An automated COVID-19 triage pipeline using artificial intelligence based on chest radiographs and clinical data

While COVID-19 diagnosis and prognosis artificial intelligence models exist, very few can be implemented for practical use given their high risk of bias. We aimed to develop a diagnosis model that addresses notable shortcomings of prior studies, integrating it into a fully automated triage pipeline that examines chest radiographs for the presence, severity, and progression of COVID-19 pneumonia. Scans were collected using the DICOM Image Analysis and Archive, a system that communicates with a hospital’s image repository. The authors collected over 6,500 non-public chest X-rays comprising diverse COVID-19 severities, along with radiology reports and RT-PCR data. The authors provisioned one internally held-out and two external test sets to assess model generalizability and compare performance to traditional radiologist interpretation. The pipeline was evaluated on a prospective cohort of 80 radiographs, reporting a 95% diagnostic accuracy. The study mitigates bias in AI model development and demonstrates the value of an end-to-end COVID-19 triage platform.

Implementation of early and late fusion methods for content-based image retrieval

Content-Based Image Retrieval (CBIR) systems retrieve images from the image repository or database in which they are visually similar to the query image. CBIR plays an important role in various fields such as medical diagnosis, crime prevention, web-based searching, and architecture. CBIR consists mainly of two stages: The first is the extraction of features and the second is the matching of similarities. There are several ways to improve the efficiency and performance of CBIR, such as segmentation, relevance feedback, expansion of queries, and fusion-based methods. The literature has suggested several methods for combining and fusing various image descriptors. In general, fusion strategies are typically divided into two groups, namely early and late fusion strategies. Early fusion is the combination of image features from more than one descriptor into a single vector before the similarity computation, while late fusion refers either to the combination of outputs produced by various retrieval systems or to the combination of different rankings of similarity. In this study, a group of color and texture features is proposed to be used for both methods of fusion strategies. Firstly, an early combination of eighteen color features and twelve texture features are combined into a single vector representation and secondly, the late fusion of three of the most common distance measures are used in the late fusion stage. Our experimental results on two common image datasets show that our proposed method has good performance retrieval results compared to the traditional way of using single features descriptor and also has an acceptable retrieval performance compared to some of the state-of-the-art methods. The overall accuracy of our proposed method is 60.6% and 39.07% for Corel-1K and GHIM-10K ‎datasets, respectively.

FathomNet: An Open, Underwater Image Repository for Automated Detection and Classification of Midwater and Benthic Objects

Ocean-going platforms and instruments are integrating cameras for observation and navigation, producing a deluge of visual data. The volume of this data collection can rapidly outpace researchers' abilities to process and analyze them. Recent advances in artificial intelligence enable fast, sophisticated analysis of visual data, but have had limited success in the oceanographic world due to lack of dataset standardization, sparse annotation tools, and insufficient formatting and aggregation of existing, expertly curated imagery for use by data scientists. To address this need, we are building FathomNet, a public platform that makes use of existing (and future), expertly curated data to know what is in the ocean and where it is for effective and responsible marine stewardship. This platform is modeled after popular terrestrial datasets (e.g., ImageNet, COCO) that enabled rapid advances in automated visual analysis. FathomNet seeks to engage a wide audience, from the general public to subject-matter experts, to further augment, contribute to, and utilize the training data set. FathomNet will accelerate development of novel algorithms to automate the analysis of underwater visual data, thereby enabling scientists, explorers, policymakers, storytellers, and the public, to learn, understand, and care more about our ocean and its inhabitants.

Kumaraswamy Distribution Based Bi-histogram Equalization for Enhancement of Microscopic Images

Among all image enhancement techniques, histogram equalization is the most used technique. However, preserving brightness is the main issue, and it creates a weird look by destroying its originality. This paper proposes a new method that has command on the brightness issue of histogram equalization to enhance the quality of microscopic images. The method splits the histogram of each color channel into two sub-histograms based on their mean as the threshold and supplanting their cumulative distribution with Kumaraswamy distribution. The proposed method is tested with color microscopic images of cancer-affected lymph nodes gathered from Biological Image Repository IICBU, and objective and subjective assessments confirm that the proposed approach performs more efficiently compared to other state-of-the-art methods.

512 Bedside Transthoracic echography volume Assessment in the BICU

Introduction Point-of-Care Ultrasound (POCUS) has been shown to be a useful adjunct in assessment of various shock states and utilized to guide resuscitative and post-resuscitation de-escalation efforts. POCUS use for guiding resuscitation in burn injured patient has not be described. Objectives characterize the use of bedside ultrasound examinations performed by advance practice providers and treating physicians in a regional burn intensive care unit Methods Daily beside ultrasound examinations were performed utilizing a bedside ultrasound device by an advanced practice provider prior to rounds POCUS examinations consist of: Ultrasound images were archived to a centralized image repository and reviewed daily during multi-disciplinary rounds. Ultrasonographic volume assessment compared to clinical volume assessment made during daily multidisciplinary rounds. Results 100 examinations were performed of those 32 were within the initial 72 hour window: Conclusions Our results demonstrate that bedside ultrasound aides in guidance of both resuscitative and post-resuscitative efforts. We identified a cohort of patients who appeared hypervolemic clinically but US findings supported hypovolemia, we refer to as pseudohypervolemia US volume assessment provides information that changes management. We believe point of care ultrasound is a viable tool in preventing over-resuscitation as well as to guide post-resuscitative diuresis.

Potential and Use of the Googlenet Ann for the Purposes of Inland Water Ships Classification

This article presents an analysis of the possibilities of using the pre-degraded GoogLeNet artificial neural network to classify inland vessels. Inland water authorities monitor the intensity of the vessels via CCTV. Such classification seems to be an improvement in their statutory tasks. The automatic classification of the inland vessels from video recording is a one of the main objectives of the Automatic Ship Recognition and Identification (SHREC) project. The image repository for the training purposes consists about 6,000 images of different categories of the vessels. Some images were gathered from internet websites, and some were collected by the project’s video cameras. The GoogLeNet network was trained and tested using 11 variants. These variants assumed modifications of image sets representing (e.g., change in the number of classes, change of class types, initial reconstruction of images, removal of images of insufficient quality). The final result of the classification quality was 83.6%. The newly obtained neural network can be an extension and a component of a comprehensive geoinformatics system for vessel recognition.

Cohort profile: a collaborative multicentre study of retinal optical coherence tomography in 539 patients with neuromyelitis optica spectrum disorders (CROCTINO)

PurposeOptical coherence tomography (OCT) captures retinal damage in neuromyelitis optica spectrum disorders (NMOSD). Previous studies investigating OCT in NMOSD have been limited by the rareness and heterogeneity of the disease. The goal of this study was to establish an image repository platform, which will facilitate neuroimaging studies in NMOSD. Here we summarise the profile of the Collaborative OCT in NMOSD repository as the initial effort in establishing this platform. This repository should prove invaluable for studies using OCT to investigate NMOSD.ParticipantsThe current cohort includes data from 539 patients with NMOSD and 114 healthy controls. These were collected at 22 participating centres from North and South America, Asia and Europe. The dataset consists of demographic details, diagnosis, antibody status, clinical disability, visual function, history of optic neuritis and other NMOSD defining attacks, and OCT source data from three different OCT devices.Findings to dateThe cohort informs similar demographic and clinical characteristics as those of previously published NMOSD cohorts. The image repository platform and centre network continue to be available for future prospective neuroimaging studies in NMOSD. For the conduct of the study, we have refined OCT image quality criteria and developed a cross-device intraretinal segmentation pipeline.Future plansWe are pursuing several scientific projects based on the repository, such as analysing retinal layer thickness measurements, in this cohort in an attempt to identify differences between distinct disease phenotypes, demographics and ethnicities. The dataset will be available for further projects to interested, qualified parties, such as those using specialised image analysis or artificial intelligence applications.