Making Artificial Intelligence Lemonade Out of Data Lemons: Adaptation of a Public Apical Echo Database for Creation of a Subxiphoid Visual Estimation Automatic Ejection Fraction Machine Learning Algorithm.

Objectives: A paucity of point-of-care ultrasound (POCUS) databases limits machine learning (ML). Assess feasibility of training ML algorithms to visually estimate left ventricular ejection fraction (EF) from a subxiphoid (SX) window using only apical 4-chamber (A4C) images.

Methods: Researchers used a long-short-term-memory algorithm for image analysis.

Deep Learning Pitfall: Impact of Novel Ultrasound Equipment Introduction on Algorithm Performance and the Realities of Domain Adaptation.

Objectives: To test deep learning (DL) algorithm performance repercussions by introducing novel ultrasound equipment into a clinical setting.

Methods: Researchers introduced prospectively obtained inferior vena cava (IVC) videos from a similar patient population using novel ultrasound equipment to challenge a previously validated DL algorithm (trained on a common point of care ultrasound [POCUS] machine) to assess IVC collapse. Twenty-one new videos were obtained for each novel ultrasound machine.

Artificial intelligence versus expert: a comparison of rapid visual inferior vena cava collapsibility assessment between POCUS experts and a deep learning algorithm.

Objectives: We sought to create a deep learning algorithm to determine the degree of inferior vena cava (IVC) collapsibility in critically ill patients to enable novice point-of-care ultrasound (POCUS) providers.

Methods: We used publicly available long short term memory (LSTM) deep learning basic architecture that can track temporal changes and relationships in real-time video, to create an algorithm for ultrasound video analysis. The algorithm was trained on public domain IVC ultrasound videos to improve its ability to recognize changes in varied ultrasound video.