Guided Acquisition of High-Quality Echocardiogram Using Deep Neural Networks

Objective: The quality of echocardiographic image acquisition is vital for precise quantifications and diagnostic accuracy. However, ultrasound equipment is limited in performance throughput and image quality. It is also governed by the operators’ acquisition competence. Although, a subjective quality control process is adopted for standard procedures; to provide optimal quality image, this further introduces major drawbacks in the degree of consistency, quantifications, and diagnostic accuracy. Materials and Methods: A deep neural network model was established that used a large data set containing 40,000 echocardiograms and implemented a guided tool for objective optimization of the apical two chamber (A2C), apical four chamber (A4C) and parasternal long axis (PLAX) images, based on clinical protocols. This tool provided real-time quality feedback on image adequacy and gave the operators’ image optimization experience, as they examined patients. Results: An average computational speed at 4.24 ms per frame, with 0.032% model error rate, was achieved on apical visibility, anatomical clarity, depth-gain, and foreshortening graded attributes. The novel pipeline was comparable to the operators’ ultrasound guidance system for quality image acquisition and reliable diagnosis in the health care system. Conclusion: The result of a guided acquisition provided novel evidence for an objective optimization process, optimal image quality, diagnostic accuracy, and improved users’ acquisition experiences, in clinical practice. A subjective assessment of a sub-optimal image quality has the potential to negatively impact patients’ clinical care.