AI Platform
Analyzing Electrocardiograms + Machine and Deep Learning =
Early Diagnosis and Treatment of Cardiovascular Diseases in Cancer Patients CARDIOCN is changing the way Oncology is Practiced!
CARDIOCAN is changing the way Oncology is Practiced!
Cardiovascular disease research and diagnosis in Cancer patients are being redefined by new technology: a technology that promises computational methodology to reduce human error and increase personalized medicine and streamline treatments to drive unprecedented results in Cancer patients. CardioCan is pushing its technology past the limits of traditional lab methodologies in diagnosing cardiovascular diseases in Cancer patients with its digital platform and pipeline of AI applications. Developed by a combination of medical advisors and software developers, CardioCan is transforming the economics and practice of oncology, putting the power of early diagnosis and treatment of cardiovascular diseases using data-centric algorithms to work in the fight against Cancer.
Our Mission – Supporting Cardiovascular Diseases using AI (machine and deep learning)
The number of cancer deaths worldwide is staggering—9.6 million in 2018, according to the UICC! In Canada alone, 30% of all deaths are due to cancer yearly. Our team at CardioCan is driven and motivated to help fight these various forms of cancers worldwide using advanced and innovative software tools for fast and effective early diagnosis and treatment of cardiovascular diseases in Cancer patients. We hope to contribute to the fight for eradicating and preventing cancers by collaborating with medical professionals and researchers worldwide. Using Deep Learning (DL) and Machine Learning (ML) platforms, we have developed specialized algorithmic solutions to analyze Electrocardiograms (ECG) for oncology for faster and better assessment than human accuracy. Our solutions, both in API and Web Platforms, are designed to offer faster and better access for second and third diagnostic opinions by medical professionals to help develop a therapeutic approach quickly. This will help the patients and physicians by reducing anxiety caused by not knowing what they are dealing with and will result in selecting the best treatment method personalized to the patient’s situation. Our solutions do all the work—consider the type of cancer the patient is dealing with, identify a correlation between cancer and ECG behavior, discovers patterns and trends, and analyze it using various algorithms. It’s a lot but not enough, as we work to expand its reach and provide treatment advice for our patients using our platform. You focus on saving lives, and We focus on early diagnosis!
Technologies involved
Platform Data Mining
Our platform’s data mining process begins with data pre-processing to remove noise. After pre-processing the data, the software extracts relevant electrocardiogram (ECG) features using a meta-heuristic algorithm which leads to feature dimension reduction. Then, perceptron, a neural network, is applied to classify and later select the features. The selected features will then be used to discern patterns and detect/predict the ailment. Association rules mining will be applied to provide information in assessing significant correlations and predictions of future possible heart conditions.
Although ECG is currently used as an assessment method for cardiac toxicity, it goes without saying that machine analysis of ECG through data mining and machine learning is more accurate and reliable compared to human-led study, as the software can detect the slightest changes in the ECG signals which are not generally discerned by the unaided eye. Furthermore, the software is designed in a way that it can be synced with available ECG wearables, including Apple Watch Series 5, Withings Move ECG, Samsung Galaxy Watch Active 2, Amazfit Verge 2, QardioCore, Hexoskin Smart, etc., and provide practitioners with real-time and permanent data on a patient’s cardiovascular status.
Based upon the result of the ECG analysis, the practitioners will be able to diagnose cardiotoxicity in the early stages of the disease and plan the anticancer treatment accordingly.