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Article ; Online: An individually adjusted approach for communicating epidemiological results on health and lifestyle to patients.

Waaler, Per Niklas / Bongo, Lars Ailo / Rolandsen, Christina / Lorem, Geir F

2024 Volume 14, Issue 1, Page(s) 3199

Abstract: If scientific research on modifiable risk factors was more accessible to the general population there is a potential to prevent disease and promote health. Mobile applications can automatically combine individual characteristics and statistical models of ...

Abstract	If scientific research on modifiable risk factors was more accessible to the general population there is a potential to prevent disease and promote health. Mobile applications can automatically combine individual characteristics and statistical models of health to present scientific information as individually tailored visuals, and thus there is untapped potential in incorporating scientific research into apps aimed at promoting healthier lifestyles. As a proof-of-concept, we develop a statistical model of the relationship between Self-rated-health (SRH) and lifestyle-related factors, and a simple app for conveying its effects through a visualisation that sets the individual as the frame of reference. Using data from the 6th (n = 12 981, 53.4% women and 46.6% men) and 7th (n = 21 083, 52.5% women and 47.5% men) iteration of the Tromsø population survey, we fitted a mixed effects linear regression model that models mean SRH as a function of self-reported intensity and frequency of physical activity (PA), BMI, mental health symptoms (HSCL-10), smoking, support from friends, and HbA1c ≥ 6.5%. We adjusted for socioeconomic and demographic factors and comorbidity. We designed a simple proof-of-concept app to register relevant user information, and use the SRH-model to translate the present status of the user into suggestions for lifestyle changes along with predicted health effects. SRH was strongly related to modifiable health factors. The strongest modifiable predictors of SRH were mental health symptoms and PA. The mean adjusted difference in SRH between those with 10-HSCL index = 1.85 (threshold for mental distress) and HSCL-10 = 1 was 0.59 (CI 0.61-0.57). Vigorous physical activity (exercising to exhaustion ≥ 4 days/week relative to sedentary) was associated with an increase on the SRH scale of 0.64 (CI 0.56-0.73). Physical activity intensity and frequency interacted positively, with large PA-volume (frequency ⨯ intensity) being particularly predictive of high SRH. Incorporating statistical models of health into lifestyle apps have great potential for effectively communicating complex health research to a general audience. Such an approach could improve lifestyle apps by helping to make the recommendations more scientifically rigorous and personalised, and offer a more comprehensive overview of lifestyle factors and their importance.
MeSH term(s)	Female ; Humans ; Male ; Exercise ; Health Promotion ; Health Status ; Life Style ; Self Report
Language	English
Publishing date	2024-02-08
Publishing country	England
Document type	Journal Article
ZDB-ID	2615211-3
ISSN	2045-2322 ; 2045-2322
ISSN (online)	2045-2322
ISSN	2045-2322
DOI	10.1038/s41598-024-53275-x
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Publicly available datasets of breast histopathology H&E whole-slide images: A scoping review.

Tafavvoghi, Masoud / Bongo, Lars Ailo / Shvetsov, Nikita / Busund, Lill-Tove Rasmussen / Møllersen, Kajsa

Journal of pathology informatics

2024 Volume 15, Page(s) 100363

Abstract: Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer ... ...

Abstract	Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E-stained whole-slide images (WSIs) that can be used to develop deep learning algorithms. We systematically searched 9 scientific literature databases and 9 research data repositories and found 17 publicly available datasets containing 10 385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled 2 lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata.
Language	English
Publishing date	2024-02-01
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2579241-6
ISSN	2153-3539 ; 2229-5089
ISSN (online)	2153-3539
ISSN	2229-5089
DOI	10.1016/j.jpi.2024.100363
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Article ; Online: Social robots in research on social and cognitive development in infants and toddlers: A scoping review.

Flatebø, Solveig / Tran, Vi Ngoc-Nha / Wang, Catharina Elisabeth Arfwedson / Bongo, Lars Ailo

PloS one

2024 Volume 19, Issue 5, Page(s) e0303704

Abstract: There is currently no systematic review of the growing body of literature on using social robots in early developmental research. Designing appropriate methods for early childhood research is crucial for broadening our understanding of young children's ... ...

Abstract	There is currently no systematic review of the growing body of literature on using social robots in early developmental research. Designing appropriate methods for early childhood research is crucial for broadening our understanding of young children's social and cognitive development. This scoping review systematically examines the existing literature on using social robots to study social and cognitive development in infants and toddlers aged between 2 and 35 months. Moreover, it aims to identify the research focus, findings, and reported gaps and challenges when using robots in research. We included empirical studies published between 1990 and May 29, 2023. We searched for literature in PsychINFO, ERIC, Web of Science, and PsyArXiv. Twenty-nine studies met the inclusion criteria and were mapped using the scoping review method. Our findings reveal that most studies were quantitative, with experimental designs conducted in a laboratory setting where children were exposed to physically present or virtual robots in a one-to-one situation. We found that robots were used to investigate four main concepts: animacy concept, action understanding, imitation, and early conversational skills. Many studies focused on whether young children regard robots as agents or social partners. The studies demonstrated that young children could learn from and understand social robots in some situations but not always. For instance, children's understanding of social robots was often facilitated by robots that behaved interactively and contingently. This scoping review highlights the need to design social robots that can engage in interactive and contingent social behaviors for early developmental research.
MeSH term(s)	Humans ; Robotics ; Infant ; Child Development/physiology ; Cognition/physiology ; Child, Preschool ; Social Behavior
Language	English
Publishing date	2024-05-15
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0303704
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Article: META-pipe Authorization service.

Raknes, Inge Alexander / Bongo, Lars Ailo

F1000Research

2018 Volume 7

Abstract: We describe the design, implementation, and use of the META-pipe Authorization service. META-pipe is a complete workflow for the analysis of marine metagenomics data. We will provide META-pipe as a web based data analysis service for ELIXIR users. We ... ...

Abstract	We describe the design, implementation, and use of the META-pipe Authorization service. META-pipe is a complete workflow for the analysis of marine metagenomics data. We will provide META-pipe as a web based data analysis service for ELIXIR users. We have integrated our Authorization service with the ELIXIR Authorization and Authentication Infrastructure (AAI) that allows single sign-on to services across the ELIXIR infrastructure. We use the Authorization service to authorize access to data on the META-pipe storage system and jobs in the META-pipe job queue. Our Authorization server was among the first SAML2 service providers that integrated with ELIXIR AAI. The code is open source at: https://gitlab.com/uit-sfb/AuthService2.
Language	English
Publishing date	2018-01-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2699932-8
ISSN	2046-1402
ISSN	2046-1402
DOI	10.12688/f1000research.13256.2
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Book ; Online: GeneNet VR

Fernández, Álvaro Martínez / Bongo, Lars Ailo / Pedersen, Edvard

Interactive visualization of large-scale biological networks using a standalone headset

2021

Abstract: Visualizations are an essential part of biomedical analysis result interpretation. Often, interactive networks are used to visualize the data. However, the high interconnectivity, and high dimensionality of the data often results in information overload, ...

Abstract	Visualizations are an essential part of biomedical analysis result interpretation. Often, interactive networks are used to visualize the data. However, the high interconnectivity, and high dimensionality of the data often results in information overload, making it hard to interpret the results. To address the information overload problem, existing solutions typically either use data reduction, reduced interactivity, or expensive hardware. We propose using the affordable Oculus Quest Virtual Reality (VR) headset for interactive visualization of large-scale biological networks. We present the design and implementation of our solution, GeneNet VR, and we evaluate its scalability and usability using large gene-to-gene interaction networks. We achieve the 72 FPS required by the Oculus performance guidelines for the largest of our networks (2693 genes) using both a GPU and the Oculus Quest standalone. We found from our interviews with biomedical researchers that GeneNet VR is innovative, interesting, and easy to use for novice VR users. We believe affordable hardware like the Oculus Quest has a big potential for biological data analysis. However, additional work is required to evaluate its benefits to improve knowledge discovery for real data analysis use cases. GeneNet VR is open-sourced: https://github.com/kolibrid/GeneNet-VR. A video demonstrating GeneNet VR used to explore large biological networks: https://youtu.be/N4QDZiZqVNY.
Keywords	Computer Science - Graphics ; Computer Science - Human-Computer Interaction ; Computer Science - Social and Information Networks
Subject code	004
Publishing date	2021-09-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Publicly available datasets of breast histopathology H&E whole-slide images

Tafavvoghi, Masoud / Bongo, Lars Ailo / Shvetsov, Nikita / Busund, Lill-Tove Rasmussen / Møllersen, Kajsa

A scoping review

2023

Abstract	Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast cancer is a big challenge that limits the development of accurate and robust deep learning models. In this scoping review, we identified the publicly available datasets of breast H&E stained whole-slide images (WSI) that can be used to develop deep learning algorithms. We systematically searched nine scientific literature databases and nine research data repositories and found 17 publicly available datasets containing 10385 H&E WSIs of breast cancer. Moreover, we reported image metadata and characteristics for each dataset to assist researchers in selecting proper datasets for specific tasks in breast cancer computational pathology. In addition, we compiled two lists of breast H&E patches and private datasets as supplementary resources for researchers. Notably, only 28% of the included articles utilized multiple datasets, and only 14% used an external validation set, suggesting that the performance of other developed models may be susceptible to overestimation. The TCGA-BRCA was used in 52% of the selected studies. This dataset has a considerable selection bias that can impact the robustness and generalizability of the trained algorithms. There is also a lack of consistent metadata reporting of breast WSI datasets that can be an issue in developing accurate deep learning models, indicating the necessity of establishing explicit guidelines for documenting breast WSI dataset characteristics and metadata. Comment: 27 pages (including references), 8 figures, 3 tables, 5 supporting information materials
Keywords	Electrical Engineering and Systems Science - Image and Video Processing ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Machine Learning ; 68T01 General topics in artificial intelligence ; I.2.0
Subject code	006
Publishing date	2023-06-02
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: More efficient manual review of automatically transcribed tabular data

Pedersen, Bjørn-Richard / Johansen, Rigmor Katrine / Holsbø, Einar / Sommerseth, Hilde / Bongo, Lars Ailo

2023

Abstract: Machine learning methods have proven useful in transcribing historical data. However, results from even highly accurate methods require manual verification and correction. Such manual review can be time-consuming and expensive, therefore the objective of ...

Abstract	Machine learning methods have proven useful in transcribing historical data. However, results from even highly accurate methods require manual verification and correction. Such manual review can be time-consuming and expensive, therefore the objective of this paper was to make it more efficient. Previously, we used machine learning to transcribe 2.3 million handwritten occupation codes from the Norwegian 1950 census with high accuracy (97%). We manually reviewed the 90,000 (3%) codes with the lowest model confidence. We allocated those 90,000 codes to human reviewers, who used our annotation tool to review the codes. To assess reviewer agreement, some codes were assigned to multiple reviewers. We then analyzed the review results to understand the relationship between accuracy improvements and effort. Additionally, we interviewed the reviewers to improve the workflow. The reviewers corrected 62.8% of the labels and agreed with the model label in 31.9% of cases. About 0.2% of the images could not be assigned a label, while for 5.1% the reviewers were uncertain, or they assigned an invalid label. 9,000 images were independently reviewed by multiple reviewers, resulting in an agreement of 86.43% and disagreement of 8.96%. We learned that our automatic transcription is biased towards the most frequent codes, with a higher degree of misclassification for the lowest frequency codes. Our interview findings show that the reviewers did internal quality control and found our custom tool well-suited. So, only one reviewer is needed, but they should report uncertainty. Comment: 19 pages, 5 figures, 1 table
Keywords	Computer Science - Machine Learning ; Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Human-Computer Interaction
Publishing date	2023-06-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Algorithm for predicting valvular heart disease from heart sounds in an unselected cohort.

Waaler, Per Niklas / Melbye, Hasse / Schirmer, Henrik / Johnsen, Markus Kreutzer / Donnem, Tom / Ravn, Johan / Andersen, Stian / Davidsen, Anne Herefoss / Aviles Solis, Juan Carlos / Stylidis, Michael / Bongo, Lars Ailo

Frontiers in cardiovascular medicine

2024 Volume 10, Page(s) 1170804

Abstract: Objective: This study aims to assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a general population that includes asymptomatic cases and intermediate stages ... ...

Abstract	Objective: This study aims to assess the ability of state-of-the-art machine learning algorithms to detect valvular heart disease (VHD) from digital heart sound recordings in a general population that includes asymptomatic cases and intermediate stages of disease progression. Methods: We trained a recurrent neural network to predict murmurs from heart sound audio using annotated recordings collected with digital stethoscopes from four auscultation positions in 2,124 participants from the Tromsø7 study. The predicted murmurs were used to predict VHD as determined by echocardiography. Results: The presence of aortic stenosis (AS) was detected with a sensitivity of 90.9%, a specificity of 94.5%, and an area under the curve (AUC) of 0.979 (CI: 0.963-0.995). At least moderate AS was detected with an AUC of 0.993 (CI: 0.989-0.997). Moderate or greater aortic and mitral regurgitation (AR and MR) were predicted with AUC values of 0.634 (CI: 0.565-703) and 0.549 (CI: 0.506-0.593), respectively, which increased to 0.766 and 0.677 when clinical variables were added as predictors. The AUC for predicting symptomatic cases was higher for AR and MR, 0.756 and 0.711, respectively. Screening jointly for symptomatic regurgitation or presence of stenosis resulted in an AUC of 0.86, with 97.7% of AS cases ( Conclusions: The algorithm demonstrated excellent performance in detecting AS in a general cohort, surpassing observations from similar studies on selected cohorts. The detection of AR and MR based on HS audio was poor, but accuracy was considerably higher for symptomatic cases, and the inclusion of clinical variables improved the performance of the model significantly.
Language	English
Publishing date	2024-01-24
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2781496-8
ISSN	2297-055X
ISSN	2297-055X
DOI	10.3389/fcvm.2023.1170804
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Article ; Online: Reproduction study using public data of: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.

Voets, Mike / Møllersen, Kajsa / Bongo, Lars Ailo

PloS one

2019 Volume 14, Issue 6, Page(s) e0217541

Abstract: We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re- ... ...

Abstract	We have attempted to reproduce the results in Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, published in JAMA 2016; 316(22), using publicly available data sets. We re-implemented the main method in the original study since the source code is not available. The original study used non-public fundus images from EyePACS and three hospitals in India for training. We used a different EyePACS data set from Kaggle. The original study used the benchmark data set Messidor-2 to evaluate the algorithm's performance. We used another distribution of the Messidor-2 data set, since the original data set is no longer available. In the original study, ophthalmologists re-graded all images for diabetic retinopathy, macular edema, and image gradability. We have one diabetic retinopathy grade per image for our data sets, and we assessed image gradability ourselves. We were not able to reproduce the original study's results with publicly available data. Our algorithm's area under the receiver operating characteristic curve (AUC) of 0.951 (95% CI, 0.947-0.956) on the Kaggle EyePACS test set and 0.853 (95% CI, 0.835-0.871) on Messidor-2 did not come close to the reported AUC of 0.99 on both test sets in the original study. This may be caused by the use of a single grade per image, or different data. This study shows the challenges of reproducing deep learning method results, and the need for more replication and reproduction studies to validate deep learning methods, especially for medical image analysis. Our source code and instructions are available at: https://github.com/mikevoets/jama16-retina-replication.
MeSH term(s)	Databases, Factual ; Deep Learning ; Diabetic Retinopathy/diagnostic imaging ; Female ; Fluorescein Angiography ; Fundus Oculi ; Humans ; Image Processing, Computer-Assisted ; India ; Male
Language	English
Publishing date	2019-06-06
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Validation Study
ZDB-ID	2267670-3
ISSN	1932-6203 ; 1932-6203
ISSN (online)	1932-6203
ISSN	1932-6203
DOI	10.1371/journal.pone.0217541
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Article: A Pragmatic Machine Learning Approach to Quantify Tumor-Infiltrating Lymphocytes in Whole Slide Images.

Shvetsov, Nikita / Grønnesby, Morten / Pedersen, Edvard / Møllersen, Kajsa / Busund, Lill-Tove Rasmussen / Schwienbacher, Ruth / Bongo, Lars Ailo / Kilvaer, Thomas Karsten

Cancers

2022 Volume 14, Issue 12

Abstract: Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to ...

Abstract	Increased levels of tumor-infiltrating lymphocytes (TILs) indicate favorable outcomes in many types of cancer. The manual quantification of immune cells is inaccurate and time-consuming for pathologists. Our aim is to leverage a computational solution to automatically quantify TILs in standard diagnostic hematoxylin and eosin-stained sections (H&E slides) from lung cancer patients. Our approach is to transfer an open-source machine learning method for the segmentation and classification of nuclei in H&E slides trained on public data to TIL quantification without manual labeling of the data. Our results show that the resulting TIL quantification correlates to the patient prognosis and compares favorably to the current state-of-the-art method for immune cell detection in non-small cell lung cancer (current standard CD8 cells in DAB-stained TMAs HR 0.34, 95% CI 0.17-0.68 vs. TILs in HE WSIs: HoVer-Net PanNuke Aug Model HR 0.30, 95% CI 0.15-0.60 and HoVer-Net MoNuSAC Aug model HR 0.27, 95% CI 0.14-0.53). Our approach bridges the gap between machine learning research, translational clinical research and clinical implementation. However, further validation is warranted before implementation in a clinical setting.
Language	English
Publishing date	2022-06-16
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2527080-1
ISSN	2072-6694
ISSN	2072-6694
DOI	10.3390/cancers14122974
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