Deep learning analysis of serial digital breast tomosynthesis images in a prospective cohort of breast cancer patients who received neoadjuvant chemotherapy.

Purpose: Different imaging tools, including digital breast tomosynthesis (DBT), are frequently used for evaluating tumor response during neoadjuvant chemotherapy (NACT). This study aimed to explore whether using artificial intelligence (AI) for serial DBT acquisitions during NACT for breast cancer can predict pathological complete response (pCR) after completion of NACT.

Methods: A total of 149 women (mean age 53 years, pCR rate 22 %) with breast cancer treated with NACT at Skane University Hospital, Sweden, between 2014 and 2019, were prospectively included in this observational cohort study (ClinicalTrials.

NoiseNet, a fully automatic noise assessment tool that can identify non-diagnostic CCTA examinations.

Image noise and vascular attenuation are important factors affecting image quality and diagnostic accuracy of coronary computed tomography angiography (CCTA). The aim of this study was to develop an algorithm that automatically performs noise and attenuation measurements in CCTA and to evaluate the ability of the algorithm to identify non-diagnostic examinations. The algorithm, "NoiseNet", was trained and tested on 244 CCTA studies from the Swedish CArdioPulmonary BioImage Study.

Improving sensitivity through data augmentation with synthetic lymph node metastases for AI-based analysis of PSMA PET-CT images.

Background: We developed a fully automated artificial intelligence (AI)AI-based-based method for detecting suspected lymph node metastases in prostate-specific membrane antigen (PSMA)(PSMA) positron emission tomography-computed tomography (PET-CT)(PET-CT) images of prostate cancer patients by using data augmentation that adds synthetic lymph node metastases to the images to expand the training set.

Methods: Synthetic data were derived from original training images to which new synthetic lymph node metastases were added. Thus, the original training set from a previous study (n = 420) was expanded by one synthetic image for every original image (n = 840), which was used to train an AI model.

Brain tumor segmentation using synthetic MR images - A comparison of GANs and diffusion models.

Large annotated datasets are required for training deep learning models, but in medical imaging data sharing is often complicated due to ethics, anonymization and data protection legislation. Generative AI models, such as generative adversarial networks (GANs) and diffusion models, can today produce very realistic synthetic images, and can potentially facilitate data sharing. However, in order to share synthetic medical images it must first be demonstrated that they can be used for training different networks with acceptable performance.

Manual prostate MRI segmentation by readers with different experience: a study of the learning progress.

Objective: To evaluate the learning progress of less experienced readers in prostate MRI segmentation.

Materials And Methods: One hundred bi-parametric prostate MRI scans were retrospectively selected from the Göteborg Prostate Cancer Screening 2 Trial (single center). Nine readers with varying degrees of segmentation experience were involved: one expert radiologist, two experienced radiology residents, two inexperienced radiology residents, and four novices.

Pre-diabetes is associated with attenuation rather than volume of epicardial adipose tissue on computed tomography.

The volume of epicardial adipose tissue (EATV) is increased in type-2 diabetes (T2D), while its attenuation (EATA) appears to be decreased. Similar patterns have been suggested in pre-diabetes, but data is scarce. In both pre-diabetes and T2D, any independent role of EATV and EATA in disease development remains to be proven, a task complicated by their substantial co-variation with other anthropometrics, e.

Automated quantification of PET/CT skeletal tumor burden in prostate cancer using artificial intelligence: The PET index.

Purpose: Consistent assessment of bone metastases is crucial for patient management and clinical trials in prostate cancer (PCa). We aimed to develop a fully automated convolutional neural network (CNN)-based model for calculating PET/CT skeletal tumor burden in patients with PCa.

Methods: A total of 168 patients from three centers were divided into training, validation, and test groups.

Common carotid segmentation in F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based and manual method.

Background: Carotid atherosclerosis is a major cause of stroke, traditionally diagnosed late. Positron emission tomography/computed tomography (PET/CT) with F-sodium fluoride (NaF) detects arterial wall micro-calcification long before macro-calcification becomes detectable by ultrasound, CT or magnetic resonance imaging. However, manual PET/CT processing is time-consuming and requires experience.

PET/CT imaging of spinal inflammation and microcalcification in patients with low back pain: A pilot study on the quantification by artificial intelligence-based segmentation.

Background: Current imaging modalities are often incapable of identifying nociceptive sources of low back pain (LBP). We aimed to characterize these by means of positron emission tomography/computed tomography (PET/CT) of the lumbar spine region applying tracers F-fluorodeoxyglucose (FDG) and F-sodium fluoride (NaF) targeting inflammation and active microcalcification, respectively.

Methods: Using artificial intelligence (AI)-based quantification, we compared PET findings in two sex- and age-matched groups, a case group of seven males and five females, mean age 45 ± 14 years, with ongoing LBP and a similar control group of 12 pain-free individuals.

Artificial intelligence based automatic quantification of epicardial adipose tissue suitable for large scale population studies.

To develop a fully automatic model capable of reliably quantifying epicardial adipose tissue (EAT) volumes and attenuation in large scale population studies to investigate their relation to markers of cardiometabolic risk. Non-contrast cardiac CT images from the SCAPIS study were used to train and test a convolutional neural network based model to quantify EAT by: segmenting the pericardium, suppressing noise-induced artifacts in the heart chambers, and, if image sets were incomplete, imputing missing EAT volumes. The model achieved a mean Dice coefficient of 0.

"Global" cardiac atherosclerotic burden assessed by artificial intelligence-based versus manual segmentation in F-sodium fluoride PET/CT scans: Head-to-head comparison.

Background: Artificial intelligence (AI) is known to provide effective means to accelerate and facilitate clinical and research processes. So in this study it was aimed to compare a AI-based method for cardiac segmentation in positron emission tomography/computed tomography (PET/CT) scans with manual segmentation to assess global cardiac atherosclerosis burden.

Methods: A trained convolutional neural network (CNN) was used for cardiac segmentation in F-sodium fluoride PET/CT scans of 29 healthy volunteers and 20 angina pectoris patients and compared with manual segmentation.

Aortic wall segmentation in F-sodium fluoride PET/CT scans: Head-to-head comparison of artificial intelligence-based versus manual segmentation.

Background: We aimed to establish and test an automated AI-based method for rapid segmentation of the aortic wall in positron emission tomography/computed tomography (PET/CT) scans.

Methods: For segmentation of the wall in three sections: the arch, thoracic, and abdominal aorta, we developed a tool based on a convolutional neural network (CNN), available on the Research Consortium for Medical Image Analysis (RECOMIA) platform, capable of segmenting 100 different labels in CT images. It was tested on F-sodium fluoride PET/CT scans of 49 subjects (29 healthy controls and 20 angina pectoris patients) and compared to data obtained by manual segmentation.

Pneumococcal carriage among children aged 4 - 12 years in Angola 4 years after the introduction of a pneumococcal conjugate vaccine.

Children in Angola are affected by a high burden of disease caused by pneumococcal infections. The 13-valent pneumococcal conjugate vaccine (PCV13) was introduced in the childhood immunization programme in 2013 but the serotype distribution of Streptococcus pneumoniae and antimicrobial susceptibility patterns are unknown. We did a cross-sectional nasopharyngeal carriage study in Luanda and Saurimo, Angola (PCV13 3rd dose coverage 67% and 84%, respectively) during November to December 2017 comprising 940 children aged 4-12 years.

Artificial intelligence-based detection of lymph node metastases by PET/CT predicts prostate cancer-specific survival.

Introduction: Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter-reader variability. Artificial intelligence (AI)-based methods can provide an objective image analysis. We aimed at developing and validating an AI-based tool for detection of lymph node lesions.