Background: Echocardiography is crucial to understanding cardiac function in the Intensive Care Unit (ICU), often by measuring the left ventricular ejection fraction (LVEF). Traditionally, measures of LVEF are completed as part of a comprehensive examination by an expert sonographer or cardiologist, but front-line practitioners increasingly perform focused point-of-care estimates of LVEF while managing life-threatening illness. The two main echocardiographic windows used to grossly estimate LVEF are parasternal and apical windows. Artificial intelligence (AI) algorithms have recently been developed to assist non-experts in obtaining and interpreting point-of-care ultrasound (POCUS) echo images. We tested the feasibility, accuracy and reliability of novice users estimating LVEF using POCUS-AI echo. Methods: A total of 30 novice users (most never holding an ultrasound probe before) received 2 h of instruction, then scanned ICU patients (10 patients, 80 scans) using the Exo Iris POCUS probe with AI guidance tool. They were permitted up to 5 min to attempt parasternal long axis (PLAX) and apical 4 chamber (A4C) views. AI-reported LVEF results from these scans were compared to gold-standard LVEF obtained by an expert echo sonographer. To further assess accuracy, this sonographer also scanned another 65 patients using Exo Iris POCUS-AI vs. conventional protocol. Results: Novices obtained images sufficient to estimate LVEF in 96% of patients in <5 min. Novices obtained PLAX views significantly faster than A4C (1.5 min vs. 2.3 min). Inter-rater reliability of LVEF estimation was very high (ICC 0.88–0.94) whether images were obtained by novices or experts. In n = 65 patients, POCUS-AI LVEF was highly specific for a decreased LVEF ≤ 40% (SP = 90% for PLAX) but only moderately sensitive (SN = 56–70%). Conclusions: Estimating cardiac LVEF from AI-enhanced POCUS is highly feasible even for novices in ICU settings, particularly using the PLAX view. POCUS-AI LVEF results were highly consistent whether performed by novice or expert. When AI detected a decreased LVEF, it was highly accurate, although a normal LVEF reported by POCUS-AI was not necessarily reassuring. This POCUS-AI tool could be clinically useful to rapidly confirm a suspected low LVEF in an ICU patient. Further improvements to sensitivity for low LVEF are needed.

Primary prevention is the cornerstone of public health. Prevention is especially important for chronic diseases of significant burden such as mental illnesses because many of them have limited treatment options, an onset in childhood or in adolescence, and are linked to adverse childhood experiences requiring a focus on early childhood and maternal-child health (MCH). Despite this need, there appears to be a paucity of research into prevention of mental illnesses within public health. To confirm this, we performed a systematic literature review to quantify the proportion of articles in public health that focus on prevention of mental illness versus intervention for these illnesses after their onset, and the proportion of published articles within MCH that focus on mental health. Between November 2019 and August 2021, we reviewed 211,794 published articles from 147 Scimago-ranked English public health journals with no limit on year of publication. As hypothesized, a very small portion (2.2%) of mental health articles included primary prevention and a small portion of MCH articles (7.8%) included mental health. These results are consistent with the existence of a research gap in mental illness prevention within the public health field. Given the early onset of mental illness, the importance of early childhood experiences in the later development of mental illness, and the importance of the social-emotional connection between mother and child for building resilience, public health professionals must incorporate evidence from the field of MCH to develop and assess more primary prevention programs for mental illness.

Background: Echocardiography is crucial to understanding cardiac function in the In-tensive Care Unit (ICU), often by measuring the left ventricular ejection fraction (LVEF). Traditionally measures of LVEF are completed as part of comprehensive ex-amination by an expert sonographer or cardiologist, but front-line practitioners in-creasingly perform focused point-of-care estimates of LVEF while managing life-threatening illness. The two main echocardiographic windows used to grossly es-timate LVEF are parasternal and apical windows. Artificial intelligence (AI) algo-rithms have recently been developed to assist non-experts in obtaining and interpret-ing point-of-care ultrasound (POCUS) echo images. We tested the feasibility, accuracy and reliability of novice users estimating LVEF using POCUS-AI echo. Methods: A total of 30 novice users (most never holding an ultrasound probe before) received 2 hours of instruction, then scanned ICU patients (10 patients, 80 scans) using the Exo Iris POCUS probe with AI guidance tool. They were permitted up to 5 minutes at-tempting parasternal long axis (PLAX) and apical 4 chamber (A4C) views. AI-reported LVEF results from these scans were compared to gold-standard LVEF obtained by an expert echo sonographer. To further assess accuracy, this so-nographer also scanned another 65 patients using Exo Iris POCUS-AI vs. conventional protocol. Results: Novices obtained images sufficient to estimate LVEF in 96% of pa-tients in < 5 minutes. Novices obtained PLAX views significantly faster than A4C (1.5 min vs. 2.3 min). Inter-rater reliability of LVEF estimation was very high (ICC 0.88-0.94) whether images were obtained by novices or experts. In n=65 patients, POCUS-AI LVEF was highly specific for a decreased LVEF< 40% (SP=90% for PLAX) but only moderately sensitive (SN=56-70%). Conclusion: Estimating cardiac LVEF from AI-enhanced POCUS is highly feasible even for novices in ICU settings, particu-larly using the PLAX view. POCUS-AI LVEF results were highly consistent whether performed by novice or expert. When AI detected a decreased LVEF, it was highly accurate, although a normal LVEF reported by POCUS-AI was not necessarily reas-suring. This POCUS-AI tool could be clinically useful to rapidly confirm a suspected low LVEF in an ICU patient. Further improvements to sensitivity for low LVEF are needed.