No code machine learning: validating the approach on use-case for classifying clavicle fractures.

Purpose: We created an infrastructure for no code machine learning (NML) platform for non-programming physicians to create NML model. We tested the platform by creating an NML model for classifying radiographs for the presence and absence of clavicle fractures.

Methods: Our IRB-approved retrospective study included 4135 clavicle radiographs from 2039 patients (mean age 52 ± 20 years, F:M 1022:1017) from 13 hospitals.

Investigating the impact of the COVID-19 pandemic on recovery colleges: multi-site qualitative study.

Background: During the COVID-19 pandemic, mental health problems increased as access to mental health services reduced. Recovery colleges are recovery-focused adult education initiatives delivered by people with professional and lived mental health expertise. Designed to be collaborative and inclusive, they were uniquely positioned to support people experiencing mental health problems during the pandemic.

Detection of abdominopelvic lymph nodes in multi-parametric MRI.

Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies.

Longitudinal follow-up of incidental renal calculi on computed tomography.

Rationale And Objectives: Measuring small kidney stones on CT is a time-consuming task often neglected. Volumetric assessment provides a better measure of size than linear dimensions. Our objective is to analyze the growth rate and prognosis of incidental kidney stones in asymptomatic patients on CT.

Universal detection and segmentation of lymph nodes in multi-parametric MRI.

Purpose: Reliable measurement of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies of the body plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Previous approaches do not adequately exploit the complementary sequences in mpMRI to universally detect and segment lymph nodes, and they have shown fairly limited performance.

Methods: We propose a computer-aided detection and segmentation pipeline to leverage the T2 fat-suppressed (T2FS) and diffusion-weighted imaging (DWI) series from a mpMRI study.

Fully Automated Longitudinal Assessment of Renal Stone Burden on Serial CT Imaging Using Deep Learning.

Use deep learning (DL) to automate the measurement and tracking of kidney stone burden over serial CT scans. This retrospective study included 259 scans from 113 symptomatic patients being treated for urolithiasis at a single medical center between 2006 and 2019. These patients underwent a standard low-dose noncontrast CT scan followed by ultra-low-dose CT scans limited to the level of the kidneys.

Fully Automated and Explainable Liver Segmental Volume Ratio and Spleen Segmentation at CT for Diagnosing Cirrhosis.

Purpose: To evaluate the performance of a deep learning (DL) model that measures the liver segmental volume ratio (LSVR) (ie, the volumes of Couinaud segments I-III/IV-VIII) and spleen volumes from CT scans to predict cirrhosis and advanced fibrosis.

Materials And Methods: For this Health Insurance Portability and Accountability Act-compliant, retrospective study, two datasets were used. Dataset 1 consisted of patients with hepatitis C who underwent liver biopsy (METAVIR F0-F4, 2000-2016).

Recovery Colleges Characterisation and Testing in England (RECOLLECT): rationale and protocol.

Background: Recovery Colleges are a relatively recent initiative within mental health services. The first opened in 2009 in London and since then numbers have grown. They are based on principles of personal recovery in mental health, co-production between people with lived experience of mental health problems and professionals, and adult learning.

Fully Automated Abdominal CT Biomarkers for Type 2 Diabetes Using Deep Learning.

Background CT biomarkers both inside and outside the pancreas can potentially be used to diagnose type 2 diabetes mellitus. Previous studies on this topic have shown significant results but were limited by manual methods and small study samples. Purpose To investigate abdominal CT biomarkers for type 2 diabetes mellitus in a large clinical data set using fully automated deep learning.

Assessment of Aortoiliac Atherosclerotic Plaque on CT in Prostate Cancer Patients Undergoing Treatment.

Traditionally, atherosclerotic risk factors for cardiovascular disease and cancer are assessed using coronary artery calcium scoring. However, this neglects the impact of atherosclerotic disease more proximal to the cancer site. This study assesses whether aortoiliac atherosclerotic plaque is associated with prostate cancer.

A deep learning system for automated kidney stone detection and volumetric segmentation on noncontrast CT scans.

Purpose: Early detection and size quantification of renal calculi are important for optimizing treatment and preventing severe kidney stone disease. Prior work has shown that volumetric measurements of kidney stones are more informative and reproducible than linear measurements. Deep learning-based systems that use abdominal noncontrast computed tomography (CT) scans may assist in detection and reduce workload by removing the need for manual stone volume measurement.

Global-Local attention network with multi-task uncertainty loss for abnormal lymph node detection in MR images.

Accurate and reliable detection of abnormal lymph nodes in magnetic resonance (MR) images is very helpful for the diagnosis and treatment of numerous diseases. However, it is still a challenging task due to similar appearances between abnormal lymph nodes and other tissues. In this paper, we propose a novel network based on an improved Mask R-CNN framework for the detection of abnormal lymph nodes in MR images.

Deep Learning CT-based Quantitative Visualization Tool for Liver Volume Estimation: Defining Normal and Hepatomegaly.

Background Imaging assessment for hepatomegaly is not well defined and currently uses suboptimal, unidimensional measures. Liver volume provides a more direct measure for organ enlargement. Purpose To determine organ volume and to establish thresholds for hepatomegaly with use of a validated deep learning artificial intelligence tool that automatically segments the liver.

Opportunistic Screening at Abdominal CT: Use of Automated Body Composition Biomarkers for Added Cardiometabolic Value.

Abdominal CT is a frequently performed imaging examination for a wide variety of clinical indications. In addition to the immediate reason for scanning, each CT examination contains robust additional data on body composition that generally go unused in routine clinical practice. There is now growing interest in harnessing this additional information.

Deep Small Bowel Segmentation with Cylindrical Topological Constraints.

We present a novel method for small bowel segmentation where a cylindrical topological constraint based on persistent homology is applied. To address the touching issue which could break the applied constraint, we propose to augment a network with an additional branch to predict an inner cylinder of the small bowel. Since the inner cylinder is free of the touching issue, a cylindrical shape constraint applied on this augmented branch guides the network to generate a topologically correct segmentation.

Atherosclerotic Plaque Burden on Abdominal CT: Automated Assessment With Deep Learning on Noncontrast and Contrast-enhanced Scans.

Background: Abdominal aortic atherosclerotic plaque burden may have clinical significance but manual measurement is time-consuming and impractical.

Purpose: To perform external validation on an automated atherosclerotic plaque detector for noncontrast and postcontrast abdominal CT.

Materials And Methods: The training data consisted of 114 noncontrast CT scans and 23 postcontrast CT urography scans.

Fully automated CT imaging biomarkers of bone, muscle, and fat: correcting for the effect of intravenous contrast.

Purpose: Fully automated CT-based algorithms for quantifying bone, muscle, and fat have been validated for unenhanced abdominal scans. The purpose of this study was to determine and correct for the effect of intravenous (IV) contrast on these automated body composition measures.

Materials And Methods: Initial study cohort consisted of 1211 healthy adults (mean age, 45.

Liver Steatosis Categorization on Contrast-Enhanced CT Using a Fully Automated Deep Learning Volumetric Segmentation Tool: Evaluation in 1204 Healthy Adults Using Unenhanced CT as a Reference Standard.

Hepatic attenuation at unenhanced CT is linearly correlated with the MRI proton density fat fraction (PDFF). Liver fat quantification at contrast-enhanced CT is more challenging. The purpose of this article is to evaluate liver steatosis categorization on contrast-enhanced CT using a fully automated deep learning volumetric hepatosplenic segmentation algorithm and unenhanced CT as the reference standard.

Exclusion Zone Phenomena in Water-A Critical Review of Experimental Findings and Theories.

The existence of the exclusion zone (EZ), a layer of water in which plastic microspheres are repelled from hydrophilic surfaces, has now been independently demonstrated by several groups. A better understanding of the mechanisms which generate EZs would help with understanding the possible importance of EZs in biology and in engineering applications such as filtration and microfluidics. Here we review the experimental evidence for EZ phenomena in water and the major theories that have been proposed.

Using a monomer potential energy surface to perform approximate path integral molecular dynamics simulation of ab initio water at near-zero added cost.

It is now established that nuclear quantum motion plays an important role in determining water's hydrogen bonding, structure, and dynamics. Such effects are important to include in density functional theory (DFT) based molecular dynamics simulation of water. The standard way of treating nuclear quantum effects, path integral molecular dynamics (PIMD), multiplies the number of energy/force calculations by the number of beads required.

Mechanisms of Action and Outcomes for Students in Recovery Colleges.

Objective: Recovery colleges are widespread, with little empirical research on how they work and the outcomes they produce. This study aimed to coproduce a change model characterizing mechanisms of action (how they work) and outcomes (their impact) for mental health service users who attend recovery colleges.

Methods: A systematized review identified all publications about recovery colleges.

Applying machine learning techniques to predict the properties of energetic materials.

We present a proof of concept that machine learning techniques can be used to predict the properties of CNOHF energetic molecules from their molecular structures. We focus on a small but diverse dataset consisting of 109 molecular structures spread across ten compound classes. Up until now, candidate molecules for energetic materials have been screened using predictions from expensive quantum simulations and thermochemical codes.

The origin of the Debye relaxation in liquid water and fitting the high frequency excess response.

We critically review the literature on the Debye absorption peak of liquid water and the excess response found on the high frequency side of the Debye peak. We find a lack of agreement on the microscopic phenomena underlying both of these features. To better understand the molecular origin of Debye peak we ran large scale molecular dynamics simulations and performed several different distance-dependent decompositions of the low frequency dielectric spectra, finding that it involves processes that take place on scales of 1.

The hydrogen-bond network of water supports propagating optical phonon-like modes.

The local structure of liquid water as a function of temperature is a source of intense research. This structure is intimately linked to the dynamics of water molecules, which can be measured using Raman and infrared spectroscopies. The assignment of spectral peaks depends on whether they are collective modes or single-molecule motions.