Publications by authors named "Samuel Surette"
Article Synopsis
- A recent study tested a machine-learning algorithm to assess left ventricular ejection fraction (EF) using various echocardiographic views commonly available in point-of-care settings, as obtaining standard views can be challenging.
- The study involved comparing automated EF measurements against experienced echocardiographers' assessments from clinical examinations and showed good agreement, with the algorithm's accuracy being comparable to that of physicians.
- The results indicate that this algorithm can effectively classify LV function in a portable imaging setup, making it a valuable tool for point-of-care evaluations.
Importance: Artificial intelligence (AI) has been applied to analysis of medical imaging in recent years, but AI to guide the acquisition of ultrasonography images is a novel area of investigation. A novel deep-learning (DL) algorithm, trained on more than 5 million examples of the outcome of ultrasonographic probe movement on image quality, can provide real-time prescriptive guidance for novice operators to obtain limited diagnostic transthoracic echocardiographic images.
Objective: To test whether novice users could obtain 10-view transthoracic echocardiographic studies of diagnostic quality using this DL-based software.