Application of Deep Learning to IVC Filter Detection from CT Scans.

IVC filters (IVCF) perform an important function in select patients that have venous blood clots. However, they are usually intended to be temporary, and significant delay in removal can have negative health consequences for the patient. Currently, all Interventional Radiology (IR) practices are tasked with tracking patients in whom IVCF are placed. Due to their small size and location deep within the abdomen it is common for patients to forget that they have an IVCF. Therefore, there is a significant delay for a new healthcare provider to become aware of the presence of a filter. Patients may have an abdominopelvic CT scan for many reasons and, fortunately, IVCF are clearly visible on these scans. In this research a deep learning model capable of segmenting IVCF from CT scan slices along the axial plane is developed. The model achieved a Dice score of 0.82 for training over 372 CT scan slices. The segmentation model is then integrated with a prediction algorithm capable of flagging an entire CT scan as having IVCF. The prediction algorithm utilizing the segmentation model achieved a 92.22% accuracy at detecting IVCF in the scans.

A Comprehensive Review of Machine Learning Used to Combat COVID-19.

Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

MicroScope: an integrated platform for the annotation and exploration of microbial gene functions through genomic, pangenomic and metabolic comparative analysis.

Large-scale genome sequencing and the increasingly massive use of high-throughput approaches produce a vast amount of new information that completely transforms our understanding of thousands of microbial species. However, despite the development of powerful bioinformatics approaches, full interpretation of the content of these genomes remains a difficult task. Launched in 2005, the MicroScope platform (https://www.genoscope.cns.fr/agc/microscope) has been under continuous development and provides analysis for prokaryotic genome projects together with metabolic network reconstruction and post-genomic experiments allowing users to improve the understanding of gene functions. Here we present new improvements of the MicroScope user interface for genome selection, navigation and expert gene annotation. Automatic functional annotation procedures of the platform have also been updated and we added several new tools for the functional annotation of genes and genomic regions. We finally focus on new tools and pipeline developed to perform comparative analyses on hundreds of genomes based on pangenome graphs. To date, MicroScope contains data for >11 800 microbial genomes, part of which are manually curated and maintained by microbiologists (>4500 personal accounts in September 2019). The platform enables collaborative work in a rich comparative genomic context and improves community-based curation efforts.

MicroScope-an integrated resource for community expertise of gene functions and comparative analysis of microbial genomic and metabolic data.

The overwhelming list of new bacterial genomes becoming available on a daily basis makes accurate genome annotation an essential step that ultimately determines the relevance of thousands of genomes stored in public databanks. The MicroScope platform (http://www.genoscope.cns.fr/agc/microscope) is an integrative resource that supports systematic and efficient revision of microbial genome annotation, data management and comparative analysis. Starting from the results of our syntactic, functional and relational annotation pipelines, MicroScope provides an integrated environment for the expert annotation and comparative analysis of prokaryotic genomes. It combines tools and graphical interfaces to analyze genomes and to perform the manual curation of gene function in a comparative genomics and metabolic context. In this article, we describe the free-of-charge MicroScope services for the annotation and analysis of microbial (meta)genomes, transcriptomic and re-sequencing data. Then, the functionalities of the platform are presented in a way providing practical guidance and help to the nonspecialists in bioinformatics. Newly integrated analysis tools (i.e. prediction of virulence and resistance genes in bacterial genomes) and original method recently developed (the pan-genome graph representation) are also described. Integrated environments such as MicroScope clearly contribute, through the user community, to help maintaining accurate resources.