Use of artificial intelligence as a didactic tool to improve ejection fraction assessment in the emergency department: A randomized controlled pilot study

Concordance of randomised controlled trials for artificial intelligence interventions with the CONSORT-AI reporting guidelines

The Consolidated Standards of Reporting Trials extension for Artificial Intelligence interventions (CONSORT-AI) was published in September 2020. Since its publication, several randomised controlled trials (RCTs) of AI interventions have been published but their completeness and transparency of reporting is unknown. This systematic review assesses the completeness of reporting of AI RCTs following publication of CONSORT-AI and provides a comprehensive summary of RCTs published in recent years. 65 RCTs were identified, mostly conducted in China (37%) and USA (18%). Median concordance with CONSORT-AI reporting was 90% (IQR 77-94%), although only 10 RCTs explicitly reported its use. Several items were consistently under-reported, including algorithm version, accessibility of the AI intervention or code, and references to a study protocol. Only 3 of 52 included journals explicitly endorsed or mandated CONSORT-AI. Despite a generally high concordance amongst recent AI RCTs, some AI-specific considerations remain systematically poorly reported. Further encouragement of CONSORT-AI adoption by journals and funders may enable more complete adoption of the full CONSORT-AI guidelines.

Training, Competency, and Interdisciplinary Collaboration in Point-of-Care Ultrasound

Point-of-care ultrasound can provide critical diagnostic information and add to procedural safety. As with any clinical skill, however, it must be applied by an adequately trained provider, with systems to ensure safety. Training can include a mix of hands-on training with traditional didactics, online coursework, and simulation, but each carries its own costs and benefits worth of review. Following training it is essential to think about assessment of competency in point-of-care ultrasound to reflect the combination of cognitive and procedural skills that makes up the practice. Within the frameworks described, expansion can be expected across specialty and professional boundaries.

Empowering Medical Students: Harnessing Artificial Intelligence for Precision Point-of-Care Echocardiography Assessment of Left Ventricular Ejection Fraction

Introduction

Point-of-care ultrasound (POCUS) use is now universal among nonexperts. Artificial intelligence (AI) is currently employed by nonexperts in various imaging modalities to assist in diagnosis and decision making.

Aim

To evaluate the diagnostic accuracy of POCUS, operated by medical students with the assistance of an AI-based tool for assessing the left ventricular ejection fraction (LVEF) of patients admitted to a cardiology department.

Methods

Eight students underwent a 6-hour didactic and hands-on training session. Participants used a hand-held ultrasound device (HUD) equipped with an AI-based tool for the automatic evaluation of LVEF. The clips were assessed for LVEF by three methods: visually by the students, by students + the AI-based tool, and by the cardiologists. All LVEF measurements were compared to formal echocardiography completed within 24 hours and were evaluated for LVEF using the Simpson method and eyeballing assessment by expert echocardiographers.

Results

The study included 88 patients (aged 58.3 ± 16.3 years). The AI-based tool measurement was unsuccessful in 6 cases. Comparing LVEF reported by students' visual evaluation and students + AI vs. cardiologists revealed a correlation of 0.51 and 0.83, respectively. Comparing these three evaluation methods with the echocardiographers revealed a moderate/substantial agreement for the students + AI and cardiologists but only a fair agreement for the students' visual evaluation.

Conclusion

Medical students' utilization of an AI-based tool with a HUD for LVEF assessment achieved a level of accuracy similar to that of cardiologists. Furthermore, the use of AI by the students achieved moderate to substantial inter-rater reliability with expert echocardiographers' evaluation.

The Role of Hand-Held Cardiac Ultrasound in Patients with COVID-19

The role of point-of-care ultrasound (POCUS) in patient management has been established in recent years as an important tool. It is increasingly used by multiple medical disciplines in numerous clinical settings, for different applications and diagnostic purposes and in the guidance of procedures. The introduction of small-sized and inexpensive hand-held ultrasound devices (HUDs) has addressed some of the POCUS-related challenges and has thus extended POCUS' applicability. HUD utilization is even more relevant in the COVID-19 setting given the operators' infection risk, excessive workload concerns and general equipment contamination. This review focuses on the available technology, usefulness, feasibility and clinical applications of HUD for echocardiogram assessment in patients with COVID-19.