How Artificial Intelligence is Enhancing Overall Medical Outcomes in Cardiology?

Roger Brown

Artificial Intelligence is transforming the healthcare industry by allowing medical practitioners to diagnose and treat any malady at a much faster pace. In the medical process, there were times when the distance between diagnosing diseases and their treatment was very big. It took time to identify and treat disease. In order to identify the exact ailment, a patient had to undergo several tests in the labs and then only doctors could identify and treat a particular disease. And in the entire process, there were high chances of unintentional human-generated errors and to get rid of these kinds of errors and problems, AI-enabled models came to the rescue.

In today’s scenario, the computer vision models are assisting the medical practitioners to a great extent, providing real-time accurate insights and solutions which directly enhance the overall outcome in several medical diagnosis verticals like Radiology, Pathology, Cardiology, Neurology, Life Sciences and among others. Deep-learning technology is revolutionizing the operational process of the healthcare industry opening more opportunities for automation into various sub-fields.

AI in Cardiology

Artificial Intelligence is the ability to make machines learn to solve complex problems and to complete the assigned task without any human intervention. Combining AI with clinical practice will enhance the overall results, reducing the efforts of the practitioners to a great extent and assisting them in the decision-making process.

AI-enabled machines have made a great impact in Cardiology, especially to identify and monitor heart problems. Although, there are several tests to identify a heart ailment, the simplest and easiest way to identify the malady is the electrocardiogram (ECG). An electrocardiogram is a crucial tool in the medical field which helps in identifying & diagnosing heart condition. During this process, sensors are placed on the chest and arms so that they can detect the electrical activity of the heart.

ECG is Helpful to Detect Several Heart Ailments Such As:

• Irregularities in heart rhythm (arrhythmias)

• Blocked or narrowed arteries in the heart

• Structural problems with heart's chambers

• A previous heart attack, etc.

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Now, to build a successful AI-enabled model, one has to have quality training/labelled data, which can be fed into machine learning algorithms so that machines/ computer vision can accurately understand, identify and perform the assigned task. The original data can be acquired through data collection and then data cleaning and data annotation come into the picture and after that, the annotated data is fed into the machine learning algorithm for invocation. Annotations are the specific labels that point to the target object in the image or the given data, describing and recording that particular event.

The same is the case with the data which is collected through ECG. In its AI application, a machine vision camera and sensors record, observe and interpret the ECG Waveforms for rhythm anomalies or heartbeat classification. In order to enhance productivity and results, several annotation methods can be used in this vertical also. For example, if someone wants to annotate the ECG report then one can use several annotation methods but generally, the polylines annotation method and keypoint annotation method are used. One can mark the endpoints of the lines shown in the below figure and labelled them and then annotated ECG images/data can be used to train the AI-enabled models. As per the requirement, one can use other annotation methods as well. Talking about keypoint annotations, as per the Association for the Advancement of Medical Instrumentation (AAMI) standards for ECG classifiers, all heartbeat annotation are labelled into five categories: Normal beat (N), Supraventricular ectopic beat (S), Ventricular ectopic beat (V), Fusion beat (F), Unknown beat (Q).

Read More: How Computer Vision Can Improve Accuracy of Diagnosis in Medical Imaging Analysis?

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Quality Outcomes with Medical AI

Quality directly reflects the overall outcome. If the quality training data is given to computer vision models then the result will also be satisfactory. So, to build a successful AI model one has to have good quality training data. The ECG data records require pre-processing, labelling and feature extraction such as ECG Sampling, Filtering, Beat Detection, Segmentation and Normalization. 

There are several benefits of incorporating AI into the medical field. Let's have a look at the top three benefits.

1. Minimizes the human error - With the correct datasets, AI can minimize unwanted human errors. Well-trained machines can easily locate and identify the errors which human might miss.

2. Provides quality healthcare - It will assist the medical practitioners in decision making and helps to improve the quality of healthcare with the help of the enormous data which is given to machine’s algorithms.

3. A well-trained machine will help medical practitioners to identify the ailment early and treat it quickly. In short, it will shorten the distance between diagnosis and treatment.

In current times, investment in AI has seen a rapid increase. The healthcare sector is now taking long strides and are implementing AI in their activities and workings. However, computer vision-based models require the processing of huge amount of images or videos.  Moreover, it is of crucial importance that this data is correctly labelled and annotated for higher accuracy of the model. In order to save time and effort on annotations, enterprises are increasingly outsourcing their training data requirements to professional training data companies such as Cogito Tech, which has a rich experience and forte in medical annotation training material.

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Hi, My name is Roger Brown and I work at Cogito as Strategic Partnerships Lead. I am an expert at comprehending & processing training data needs for organizations from various industries & sectors involved in Machine Learning, AI or NLP implementations. I lead highly motivated teams of data annotators , labellers and content moderators for processing multiple data-sets during the day and write about my learnings and share insights & solutions by the night.

Levittown, NY
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