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<!DOCTYPE html>
<html lang="" xml:lang="">
<head>
<title>All-in-one smartphone-based system for quantitative analysis of point-of-care diagnostics</title>
<meta charset="utf-8" />
<meta name="author" content="Weronika Schary and Filip Paskali" />
<meta name="date" content="2021-07-05" />
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<textarea id="source">
class: center, middle, inverse, title-slide
# All-in-one smartphone-based system for quantitative analysis of point-of-care diagnostics
### Weronika Schary and Filip Paskali
### Institute of Precision Medicine, HFU
### 2021-07-05
---
### Cooperation of Research Groups
--
#### Data Science for Life Science, Institute of Precision Medicine, HFU
Prof. Matthias Kohl, Weronika Schary, Filip Paskali
`\(\rightarrow\)` Data Science, Bioinformatics
--
#### Deigner Lab, Institute of Precision Medicine, HFU
Prof. Hans-Peter Deigner, Simone Rentschler, Christoph Ruppert
`\(\rightarrow\)` Development of Point-of-Care (POC) Diagnostics, Experiments
---
### Cooperation of Research Groups
--
#### Institute of Hygiene, Microbiology and Environmental Medicine, Medical University Graz
Prof. Ivo Steinmetz, Dr. Gabriel Wagner-Lichtenegger
`\(\rightarrow\)` Development of Point-of-Care (POC) Diagnostics, Experiments
---
### POC Diagnostics
--
- POC tests are performed at the site of patient care, e.g. bedside, and offer straightforward results
--
- in particular lateral flow assays (LFA) are a great opportunity for rapid, low-cost, accessible diagnosis
--
- LFAs contain a carrier material with dry reagents activated by a liquid sample; this simple test setup creates the bands
--
.pull-left[
.footnote[
Melioidosis DS rapid test
see Wagner et al. 2020 [1](https://github.com/yihui/xaringan/issues/2)]
]
.pull-right[
.footnote[
Pregnancy Test [2](htthttps://upload.wikimedia.org/wikipedia/commons/1/1f/Pregnancy_test_result.jpg)
]
]
---
### Smartphone-Reader for POC Analysis
--
- 3D-printed photo box for standardized positioning and lighting
--
- smartphone for image acquisition
--
- R Shiny App with modular, customizable workflow for image editing, analysis, data extraction, calibration and quantification
--
.center[
]
---
### LFA Shiny Application
- LFA App core `\(-\)` image editing, thresholding, intensity extraction
- LFA App calibration `\(-\)` core + calibration
- LFA App quantification `\(-\)` core + quantification
- LFA App analysis `\(-\)` core + calibration + quantification
--
.center[[See demo](https://www.youtube.com/watch?v=BNL1W5slZnY) --- [Try the app](https://lfapp.shinyapps.io/LFAnalysis/) --- [Code on GitHub](https://github.com/fpaskali/LFApp)]
.center[]
---
### Outlook and further Projects
- Optimization and further improvements based on feedback
- Apps for Spot Detection for Array Formats
.center[
]
---
### Outlook and further Projects
- Automatic Spot or Band Recognition via Machine Learning
- Use for other applications with spots or band, e.g. Gelelectrophoresis images
Support was provided by Bundesministerium für Bildung und Forschung (FlowArray project 13FH121PX8, MultiFlow project 03FH046PX4).
---
### R Packages & other Ressources .pull-left[
- [**shiny**](https://cran.r-project.org/package=shiny)
- [**shinyMobile**](https://cran.r-project.org/package=shinyMobile)
- [**shinyjs**](https://cran.r-project.org/package=shinyjs)
- [**shinythemes**](https://cran.r-project.org/package=shinythemes)
- [**EBImage**](https://bioconductor.org/packages/release/bioc/html/EBImage.html)
- [**rmarkdown**](https://cran.r-project.org/package=rmarkdown)
- [**DT**](https://cran.r-project.org/package=DT)
- [**ggplot2**](https://cran.r-project.org/package=ggplot2)
- [**fs**](https://cran.r-project.org/package=fs)
- [**mgcv**](https://cran.r-project.org/package=mgcv)]
.pull-right[
- [**Mahmoud et al. 2020**](https://doi.org/10.1016/j.snb.2020.129246)
- [**Ruppert et al. 2020**](https://doi.org/10.1186/s12951-020-00688-1)
- [**Ruppert et al. 2019**](https://doi.org/10.1007/s00604-018-3195-6)
- [**Wagner et al. 2020**](https://doi.org/10.1371/journal.pntd.0008452)]
---
class: center, middle
# Thank you!
Slides created via the R package [**xaringan**](https://github.com/yihui/xaringan).
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