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AI Detects Unseen Signs of Heart Failure

Mount Sinai researchers created a special artificial intelligence (AI)-based computer algorithm that was able to learn how to identify subtle changes in electrocardiograms (also known as ECGs or EKGs) to predict whether a patient was experiencing heart failure. This study sought to develop Deep Learning (DL) models capable of comprehensively quantifying left and right ventricular dysfunction from ECG data in a large, diverse population.

We showed that deep-learning algorithms can recognise blood pumping problems on both sides of the heart from ECG waveform data. Ordinarily, diagnosing these types of heart conditions requires expensive and time-consuming procedures. We hope that this algorithm will enable a quicker diagnosis of heart failure.

– Benjamin S. Glicksberg, PhD, Assistant Professor of Genetics and Genomic Sciences

Affecting about 6.2 million Americans, heart failure or congestive heart failure occurs when the heart pumps less blood than the body normally needs. For years doctors have relied heavily on an imaging technique called an echocardiogram to assess whether a patient may be experiencing heart failure. While helpful, echocardiograms can be labour-intensive procedures that are only offered at select hospitals.

However, recent breakthroughs in AI suggest that electrocardiograms—a widely used electrical recording device—could be a fast and readily available alternative in these cases. For instance, many studies have shown how a deep-learning algorithm can detect weakness in the heart’s left ventricle, which pushes freshly oxygenated blood out to the rest of the body.

In this study, the researchers described the development of an algorithm that not only assessed the strength of the left ventricle but also the right ventricle, which takes deoxygenated blood streaming in from the body and pumps it to the lungs. This study represents an exciting step forward in finding information hidden within the ECG data which can lead to better screening and treatment paradigms using a relatively simple and widely available test.

For this study, the researchers programmed a computer to read patient electrocardiograms along with data extracted from written reports summarising the results of corresponding echocardiograms taken from the same patients. In this situation, the written reports acted as a standard set of data for the computer to compare with the electrocardiogram data and learn how to spot weaker hearts.

Natural language processing programs helped the computer extract data from the written reports. Meanwhile, special neural networks capable of discovering patterns in images were incorporated to help the algorithm learn to recognise pumping strengths.

Initial results suggested that the algorithm was effective at predicting which patients would have either healthy or very weak left ventricles. Strength was defined by left ventricle ejection fraction, an estimate of how much fluid the ventricle pumps out with each beat as observed on echocardiograms. Healthy hearts have an ejection fraction of 50% or greater while weak hearts have ones that are equal to or below 40%.

The algorithm was 94% accurate at predicting which patients had a healthy ejection fraction and 87% accurate at predicting those who had an ejection fraction that was below 40%. However, the algorithm was not as effective at predicting which patients would have slightly weakened hearts.

The overall results suggest that this algorithm could be a useful tool for helping clinical practitioners combat heart failure suffered by a variety of patients. The researchers are in the process of carefully designing prospective trials to test out their effectiveness in a more real-world setting.

As reported by OpenGov Asia, AI can learn to solve all sorts of problems, but whether these powerful, pattern-recognising algorithms actually understand the tasks they are performing remains an open question. Researchers at MIT have now shown that a certain type of AI can learn the true cause-and-effect structure of the navigation task it is being trained to perform.

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