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General General

Rise of the Robots: Is Artificial Intelligence a Friend or Foe to Nursing Practice?

In Critical care nursing quarterly

Much like other aspects of health care, nursing has become increasingly saturated with technology over the past several decades. Existing technology has advanced nursing in many ways and contributed to patient safety but at the cost of decreasing nurse-patient interaction. As health care technology progresses to the inclusion of artificial intelligence (AI), the future impact on nursing and direct patient care remains largely unknown, unexplored, and difficult to predict. This article aims to explore the relevance of nursing in a technologically advanced postmodern health care system. The relevance of nursing in the future is solidified by the unique nature of nursing that includes the embodiment of human caring and emotional intelligence. Nurses' abilities to intervene before patient deterioration, care for patients holistically, and manage various aspects of care will be heightened by the adoption of AI. Nurses should embrace AI technology, as we predict that it will decrease nurse workload and cognitive overload and allow for increased patient-nurse interaction. Current and future nurses should take the lead on determining how it augments nursing practice.

Watson Daniel, Womack Joshua, Papadakos Suzanne

General General

Deep Learning-Based HCS Image Analysis for the Enterprise.

In SLAS discovery : advancing life sciences R & D

Drug discovery programs are moving increasingly toward phenotypic imaging assays to model disease-relevant pathways and phenotypes in vitro. These assays offer richer information than target-optimized assays by investigating multiple cellular pathways simultaneously and producing multiplexed readouts. However, extracting the desired information from complex image data poses significant challenges, preventing broad adoption of more sophisticated phenotypic assays. Deep learning-based image analysis can address these challenges by reducing the effort required to analyze large volumes of complex image data at a quality and speed adequate for routine phenotypic screening in pharmaceutical research. However, while general purpose deep learning frameworks are readily available, they are not readily applicable to images from automated microscopy. During the past 3 years, we have optimized deep learning networks for this type of data and validated the approach across diverse assays with several industry partners. From this work, we have extracted five essential design principles that we believe should guide deep learning-based analysis of high-content images and multiparameter data: (1) insightful data representation, (2) automation of training, (3) multilevel quality control, (4) knowledge embedding and transfer to new assays, and (5) enterprise integration. We report a new deep learning-based software that embodies these principles, Genedata Imagence, which allows screening scientists to reliably detect stable endpoints for primary drug response, assess toxicity and safety-relevant effects, and discover new phenotypes and compound classes. Furthermore, we show how the software retains expert knowledge from its training on a particular assay and successfully reapplies it to different, novel assays in an automated fashion.

Steigele Stephan, Siegismund Daniel, Fassler Matthias, Kustec Marusa, Kappler Bernd, Hasaka Tom, Yee Ada, Brodte Annette, Heyse Stephan

2020-May-20

cell-based assays, high-content screening, image analysis, imaging technologies, phenotypic drug discovery

General General

Non - invasive modelling methodology for the diagnosis of coronary artery disease using fuzzy cognitive maps.

In Computer methods in biomechanics and biomedical engineering

Cardiovascular diseases (CVD) and strokes produce immense health and economic burdens globally. Coronary Artery Disease (CAD) is the most common type of cardiovascular disease. Coronary Angiography, which is an invasive approach for detection and treatment, is also the standard procedure for diagnosing CAD. In this work, we illustrate a Medical Decision Support System for the prediction of Coronary Artery Disease (CAD) using Fuzzy Cognitive Maps (FCM). FCMs are a promising modeling methodology, based on human knowledge, capable of dealing with ambiguity and uncertainty and learning how to adapt to the unknown or changing environment. The newly proposed MDSS is developed using the basic notions of Fuzzy Cognitive Maps and is intended to diagnose CAD utilizing specific inputs related to the patient's clinical conditions. We show that the proposed model, when tested on a dataset collected from the Laboratory of Nuclear Medicine of the University Hospital of Patras achieves accuracy of 78.2% outmatching several state-of-the-art classification algorithms.

Apostolopoulos Ioannis D, Groumpos Peter P

2020-May-20

Coronary Artery Disease, decision support system, fuzzy cognitive maps, machine learning

Pathology Pathology

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources.

In Metabolites

As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

Eicher Tara, Kinnebrew Garrett, Patt Andrew, Spencer Kyle, Ying Kevin, Ma Qin, Machiraju Raghu, Mathé And Ewy A

2020-May-15

biological pathways, clustering, co-regulation, deep learning, dimensionality reduction, machine learning, multi-omics integration, network analysis, pathway enrichment, visualization

General General

Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit

ArXiv Preprint

Social media discussion of COVID-19 provides a rich source of information into how the virus affects people's lives that is qualitatively different from traditional public health datasets. In particular, when individuals self-report their experiences over the course of the virus on social media, it can allow for identification of the emotions each stage of symptoms engenders in the patient. Posts to the Reddit forum r/COVID19Positive contain first-hand accounts from COVID-19 positive patients, giving insight into personal struggles with the virus. These posts often feature a temporal structure indicating the number of days after developing symptoms the text refers to. Using topic modelling and sentiment analysis, we quantify the change in discussion of COVID-19 throughout individuals' experiences for the first 14 days since symptom onset. Discourse on early symptoms such as fever, cough, and sore throat was concentrated towards the beginning of the posts, while language indicating breathing issues peaked around ten days. Some conversation around critical cases was also identified and appeared at a roughly constant rate. We identified two clear clusters of positive and negative emotions associated with the evolution of these symptoms and mapped their relationships. Our results provide a perspective on the patient experience of COVID-19 that complements other medical data streams and can potentially reveal when mental health issues might appear.

Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay

2020-05-21

oncology Oncology

Use of Deep Learning to Develop and Analyze Computational Hematoxylin and Eosin Staining of Prostate Core Biopsy Images for Tumor Diagnosis.

In JAMA network open

Importance : Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue samples that cannot be reused.

Objectives : To use deep learning algorithms to develop models that perform accurate computational H&E staining of native nonstained prostate core biopsy images and to develop methods for interpretation of H&E staining deep learning models and analysis of computationally stained images by computer vision and clinical approaches.

Design, Setting, and Participants : This cross-sectional study used hundreds of thousands of native nonstained RGB (red, green, and blue channel) whole slide image (WSI) patches of prostate core tissue biopsies obtained from excess tissue material from prostate core biopsies performed in the course of routine clinical care between January 7, 2014, and January 7, 2017, at Brigham and Women's Hospital, Boston, Massachusetts. Biopsies were registered with their H&E-stained versions. Conditional generative adversarial neural networks (cGANs) that automate conversion of native nonstained RGB WSI to computational H&E-stained images were then trained. Deidentified whole slide images of prostate core biopsy and medical record data were transferred to Massachusetts Institute of Technology, Cambridge, for computational research. Results were shared with physicians for clinical evaluations. Data were analyzed from July 2018 to February 2019.

Main Outcomes and Measures : Methods for detailed computer vision image analytics, visualization of trained cGAN model outputs, and clinical evaluation of virtually stained images were developed. The main outcome was interpretable deep learning models and computational H&E-stained images that achieved high performance in these metrics.

Results : Among 38 patients who provided samples, single core biopsy images were extracted from each whole slide, resulting in 102 individual nonstained and H&E dye-stained image pairs that were compared with matched computationally stained and unstained images. Calculations showed high similarities between computationally and H&E dye-stained images, with a mean (SD) structural similarity index (SSIM) of 0.902 (0.026), Pearson correlation coefficient (PCC) of 0.962 (0.096), and peak signal to noise ratio (PSNR) of 22.821 (1.232) dB. A second cGAN performed accurate computational destaining of H&E-stained images back to their native nonstained form, with a mean (SD) SSIM of 0.900 (0.030), PCC of 0.963 (0.011), and PSNR of 25.646 (1.943) dB compared with native nonstained images. A single blind prospective study computed approximately 95% pixel-by-pixel overlap among prostate tumor annotations provided by 5 board certified pathologists on computationally stained images, compared with those on H&E dye-stained images. This study also used the first visualization and explanation of neural network kernel activation maps during H&E staining and destaining of RGB images by cGANs. High similarities between kernel activation maps of computationally and H&E-stained images (mean-squared errors <0.0005) provide additional mathematical and mechanistic validation of the staining system.

Conclusions and Relevance : These findings suggest that computational H&E staining of native unlabeled RGB images of prostate core biopsy could reproduce Gleason grade tumor signatures that were easily assessed and validated by clinicians. Methods for benchmarking, visualization, and clinical validation of deep learning models and virtually H&E-stained images communicated in this study have wide applications in clinical informatics and oncology research. Clinical researchers may use these systems for early indications of possible abnormalities in native nonstained tissue biopsies prior to histopathological workflows.

Rana Aman, Lowe Alarice, Lithgow Marie, Horback Katharine, Janovitz Tyler, Da Silva Annacarolina, Tsai Harrison, Shanmugam Vignesh, Bayat Akram, Shah Pratik

2020-May-01