How can AI and deep learning improve diagnostics using cardiac ultrasound in these cases?
Di Vece: Echocardiography plays a pivotal role in the diagnosis of suspected TTS in patients. The main morphologic hallmark of TTS is the presence of transient myocardial wall motion abnormalities, usually extending beyond the area of a single epicardial coronary artery distribution. The identification of specific patterns of regional wall motion abnormalities can support the cardiologist in identifying TTS. It can be assumed that artificial intelligence detects new patterns to distinguish between TTS and acute myocardial infarction and enhance the diagnostic value of echocardiography as it pertains to a differential diagnosis.
The study showed that AI was more accurate in the applied data sets than experienced human cardiologists. While we have still a long way to go before the technology can be used in everyday clinical practice, what would a "collaboration" between AI and cardiovascular specialists look like in the future (as it pertains to acute myocardial infarction vs. Takotsubo syndrome)?
Di Vece: The skillful use of AI by trained specialists can improve the quality of the images taken. It results in more data for processing that would otherwise been unusable. Not only does this save time, but also means a cost reduction. That being said, patients benefit the most from this because AI can become another tool to facilitate a more accurate diagnosis and subsequent personalized treatment. The latter aspect is especially interesting when it comes to more complex cases with multiple comorbidities.
Keeping all these aspects in mind, the use of AI in cardiovascular imaging seems like a tool for cardiology experts that can serve clinical intelligence, though it is no replacement or substitute for the knowledge and experience of a skilled cardiologist.
The cardiologist must augment the use of AI with other clinical elements as it pertains to the differential diagnosis between myocardial infarction and Takotsubo syndrome. Indeed, our ultimate goal is to develop a comprehensive algorithm that comprises multiple parameters such as – among others – imaging data, demographic data, ECG, and biomarkers as a non-invasive tool to detect TTS. If coronary angiography is not immediately available, a plausible determination of the pretest probability of TTS can support prognostic stratification and help in determining subsequent medical treatment.