Chest radiographs and machine learning - Past, present and future.

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed

Assessment of the effect of a comprehensive chest radiograph deep learning model on radiologist reports and patient outcomes: a real-world observational study.

OBJECTIVES: Artificial intelligence (AI) algorithms have been developed to detect imaging features on chest X-ray (CXR) with a comprehensive AI model capable of detecting 124 CXR findings being recently developed. The aim of this study was to evaluate the real-world usefulness of the model as a diagnostic assistance device for radiologists. DESIGN: This prospective real-world multicentre study involved a group of radiologists using the model in their daily reporting workflow to report consecutive CXRs and recording their feedback on level of agreement with the model findings and whether this significantly affected their reporting. SETTING: The study took place at radiology clinics and hospitals within a large radiology network in Australia between November and December 2020. PARTICIPANTS: Eleven consultant diagnostic radiologists of varying levels of experience participated in this study. PRIMARY AND SECONDARY OUTCOME MEASURES: Proportion of CXR cases where use of the AI model led to significant material changes to the radiologist report, to patient management, or to imaging recommendations. Additionally, level of agreement between radiologists and the model findings, and radiologist attitudes towards the model were assessed. RESULTS: Of 2972 cases reviewed with the model, 92 cases (3.1%) had significant report changes, 43 cases (1.4%) had changed patient management and 29 cases (1.0%) had further imaging recommendations. In terms of agreement with the model, 2569 cases showed complete agreement (86.5%). 390 (13%) cases had one or more findings rejected by the radiologist. There were 16 findings across 13 cases (0.5%) deemed to be missed by the model. Nine out of 10 radiologists felt their accuracy was improved with the model and were more positive towards AI poststudy. CONCLUSIONS: Use of an AI model in a real-world reporting environment significantly improved radiologist reporting and showed good agreement with radiologists, highlighting the potential for AI diagnostic support to improve clinical practice

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography

ObjectivesTo evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothesis was that performance would not differ significantly in each of these subgroups when compared with the overall test dataset.DesignA retrospective case–control study was undertaken.SettingCommunity radiology clinics and hospitals in Australia and the USA.ParticipantsA test dataset of 2557 chest radiography studies was ground-truthed by three subspecialty thoracic radiologists for the presence of simple or tension pneumothorax as well as each subgroup other than positioning. Radiograph positioning was derived from radiographer annotations on the images.Outcome measuresDCNN performance for detecting simple and tension pneumothorax was evaluated over the entire test set, as well as within each subgroup, using the area under the receiver operating characteristic curve (AUC). A difference in AUC of more than 0.05 was considered clinically significant.ResultsWhen compared with the overall test set, performance of the DCNN for detecting simple and tension pneumothorax was statistically non-inferior in all subgroups. The DCNN had an AUC of 0.981 (0.976–0.986) for detecting simple pneumothorax and 0.997 (0.995–0.999) for detecting tension pneumothorax.ConclusionsHidden stratification has significant implications for potential failures of deep learning when applied in clinical practice. This study demonstrated that a comprehensively trained DCNN can be resilient to hidden stratification in several clinically meaningful subgroups in detecting pneumothorax.</jats:sec

Multi-Trait and Multi-Environment QTL Analyses for Resistance to Wheat Diseases

BACKGROUND: Stripe rust, leaf rust, tan spot, and Karnal bunt are economically significant diseases impacting wheat production. The objectives of this study were to identify quantitative trait loci for resistance to these diseases in a recombinant inbred line (RIL) from a cross HD29/WH542, and to evaluate the evidence for the presence loci on chromosome region conferring multiple disease resistance. METHODOLOGY/PRINCIPAL FINDINGS: The RIL population was evaluated for four diseases and genotyped with DNA markers. Multi-trait (MT) analysis revealed thirteen QTLs on nine chromosomes, significantly associated with resistance. Phenotypic variation explained by all significant QTLs for KB, TS, Yr, Lr diseases were 57%, 55%, 38% and 22%, respectively. Marginal trait analysis identified the most significant QTLs for resistance to KB on chromosomes 1BS, 2DS, 3BS, 4BL, 5BL, and 5DL. Chromosomes 3AS and 4BL showed significant association with TS resistance. Significant QTLs for Yr resistance were identified on chromosomes 2AS, 4BL and 5BL, while Lr was significant on 6DS. MT analysis revealed that all the QTLs except 3BL significantly reduce KB and was contributed from parent HD29 while all resistant QTLs for TS except on chromosomes 2DS.1, 2DS.2 and 3BL came from WH542. Five resistant QTLs for Yr and six for Lr were contributed from parents WH542 and HD29 respectively. Chromosome region on 4BL showed significant association to KB, TS, and Yr in the population. The multi environment analysis for KB identified three putative QTLs of which two new QTLs, mapped on chromosomes 3BS and 5DL explained 10 and 20% of the phenotypic variation, respectively. CONCLUSIONS/SIGNIFICANCE: This study revealed that MT analysis is an effective tool for detection of multi-trait QTLs for disease resistance. This approach is a more effective and practical than individual QTL mapping analyses. MT analysis identified RILs that combine resistance to multiple diseases from parents WH542 and/or HD29

Sequential induction of three recombination directionality factors directs assembly of tripartite integrative and conjugative elements

Tripartite integrative and conjugative elements (ICE3) are a novel form of ICE that exist as three separate DNA regions integrated within the genomes of Mesorhizobium spp. Prior to conjugative transfer the three ICE3 regions of M. ciceri WSM1271 ICEMcSym1271 combine and excise to form a single circular element. This assembly requires three coordinated recombination events involving three site-specific recombinases IntS, IntG and IntM. Here, we demonstrate that three excisionases–or recombination directionality factors—RdfS, RdfG and RdfM are required for ICE3 excision. Transcriptome sequencing revealed that expression of ICE3 transfer and conjugation genes was induced by quorum sensing. Quorum sensing activated expression of rdfS, and in turn RdfS stimulated transcription of both rdfG and rdfM. Therefore, RdfS acts as a “master controller” of ICE3 assembly and excision. The dependence of all three excisive reactions on RdfS ensures that ICE3 excision occurs via a stepwise sequence of recombination events that avoids splitting the chromosome into a non-viable configuration. These discoveries expose a surprisingly simple control system guiding molecular assembly of these novel and complex mobile genetic elements and highlight the diverse and critical functions of excisionase proteins in control of horizontal gene transfer

Value of hospital antimicrobial stewardship programs [ASPs]:a systematic review

Abstract Background Hospital antimicrobial stewardship programs (ASPs) aim to promote judicious use of antimicrobials to combat antimicrobial resistance. For ASPs to be developed, adopted, and implemented, an economic value assessment is essential. Few studies demonstrate the cost-effectiveness of ASPs. This systematic review aimed to evaluate the economic and clinical impact of ASPs. Methods An update to the Dik et al. systematic review (2000–2014) was conducted on EMBASE and Medline using PRISMA guidelines. The updated search was limited to primary research studies in English (30 September 2014–31 December 2017) that evaluated patient and/or economic outcomes after implementation of hospital ASPs including length of stay (LOS), antimicrobial use, and total (including operational and implementation) costs. Results One hundred forty-six studies meeting inclusion criteria were included. The majority of these studies were conducted within the last 5 years in North America (49%), Europe (25%), and Asia (14%), with few studies conducted in Africa (3%), South America (3%), and Australia (3%). Most studies were conducted in hospitals with 500–1000 beds and evaluated LOS and change in antibiotic expenditure, the majority of which showed a decrease in LOS (85%) and antibiotic expenditure (92%). The mean cost-savings varied by hospital size and region after implementation of ASPs. Average cost savings in US studies were $732 per patient (range:$2.50 to $2640), with similar trends exhibited in European studies. The key driver of cost savings was from reduction in LOS. Savings were higher among hospitals with comprehensive ASPs which included therapy review and antibiotic restrictions. Conclusions Our data indicates that hospital ASPs have significant value with beneficial clinical and economic impacts. More robust published data is required in terms of implementation, LOS, and overall costs so that decision-makers can make a stronger case for investing in ASPs, considering competing priorities. Such data on ASPs in lower- and middle-income countries is limited and requires urgent attention