Machine Learning is gaining a considerable momentum amongst scientific community more and more ML papers are being published in 2020 also it’s true capabilities are beginning to unfold in industries such as Digital Marketing, Communication, IT, etc. One of the largest areas that ML has been left behind for years is Health care and Medicine. The area where not only it’s financial benefits but also global health and wellbeing is being neglected. There are a lot of reasons to this for example lack of skilled individuals who have a proper understanding of health care and ML or lack of common ground to share these ideas between different scientists or lack of adequate data usable to ML.
Regarding the COVID19 pandemic in 2020, patient management and treatment have become a huge burden to hospitals and health care workers, and dispersing resources between patients can be a life or death issue. So now more than ever the help of AI in hospitals is needed.
From a clinical standpoint, lots of COVID19 patients have other comorbidities and Cardiac disorders are among the most serious of them, worth adding that a huge portion of drugs prescribed for COVID19 has Cardiac adverse effects which proven to be fetal. In many cases, COVID19 medication such as Hydroxychloroquine has caused Cardiac arrhythmias due to improper adjustment to a specific patient need or lack of proper cardiac monitoring or the worst case, neglected due to large hospitalized patients or overwhelmed staff. On the other hand, cardiac monitoring needs very expensive tools if done digitally. Or if done by a staff member consumes a very essential resource in Pandemic days in clinics which is time.
The first line, the most accessible and most cost-effective tool to assess cardiac activity in patients is ECG. Which is a rich source of information about Heart and general cardiac condition. What we propose is to build a platform in which Health workers around the could share their COVID19 patient’s ECG signals as a picture format using their smartphone right away without any technical difficulties then using research-proven methods converting them to digital signals with proper discretion. In the end forming a large Database of COVID19 ECG signals ready to use by ML experts to enable developing faster, more accurate, and more cost-effective monitoring tools to be used in clinics.
It is worth noting that any database large enough which can be easily accessible for ML purposes can open the way for innovation and technological advancement and indeed could be life-saving in managing and treating COVID19 patients with existing cardiac disease or even prevention of any cardiac injury during treatment.
As part of Global society, it is on us to clear the way to a better and more intelligent future in these harsh times.
By submitting this idea, we want to use the acceleration power of CERN accelerator, to accelerate this emergency kit to help the world as fast as possible.
1. Writing code for Digitization of Paper Electrocardiography Data (there are a lot of academic papers about the theory. So we only need to implement in an efficient way).
2. Building a website and embedding the written code in that website.
- Novel Tool for Complete Digitization of Paper Electrocardiography Data (10.1109/JTEHM.2013.2262024) 
- ECG Paper Records Digitization through Image Processing Techniques (10.5120/7411-0485) 
- A novel method for digitizing standard ECG papers (10.1109/ICCCE.2008.4580816) 
- Converting ECG and Other Paper Legated Biomedical Maps into Digital Signals (https://doi.org/10.1007/978-3-540-88188-9_3) 
- Digitizing paper electrocardiograms: Status and challenges (https://doi.org/10.1016/j.jelectrocard.2016.09.007) 
- Analysis on conversion process from paper record ECG to computer based ECG (10.15406/mojabb.2017.01.00011) 
- High Precision Digitization of Paper-Based ECG Records: A Step Toward Machine Learning (10.1109/JTEHM.2019.2949784) 
- Digitization of Paper Electrocardiogram: A Review (10.4018/978-1-5225-7525-2.ch009) 
- ECG Waveform Extraction from Paper Records (10.1007/978-3-319-71589-6_44) 
- Novel method of digitization of electrocardiogram signals (http://hdl.handle.net/1853/62860) 
- Conversion of ECG Graph into Digital Format (https://acadpubl.eu/jsi/2018-118-16-17/articles/17/31.pdf) 
- A Novel Method for the Conversion of Scanned Electrocardiogram (ECG) Image to Digital Signal (https://doi.org/10.1007/978-981-10-5520-1_34)