Development of Nipple Trauma Evaluation System With Deep Learning.

Background: No research has been conducted on the use of deep learning for breastfeeding support.

Research Aim: This study aims to develop a nipple trauma evaluation system using deep learning.

Methods: We used an exploratory data analysis approach to develop a deep-learning model for medical imaging.

Deep learning-based correction for time truncation in cerebral computed tomography perfusion.

Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a three-dimensional (two-dimensional + time) convolutional neural network (CNN)-based approach to predict missing CTP image frames at the end of the series from earlier acquired image frames. Moreover, we evaluated three strategies for predicting multiple time points.

Artificial Intelligence Quantification of Enhanced Synovium Throughout the Entire Hand in Rheumatoid Arthritis on Dynamic Contrast-Enhanced MRI.

Background: Challenges persist in achieving automatic and efficient inflammation quantification using dynamic contrast-enhanced (DCE) MRI in rheumatoid arthritis (RA) patients.

Purpose: To investigate an automatic artificial intelligence (AI) approach and an optimized dynamic MRI protocol for quantifying disease activity in RA in whole hands while excluding arterial pixels.

Study Type: Retrospective.

Fully automatic quantification for hand synovitis in rheumatoid arthritis using pixel-classification-based segmentation network in DCE-MRI.

Purpose: A classification-based segmentation method is proposed to quantify synovium in rheumatoid arthritis (RA) patients using a deep learning (DL) method based on time-intensity curve (TIC) analysis in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI).

Materials And Methods: This retrospective study analyzed a hand MR dataset of 28 RA patients (six males, mean age 53.7 years).

Deep learning to assess right ventricular ejection fraction from two-dimensional echocardiograms in precapillary pulmonary hypertension.

Background: Precapillary pulmonary hypertension (PH) is characterized by a sustained increase in right ventricular (RV) afterload, impairing systolic function. Two-dimensional (2D) echocardiography is the most performed cardiac imaging tool to assess RV systolic function; however, an accurate evaluation requires expertise. We aimed to develop a fully automated deep learning (DL)-based tool to estimate the RV ejection fraction (RVEF) from 2D echocardiographic videos of apical four-chamber views in patients with precapillary PH.

Imaging of O-labeled Water Using Fast T2 Mapping with T2-preparation: A Phantom Study.

O-labeled water is a T2-shortening contrast agent used in proton MRI and is a promising method for visualizing cerebrospinal fluid (CSF) dynamics because it provides long-term tracking of water molecules. However, various external factors reduce the accuracy of O-concentration measurements using conventional signal-intensity-based methods. In addition, T2 mapping, which is expected to provide a stable assessment, is generally limited to temporal-spatial resolution.

Estimating Body Weight From Measurements From Different Single-Slice Computed Tomography Levels: An Evaluation of Total Cross-Sectional Body Area Measurements and Deep Learning.

Objective: This study aimed to evaluate the correlation between the estimated body weight obtained from 2 easy-to-perform methods and the actual body weight at different computed tomography (CT) levels and determine the best reference site for estimating body weight.

Methods: A total of 862 patients from a public database of whole-body positron emission tomography/CT studies were retrospectively analyzed. Two methods for estimating body weight at 10 single-slice CT levels were evaluated: a linear regression model using total cross-sectional body area and a deep learning-based model.

Deep learning-based video-analysis of instrument motion in microvascular anastomosis training.

Purpose: Attaining sufficient microsurgical skills is paramount for neurosurgical trainees. Kinematic analysis of surgical instruments using video offers the potential for an objective assessment of microsurgical proficiency, thereby enhancing surgical training and patient safety. The purposes of this study were to develop a deep-learning-based automated instrument tip-detection algorithm, and to validate its performance in microvascular anastomosis training.

Body temperature in the acute phase and clinical outcomes after acute ischemic stroke.

Background: This study aimed to examine whether post-stroke early body temperature is associated with neurological damage in the acute phase and functional outcomes at three months.

Methods: We included 7,177 patients with acute ischemic stroke within 24 h of onset. Axillary temperature was measured daily in the morning for seven days.

Visualization of Gastrointestinal Bezoar Movement Causing and Releasing Small Bowel Obstruction on Computed Tomography in a Patient With Diabetes Mellitus.

Although delayed gastric emptying promotes gastrointestinal bezoar formation in patients with diabetes mellitus (DM), the association between movement of gastrointestinal bezoars and glycemic status remains unclear. We report a case of small bowel obstruction (SBO) caused by impaction of the migrated gastric bezoar into the small bowel in a patient with DM. Correction of hyperglycemia and lactic acidosis led to normalization of gastrointestinal motility, followed by expulsion of the impacted bezoar and resolution of SBO.

Energy spectrum measurement of scattered X-rays during IVR procedure.

With the increase of the number of interventional radiology (IVR) procedures, the occupational exposure of operators and medical staff has attracted keen attention. The energy of scattered radiation in medical clinical sites is important for estimating the biological effects of occupational exposure. Recent years have seen many reports on the dose of scattered radiation by IVR, but few on the energy spectrum.

The montage method improves the classification of suspected acute ischemic stroke using the convolution neural network and brain MRI.

This study investigated the usefulness of the montage method that combines four different magnetic resonance images into one images for automatic acute ischemic stroke (AIS) diagnosis with deep learning method. The montage image was consisted from diffusion weighted image (DWI), fluid attenuated inversion recovery (FLAIR), arterial spin labeling (ASL), and apparent diffusion coefficient (ASL). The montage method was compared with pseudo color map (pCM) which was consisted from FLAIR, ASL and ADC.

Nanoarchitectonics and Catalytic Performance of Au-Pd Nanoflowers Supported on FeO.

Supported anisotropic bimetallic nanocrystals are attractive owing to their potential for novel catalytic applications. Au-Pd nanocrystals are expected to have higher catalytic activity for alcohol oxidation than Au nanocrystals. However, only a few studies have reported the application of anisotropic Au-Pd nanocrystals as alcohol-oxidation nanocatalysts.

Mid-term outcomes of surgical aortic valve replacement using a mosaic porcine bioprosthesis with concomitant mitral valve repair.

This study retrospectively evaluated the mid-term outcomes of surgical aortic valve replacement (SAVR) using a stented porcine aortic valve bioprosthesis (Mosaic; Medtronic Inc., Minneapolis, MN, USA) with concomitant mitral valve (MV) repair. From 1999 to 2014, 157 patients (median [interquartile range] age, 75 [70-79] years; 47% women) underwent SAVR with concomitant MV repair (SAVR + MV repair), and 1045 patients (median [interquartile range] age, 76 [70-80] years; 54% women) underwent SAVR only at 10 centers in Japan as part of the long-term multicenter Japan Mosaic valve (J-MOVE) study.

Soluble C-type lectin-like receptor 2 in stroke (CLECSTRO) study: protocol of a multicentre, prospective cohort of a novel platelet activation marker in acute ischaemic stroke and transient ischaemic attack.

Introduction: Soluble C-type lectin-like receptor 2 (sCLEC-2) is a new biomarker for platelet activation, which can be easily measured by usual blood collection. We conducted the CLECSTRO, a prospective, observational cohort study, to evaluate the clinical implications of sCLEC-2 in patients with acute ischaemic stroke (AIS) and transient ischaemic attack (TIA).

Methods And Analysis: The participants are patients with AIS/TIA and control patients required for differentiation from AIS/TIA.

Magnetic Resonance Water Tracer Imaging Using 17 O-Labeled Water.

Magnetic resonance imaging (MRI) is a crucial imaging technique for visualizing water in living organisms. Besides proton MRI, which is widely available and enables direct visualization of intrinsic water distribution and dynamics in various environments, MR-WTI (MR water tracer imaging) using 17 O-labeled water has been developed, benefiting from the many advancements in MRI software and hardware that have substantially improved the signal-to-noise ratio and made possible faster imaging. This cutting-edge technique allows the generation of novel and valuable images for clinical use.

Factors Associated with Refraining from Health Checkups during the COVID-19 Pandemic in Japan.

This study aimed to determine the characteristics of people who refrained from having regular checkups due to the spread of the novel coronavirus 2019 (COVID-19) infection and the factors associated with this behavior. We conducted a nationwide internet survey of 4593 males and females aged 20-69 in Japan regarding their health checkups from April 2020 to March 2021, when COVID-19 was widespread. Individuals who received checkups during this time were "the receiving group"; those who did not were "the refraining group".

Urinary levels of cortisol but not catecholamines are associated with those of 8-hydroxy-2'-deoxyguanosine in uncomplicated primary hypertension.

Objectives: The relationships between stress hormones and oxidative DNA damage have not yet been explored in human hypertension. We investigated the associations of urinary levels of cortisol or catecholamines with those of 8-hydroxy-2'-deoxyguanosine, a marker of oxidative DNA damage in primary hypertension.

Methods: Untreated 156 primary hypertensives without apparent cardiovascular diseases were entered into the study.

Estimation of Left and Right Ventricular Ejection Fractions from cine-MRI Using 3D-CNN.

Cardiac function indices must be calculated using tracing from short-axis images in cine-MRI. A 3D-CNN (convolutional neural network) that adds time series information to images can estimate cardiac function indices without tracing using images with known values and cardiac cycles as the input. Since the short-axis image depicts the left and right ventricles, it is unclear which motion feature is captured.

Predicting Mechanical Thrombectomy Outcome and Time Limit through ADC Value Analysis: A Comprehensive Clinical and Simulation Study Using Machine Learning.

Predicting outcomes after mechanical thrombectomy (MT) remains challenging for patients with acute ischemic stroke (AIS). This study aimed to explore the usefulness of machine learning (ML) methods using detailed apparent diffusion coefficient (ADC) analysis to predict patient outcomes and simulate the time limit for MT in AIS. A total of 75 consecutive patients with AIS with complete reperfusion in MT were included; 20% were separated to test data.

Tissue Acceleration as a Novel Metric for Surgical Performance During Carotid Endarterectomy.

Background And Objectives: Gentle tissue handling to avoid excessive motion of affected fragile vessels during surgical dissection is essential for both surgeon proficiency and patient safety during carotid endarterectomy (CEA). However, a void remains in the quantification of these aspects during surgery. The video-based measurement of tissue acceleration is presented as a novel metric for the objective assessment of surgical performance.

Deep learning-based body weight from scout images can be an alternative to actual body weight in CT radiation dose management.

Purpose: Accurate body weight measurement is essential to promote computed tomography (CT) dose optimization; however, body weight cannot always be measured prior to CT examination, especially in the emergency setting. The aim of this study was to investigate whether deep learning-based body weight from chest CT scout images can be an alternative to actual body weight in CT radiation dose management.

Methods: Chest CT scout images and diagnostic images acquired for medical checkups were collected from 3601 patients.