Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register-based medical hospital data and neural networks published in European Heart Journal – Digital Health

Neural network prediction of mortality and atrial fibrillation. (Image taken from the article’s graphical abstract.)
Enhanced prediction of atrial fibrillation and mortality among patients with congenital heart disease using nationwide register based medical hospital data and neural networks
Kok Wai Giang, Saga Helgadottir, Mikael Dellborg, Giovanni Volpe, Zacharias Mandalenakis
European Heart Journal – Digital Health (2021)
doi: 10.1093/ehjdh/ztab065

Aims: To improve short-and long-term predictions of mortality and atrial fibrillation (AF) among patients with congenital heart disease (CHD) from a nationwide population using neural networks (NN).

Methods and results: The Swedish National Patient Register and the Cause of Death Register were used to identify all patients with CHD born from 1970 to 2017. A total of 71 941 CHD patients were identified and followed-up from birth until the event or end of study in 2017. Based on data from a nationwide population, a NN model was obtained to predict mortality and AF. Logistic regression (LR) based on the same data was used as a baseline comparison. Of 71 941 CHD patients, a total of 5768 died (8.02%) and 995 (1.38%) developed AF over time with a mean follow-up time of 16.47 years (standard deviation 12.73 years). The performance of NN models in predicting the mortality and AF was higher than the performance of LR regardless of the complexity of the disease, with an average area under the receiver operating characteristic of >0.80 and >0.70, respectively. The largest differences were observed in mortality and complexity of CHD over time.

Conclusion: We found that NN can be used to predict mortality and AF on a nationwide scale using data that are easily obtainable by clinicians. In addition, NN showed a high performance overall and, in most cases, with better performance for prediction as compared with more traditional regression methods.

Extracting quantitative biological information from brightfield cell images using deep learning featured in AIP SciLight

The article Extracting quantitative biological information from brightfield cell images using deep learning
has been featured in: “Staining Cells Virtually Offers Alterative Approach to Chemical Dyes”, AIP SciLight (July 23, 2021).

Scilight showcases the most interesting research across the physical sciences published in AIP Publishing Journals.

Scilight is published weekly (52 issues per year) by AIP Publishing.

Extracting quantitative biological information from brightfield cell images using deep learning published in Biophysics Reviews

Virtually-stained generated image for lipid-droplet.
Extracting quantitative biological information from brightfield cell images using deep learning
Saga Helgadottir, Benjamin Midtvedt, Jesús Pineda, Alan Sabirsh, Caroline B. Adiels, Stefano Romeo, Daniel Midtvedt, Giovanni Volpe
Biophysics Rev. 2, 031401 (2021)
arXiv: 2012.12986
doi: 10.1063/5.0044782

Quantitative analysis of cell structures is essential for biomedical and pharmaceutical research. The standard imaging approach relies on fluorescence microscopy, where cell structures of interest are labeled by chemical staining techniques. However, these techniques are often invasive and sometimes even toxic to the cells, in addition to being time-consuming, labor-intensive, and expensive. Here, we introduce an alternative deep-learning-powered approach based on the analysis of brightfield images by a conditional generative adversarial neural network (cGAN). We show that this approach can extract information from the brightfield images to generate virtually-stained images, which can be used in subsequent downstream quantitative analyses of cell structures. Specifically, we train a cGAN to virtually stain lipid droplets, cytoplasm, and nuclei using brightfield images of human stem-cell-derived fat cells (adipocytes), which are of particular interest for nanomedicine and vaccine development. Subsequently, we use these virtually-stained images to extract quantitative measures about these cell structures. Generating virtually-stained fluorescence images is less invasive, less expensive, and more reproducible than standard chemical staining; furthermore, it frees up the fluorescence microscopy channels for other analytical probes, thus increasing the amount of information that can be extracted from each cell.

Saga Helgadottir defended her PhD Thesis in Physics on June 16, 2021. Congrats!

Digital video microscopy enhanced by deep learning
Saga Helgadottir defended her PhD Thesis in Physics on June 16, 2021. Congrats!

The disputation took place at 9 a.m. digitally via Zoom. A link to the Zoom meeting was published the day before dissertation on the GU website.

Title:  Deep Learning Applications – From image analysis to medical diagnosis

Abstract:
Deep learning is a subcategory of machine learning and artificial intelligence. Instead of using explicit rules to perform a desired task as in standard algorithmic approaches, machine-learning algorithms autonomously learn from data to determine the rules for the task at hand. The idea of deep learning has been around since the 1950s but was for a long time limited by available computational power and amount of training data. Once overcome these problems, in recent years, deep learning has made great advances in solving various problems.

In this thesis, I show how deep learning can be applied in image analysis and medical diagnosis, while outperforming standard algorithmic methods and simpler machine-learning methods. I begin with showing that a convolutional neural network trained with simulated particle images is able to track experimental single particles, even in poor illumination conditions. I then show how this inspired the development of an all-in-one software to design, train and validate deep-learning solutions for digital microscopy, from particle tracking and characterization in 2D and 3D to the segmentation, characterization and counting of biological cells and image transformation. I show that this software package can be further used to develop a generative adversarial neural network to virtually stain brightfield images of cells, replacing the traditional chemical staining for a downstream analysis of biological features. I then move on from applications in microscopy and image analysis to show the potential of deep learning in medical diagnosis. I show that dense neural networks perform better than simpler machine-learning algorithm and the clinical standard in the diagnosis of a genetic disease and in the prediction of short- and long-term morbidity in patients with congenital-heart-disease. At last, I have shown that a neural network- powered strategy for testing and isolating individuals adapts to the parameters of a disease outbreak achieves an epidemic containment.

The interdisciplinary nature of the work in this thesis has allowed the application of new technologies developed in the field of physics to solve problems in the fields of biology and biomedicine, as well as overcoming barriers for the continued revolutionization of deep learning in microscopy.

Thesishttp://hdl.handle.net/2077/67506

Supervisor: Giovanni Volpe
Examiner: Bernhard Mehlig
Opponent: Carolina Wählby
Committee: Marj Tonini, Maria Garcia-Parajo, Alexander Rohrbach

Screenshots from Saga Helgadottir’s PhD Thesis defense.

PhD Opponent’s presentation.
PhD Thesis presentation: Saga Helgadottir, Giovanni Volpe (Supervisor), Raimund Feifel (GU Physics), Carolina Wählby (Opponent), Marj Tonini (Committee member), Maria Garcia-Parajo (Committee member), Måns Henningson (GU Physics Department Chair), Alexander Rohrbach (Committee member).
PhD Thesis presentation.
PhD Thesis presentation front slide.
PhD Thesis presentation content slide (1).
PhD Thesis presentation content slide (2).
PhD Thesis presentation conclusion slide.
Screenshot from the discussion (1).
Screenshot from the discussion (2).
Screenshot from the discussion (3).

Presentation by L. Natali at Spatial Data Science 2020, 11 June 2021

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)
Improving epidemic testing and containment strategies using machine learning. 
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe.
Submitted to SDS2020
Date: 11 June
Time: 16:15 (CEST)

Abstract: 
Containment of epidemic outbreaks entails great societal and economic costs.  Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Improving epidemic testing and containment strategies using machine learning accepted in Machine Learning: Science and Technology

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half).
Improving epidemic testing and containment strategies using machine learning
Laura Natali, Saga Helgadottir, Onofrio M. Maragò, Giovanni Volpe
Machine Learning: Science and Technology, 2 035007 (2021)
doi: 10.1088/2632-2153/abf0f7
arXiv: 2011.11717

Containment of epidemic outbreaks entails great societal and economic costs. Cost-effective containment strategies rely on efficiently identifying infected individuals, making the best possible use of the available testing resources. Therefore, quickly identifying the optimal testing strategy is of critical importance. Here, we demonstrate that machine learning can be used to identify which individuals are most beneficial to test, automatically and dynamically adapting the testing strategy to the characteristics of the disease outbreak. Specifically, we simulate an outbreak using the archetypal susceptible-infectious-recovered (SIR) model and we use data about the first confirmed cases to train a neural network that learns to make predictions about the rest of the population. Using these prediction, we manage to contain the outbreak more effectively and more quickly than with standard approaches. Furthermore, we demonstrate how this method can be used also when there is a possibility of reinfection (SIRS model) to efficiently eradicate an endemic disease.

Press release on Machine learning can help slow down future pandemics

Comparison of different evolution regimes of disease spreading: free evolution (bottom left half) vs network strategy (top right half). (Image by Laura Natali.)

The article Improving epidemic testing and containment strategies using machine learning has been featured in the News of the Faculty of Science of Gothenburg University.

Here the links to the press releases:
Swedish: Maskininlärning kan bidra till att bromsa framtida pandemier
English: Machine learning can help slow down future pandemics

The articles was also featured in:
AI ska bromsa framtidens pandemier Metal Supply (23/04/2021)
El papel de la inteligencia artificial para frenar futuras pandemias El Nacional.cat. (16/04/2021)
AI could be critical to preventing future pandemics – study Health Tech World. (16/04/2021)
Machine Learning Slows Down Future Pandemics MedIndia. (15/04/2021)
Machine Learning May Be Key to Avoiding the Next Possible Pandemic News18.com (15/04/2021)
Så kan AI bromsa nästa pandemi – svensk forskningförfinar testningen Computer Sweden (15/04/2021)
AI could prevent future pandemics Electronics360 (14/04/2021)
L’IA peut contribuer à limiter la propagation des infections lors des futures épidémies (étude) Ecofin Telecom. (14/04/2021)
Machine Learning can help slow down future pandemics:Study SocialNews.xyz (14/04/2021)
Machine learning can help slow down future pandemics —ScienceDaily Sortiwa Trending Viral News Portal (14/04/2021)
AI mot smittspridning Sveriges Radio Vetenskapsradion. (14/04/2021)

Quantitative Digital Microscopy with Deep Learning published in Applied Physics Reviews

Particle tracking and characterization in terms of radius and refractive index.

Quantitative Digital Microscopy with Deep Learning
Benjamin Midtvedt, Saga Helgadottir, Aykut Argun, Jesús Pineda, Daniel Midtvedt, Giovanni Volpe
Applied Physics Reviews 8, 011310 (2021)
doi: 10.1063/5.0034891
arXiv: 2010.08260

Video microscopy has a long history of providing insights and breakthroughs for a broad range of disciplines, from physics to biology. Image analysis to extract quantitative information from video microscopy data has traditionally relied on algorithmic approaches, which are often difficult to implement, time consuming, and computationally expensive. Recently, alternative data-driven approaches using deep learning have greatly improved quantitative digital microscopy, potentially offering automatized, accurate, and fast image analysis. However, the combination of deep learning and video microscopy remains underutilized primarily due to the steep learning curve involved in developing custom deep-learning solutions. To overcome this issue, we introduce a software, DeepTrack 2.0, to design, train and validate deep-learning solutions for digital microscopy. We use it to exemplify how deep learning can be employed for a broad range of applications, from particle localization, tracking and characterization to cell counting and classification. Thanks to its user-friendly graphical interface, DeepTrack 2.0 can be easily customized for user-specific applications, and, thanks to its open-source object-oriented programming, it can be easily expanded to add features and functionalities, potentially introducing deep-learning-enhanced video microscopy to a far wider audience.

Presentation by S. Helgadottir at the Gothenburg Science Festival, 2 October 2020

Logo of the Gothenburg Science Festival.

Saga Helgadottir will give a presentation at the Gothenburg Science Festival 2020.

The International Science Festival Gothenburg is one of Europe’s leading popular science events. Its first edition dates back to 1997, and it is held every year in spring.
This year the festival will take place during autumn, 28 September-4 October. Due to the current situation the festival will be a digital event. The digital festival will be available during the week of the festival.

The contribution of Saga Helgadottir will be presented according to the following schedule:

Saga Helgadottir
Deep Learning for Object Recognition
Deep Learning is a machine learning technique that teaches computers to do what comes naturally to humans: learn by example. In this talk, I will show how Deep Learning can be used to identify objects in images, in particular microscopic particles.

Date: 2 October 2020
Time: 18:08
Duration: 17′
Link: Deep Learning for Object Recognition

Links:
Vetenskapsfestivalen Göteborg (in Swedish)
The International Science Festival Gothenburg (in English)
Full Program

Diagnosis of a genetic disease improves with machine learning, a summary in Swedish published in Fysikaktuellt

Neural networks consist of a series of connected layers of neurons, whose connection weights are adjusted to learn how to determine the diagnosis from the input data.

A summary in Swedish of our previously published article “Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning” has been published in Fysikaktuellt, the journal of the Swedish Physical Society (Svenska fysikersamfundet).

Article: “Diagnostisering av sjukdomar förbättras med maskininlärning”, Saga Helgadottir, Giovanni Volpe and Stefano Romeo (in Swedish)

Original article: Virtual genetic diagnosis for familial hypercholesterolemia powered by machine learning

Press release: 
Algoritm lär sig diagnostisera genetisk sjukdom (in Swedish)
An algorithm that learns to diagnose genetic disease (in English)