To describe a randomized controlled trial (RCT) of digital breast tomosynthesis including synthesized two-dimensional mammograms (DBT) versus digital mammography (DM) in a population-based screening program for breast cancer and to compare selected secondary screening outcomes for the two techniques. ⋯ • In this RCT, DBT was associated with longer interpretation time than DM • Recall rates were lower for DBT than for DM • Mean glandular radiation dose did not differ between DBT and DM.
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The recent explosion of 'big data' has ushered in a new era of artificial intelligence (AI) algorithms in every sphere of technological activity, including medicine, and in particular radiology. However, the recent success of AI in certain flagship applications has, to some extent, masked decades-long advances in computational technology development for medical image analysis. In this article, we provide an overview of the history of AI methods for radiological image analysis in order to provide a context for the latest developments. ⋯ We discuss the unique characteristics of medical data and medical science that set medicine apart from other technological domains in order to highlight not only the potential of AI in radiology but also the very real and often overlooked constraints that may limit the applicability of certain AI methods. Finally, we provide a comprehensive perspective on the potential impact of AI on radiology and on how to evaluate it not only from a technical point of view but also from a clinical one, so that patients can ultimately benefit from it. KEY POINTS: • Artificial intelligence (AI) research in medical imaging has a long history • The functioning, strengths and limitations of more classical AI methods is reviewed, together with that of more recent deep learning methods. • A perspective is provided on the potential impact of AI on radiology and on its evaluation from both technical and clinical points of view.
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Multicenter Study Clinical Trial
Magnetic resonance enterography, small bowel ultrasound and colonoscopy to diagnose and stage Crohn's disease: patient acceptability and perceived burden.
To compare patient acceptability and burden of magnetic resonance enterography (MRE) and ultrasound (US) to each other, and to other enteric investigations, particularly colonoscopy. ⋯ • MRE and US are rated as acceptable by most patients and superior to colonoscopy. • MRE generates significantly greater burden and longer recovery times than US, particularly in younger patients and those with high levels of emotional distress. • Most patients prefer the experience of undergoing US than MRE; however, patients rate test accuracy as more importance than scan burden.
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Developmental dysplasia of the hip (DDH) diagnosis by two-dimensional ultrasound (2DUS) can have poor inter-rater reliability. 3D ultrasound (3DUS) may be more reliably performed, particularly by novice users. We compared intra- and inter-rater reliability between expert and novice operators performing 2DUS and 3DUS for DDH. ⋯ • Novice/expert inter-rater reliability improved from poor with 2DUS to moderate/high with 3DUS. • Novice operators using 3DUS correctly classified 57/58 (98%) of infant hips. • DDH can be reliably assessed by novice operators using 3DUS.