Automated detection, labelling and radiological grading of clinical spinal MRIs.

Spinal magnetic resonance (MR) scans are a vital tool for diagnosing the cause of back pain for many diseases and conditions. However, interpreting clinically useful information from these scans can be challenging, time-consuming and hard to reproduce across different radiologists. In this paper, we alleviate these problems by introducing a multi-stage automated pipeline for analysing spinal MR scans.

The alteration of oral microbiota before and after training in swimmers.

We aimed to analyze the effect of acute exercise on oral microbiota in regularly trained swimmers. As environmental factors may affect the oral microbiota; we also aimed to analyze the short-duration effect of swimming training on the oral bacteria relative difference in swimmers. Saliva samples of 20 swimmers both before and after the training were used for the oral microbiota metagenesis.

A deep learning approach to private data sharing of medical images using conditional generative adversarial networks (GANs).

Clinical data sharing can facilitate data-driven scientific research, allowing a broader range of questions to be addressed and thereby leading to greater understanding and innovation. However, sharing biomedical data can put sensitive personal information at risk. This is usually addressed by data anonymization, which is a slow and expensive process.

Automated measurement of size of spinal curve in population-based cohorts: Validation of a method based on total body dual energy X-ray absorptiometry scans.

Background: Scoliosis is spinal curvature that may progress to require surgical stabilisation. Risk factors for progression are little understood due to lack of population-based research, since radiographs cannot be performed on entire populations due to high levels of radiation. To help address this, we have previously developed and validated a method for quantification of spinal curvature from total body dual energy X-ray absorptiometry (DXA) scans.

ISSLS PRIZE in Clinical Science 2023: comparison of degenerative MRI features of the intervertebral disc between those with and without chronic low back pain. An exploratory study of two large female populations using automated annotation.

Objectives: The relationship of degeneration to symptoms has been questioned. MRI detects apparently similar disc degeneration and degenerative changes in subjects both with and without back pain. We aimed to overcome these problems by re-annotating MRIs from asymptomatic and symptomatics groups onto the same grading system.

External Validation of SpineNet, an Open-Source Deep Learning Model for Grading Lumbar Disk Degeneration MRI Features, Using the Northern Finland Birth Cohort 1966.

Study Design: This is a retrospective observational study to externally validate a deep learning image classification model.

Objective: Deep learning models such as SpineNet offer the possibility of automating the process of disk degeneration (DD) classification from magnetic resonance imaging (MRI). External validation is an essential step to their development.

Bifidobacterium animalis subsp. lactis as adjunct to non-surgical periodontal treatment in periodontitis: a randomized controlled clinical trial.

Objectives: The purpose of this study is to investigate the clinical and microbiological effects of Bifidobacterium animalis subsp. lactis DN-173010 containing yogurt as an adjunct to non-surgical periodontal treatment in periodontitis patients.

Materials And Methods: This is a prospective randomized controlled clinical study registered with NCT05408364 under clinical trial registration.

The utility of a convolutional neural network (CNN) model score for cancer risk in indeterminate small solid pulmonary nodules, compared to clinical practice according to British Thoracic Society guidelines.

Purpose: To determine how implementation of an artificial intelligence nodule algorithm, the Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), at the point of incidental nodule detection would have influenced further investigation and management using a series of threshold scores at both the benign and malignant end of the spectrum.

Method: An observational retrospective study was performed in the assessment of nodules between 5-15 mm (158 benign, 32 malignant) detected on CT scans, which were performed as part of routine practice. The LCP-CNN was applied to the baseline CT scan producing a percentage score, and subsequent imaging and management determined for each threshold group.

Lung cancer prediction by Deep Learning to identify benign lung nodules.

Introduction: Deep Learning has been proposed as promising tool to classify malignant nodules. Our aim was to retrospectively validate our Lung Cancer Prediction Convolutional Neural Network (LCP-CNN), which was trained on US screening data, on an independent dataset of indeterminate nodules in an European multicentre trial, to rule out benign nodules maintaining a high lung cancer sensitivity.

Methods: The LCP-CNN has been trained to generate a malignancy score for each nodule using CT data from the U.

Identifying climate change impacts on surface water supply in the southern Central Valley, California.

Mountain regions in arid and semi-arid climates, such as California, are considered particularly sensitive to climate change because global warming is expected to alter snowpack storage and related surface water supply. It is therefore important to accurately capture snowmelt processes in watershed models for climate change impact assessment. In this study we use the Soil and Water Assessment Tool (SWAT) to estimate projected changes in snowpack and streamflow in four alpine tributaries to the agriculturally important but less studied southern Central Valley, California.

Adherence frequency of CANDIDA ALBICANS on nasoalveolar molding (NAM) appliances.

Background And Method: The aim of this prospective study was to evaluate the adherence frequency of Candida albicans and non-albicans Candida species in newborn babies with Cleft Lip and Palate (CLP) who receive presurgical orthopedic therapy with Nasoalveolar Molding (NAM) appliances. This study comprised of 25 CLP newborns including 8-right unilateral, 8-left unilateral and 7-bilateral CLP. First swab samples were taken from the hard palate when the baby was referred and renewed after 3 days.

The use of technology in the subcategorisation of osteoarthritis: a Delphi study approach.

Objective: This UK-wide OATech Network + consensus study utilised a Delphi approach to discern levels of awareness across an expert panel regarding the role of existing and novel technologies in osteoarthritis research. To direct future cross-disciplinary research it aimed to identify which could be adopted to subcategorise patients with osteoarthritis (OA).

Design: An online questionnaire was formulated based on technologies which might aid OA research and subcategorisation.

Assessing the Accuracy of a Deep Learning Method to Risk Stratify Indeterminate Pulmonary Nodules.

The management of indeterminate pulmonary nodules (IPNs) remains challenging, resulting in invasive procedures and delays in diagnosis and treatment. Strategies to decrease the rate of unnecessary invasive procedures and optimize surveillance regimens are needed. To develop and validate a deep learning method to improve the management of IPNs.

Correction to: Identifying Scoliosis in Population‑Based Cohorts: Automation of a Validated Method Based on Total Body Dual Energy X‑ray Absorptiometry Scans.

In the original version of the article, the co-author would like to add to the acknowledgements section to highlight their funding stream (EPSRC). The revised acknowledgements is given below.

Could automated machine-learned MRI grading aid epidemiological studies of lumbar spinal stenosis? Validation within the Wakayama spine study.

Background: MRI scanning has revolutionized the clinical diagnosis of lumbar spinal stenosis (LSS). However, there is currently no consensus as to how best to classify MRI findings which has hampered the development of robust longitudinal epidemiological studies of the condition. We developed and tested an automated system for grading lumbar spine MRI scans for central LSS for use in epidemiological research.

External validation of a convolutional neural network artificial intelligence tool to predict malignancy in pulmonary nodules.

Background: Estimation of the risk of malignancy in pulmonary nodules detected by CT is central in clinical management. The use of artificial intelligence (AI) offers an opportunity to improve risk prediction. Here we compare the performance of an AI algorithm, the lung cancer prediction convolutional neural network (LCP-CNN), with that of the Brock University model, recommended in UK guidelines.

Identifying Scoliosis in Population-Based Cohorts: Automation of a Validated Method Based on Total Body Dual Energy X-ray Absorptiometry Scans.

Scoliosis is a 3D-torsional rotation of the spine, but risk factors for initiation and progression are little understood. Research is hampered by lack of population-based research since radiographs cannot be performed on entire populations due to the relatively high levels of ionising radiation. Hence we have developed and validated a manual method for identifying scoliosis from total body dual energy X-ray absorptiometry (DXA) scans for research purposes.

Artificial intelligence and radiomics in pulmonary nodule management: current status and future applications.

Artificial intelligence (AI) has been present in some guise within the field of radiology for over 50 years. The first studies investigating computer-aided diagnosis in thoracic radiology date back to the 1960s, and in the subsequent years, the main application of these techniques has been the detection and classification of pulmonary nodules. In addition, there have been other less intensely researched applications, such as the diagnosis of interstitial lung disease, chronic obstructive pulmonary disease, and the detection of pulmonary emboli.

Quantitative assessment of cyanide in cystic fibrosis sputum and its oxidative catabolism by hypochlorous acid.

Rationale: Cystic fibrosis (CF) patients are known to produce cyanide (CN) although challenges exist in determinations of total levels, the precise bioactive levels, and specificity of its production by CF microflora, especially P. aeruginosa. Our objective was to measure total CN levels in CF sputa by a simple and novel technique in P.

Lung cancer prediction using machine learning and advanced imaging techniques.

Machine learning based lung cancer prediction models have been proposed to assist clinicians in managing incidental or screen detected indeterminate pulmonary nodules. Such systems may be able to reduce variability in nodule classification, improve decision making and ultimately reduce the number of benign nodules that are needlessly followed or worked-up. In this article, we provide an overview of the main lung cancer prediction approaches proposed to date and highlight some of their relative strengths and weaknesses.

Can Atlas-Based Auto-Segmentation Ever Be Perfect? Insights From Extreme Value Theory.

Atlas-based segmentation is used in radiotherapy planning to accelerate the delineation of organs at risk (OARs). Atlas selection has been proposed to improve the performance of segmentation, assuming that the more similar the atlas is to the patient, the better the result. It follows that the larger the database of atlases from which to select, the better the results should be.

Pulmonary nodules: Assessing the imaging biomarkers of malignancy in a "coffee-break".

Introduction: Although nodule volumetry is a recognized biomarker of malignancy in pulmonary nodules (PNs), caution is needed in its interpretation because of variables such as respiratory volume variation and inter-scan variability of up to 25%. CT Texture Analysis (CTTA) is a potential independent biomarker of malignancy but inter-scan variability and respiratory volume variation has not been assessed.

Methods: In this prospective cohort study, 40 patients (20 with an indeterminate PN and 20 with pulmonary metastases) underwent two LDCTs within a 60-min period (the "Coffee-break") with the aim of assessing the repeatability of CTTA and semi-automated volume measurements.

SpineNet: Automated classification and evidence visualization in spinal MRIs.

The objective of this work is to automatically produce radiological gradings of spinal lumbar MRIs and also localize the predicted pathologies. We show that this can be achieved via a Convolutional Neural Network (CNN) framework that takes intervertebral disc volumes as inputs and is trained only on disc-specific class labels. Our contributions are: (i) a CNN architecture that predicts multiple gradings at once, and we propose variants of the architecture including using 3D convolutions; (ii) showing that this architecture can be trained using a multi-task loss function without requiring segmentation level annotation; and (iii) a localization method that clearly shows pathological regions in the disc volumes.

ISSLS PRIZE IN BIOENGINEERING SCIENCE 2017: Automation of reading of radiological features from magnetic resonance images (MRIs) of the lumbar spine without human intervention is comparable with an expert radiologist.

Study Design: Investigation of the automation of radiological features from magnetic resonance images (MRIs) of the lumbar spine.

Objective: To automate the process of grading lumbar intervertebral discs and vertebral bodies from MRIs. MR imaging is the most common imaging technique used in investigating low back pain (LBP).