Artificial Intelligence Application on Point-of-Care Ultrasound

Assessment of the Educational and Training Modalities in Point-of-Care Ultrasound (POCUS) for Anesthesiologists

Point-of-care ultrasound (POCUS) has been developed as a critical tool for diagnostic patient evaluation and clinical management. Its transcendence into anesthesiology necessitates appropriate and effective educational strategies to assist in the development of anesthesia POCUS learners. Several professional societies, including the American Society of Anesthesiologists (ASA), American Society of Regional Anesthesia (ASRA), and Accreditation Council for Graduate Medical Education (ACGME) for anesthesiology have established minimum training standards for POCUS education for anesthesiologists, residents, and fellows.1,4 The article at hand aims to summarize and provide insight into the various educational modalities utilized in POCUS training, incorporate these strategies in the established "Indication, Acquisition, Interpretation, and Medical decision-making" (I-AIM) framework, and include recommendations on the minimum number of POCUS exams to aid in achieving competency. 3.

Artificial intelligence and point-of-care ultrasound: Benefits, limitations, and implications for the future

The utilization of artificial intelligence (AI) in medical imaging has become a rapidly growing field as a means to address contemporary demands and challenges of healthcare. Among the emerging applications of AI is point-of-care ultrasound (POCUS), in which the combination of these two technologies has garnered recent attention in research and clinical settings. In this Controversies paper, we will discuss the benefits, limitations, and future considerations of AI in POCUS for patients, clinicians, and healthcare systems.

Artificial Intelligence Assessment of Biological Age From Transthoracic Echocardiography: Discrepancies with Chronologic Age Predict Significant Excess Mortality

BACKGROUND

Age and sex can be estimated using artificial intelligence on the basis of various sources. The aims of this study were to test whether convolutional neural networks could be trained to estimate age and predict sex using standard transthoracic echocardiography and to evaluate the prognostic implications.

METHODS

The algorithm was trained on 76,342 patients, validated in 22,825 patients, and tested in 20,960 patients. It was then externally validated using data from a different hospital (n = 556). Finally, a prospective cohort of handheld point-of-care ultrasound devices (n = 319; ClinicalTrials.gov identifier NCT05455541) was used to confirm the findings. A multivariate Cox regression model was used to investigate the association between age estimation and chronologic age with overall survival.

RESULTS

The mean absolute error in age estimation was 4.9 years, with a Pearson correlation coefficient of 0.922. The probabilistic value of sex had an overall accuracy of 96.1% and an area under the curve of 0.993. External validation and prospective study cohorts yielded consistent results. Finally, survival analysis demonstrated that age prediction ≥5 years vs chronologic age was associated with an independent 34% increased risk for death during follow-up (P < .001).

CONCLUSIONS

Applying artificial intelligence to standard transthoracic echocardiography allows the prediction of sex and the estimation of age. Machine-based estimation is an independent predictor of overall survival and, with further evaluation, can be used for risk stratification and estimation of biological age.

Rescue Transesophageal Echocardiography: A Narrative Review of Current Knowledge and Practice

Perioperative transesophageal echocardiography (TEE) has been part of clinical activity for more than 40 years. During this period, TEE has evolved in terms of technology and clinical applications beyond the initial fields of cardiology and cardiac surgery. The benefits of TEE in the diagnosis and management of acute hemodynamic and respiratory collapse have been recognized in noncardiac surgery and by other specialties too. This natural progress led to the development of rescue TEE, a relatively recent clinical application that extends the use of TEE and makes it accessible to a large group of clinicians and patients requiring acute care. In this review, the authors appraise the current clinical applications and evidence base around this topic. The authors provide a thorough review of the various image acquisition protocols, clinical benefits, and compare it with the more frequently used transthoracic echocardiography. Furthermore, the authors have reviewed the current training and credentialing pathways. Overall, rescue TEE is a highly attractive and useful point-of-care examination, but the current evidence base is limited and the technical protocols, training, and credentialing processes are not standardized. There is a need for adequate guidelines and high-quality research to support its application as a bedside rescue tool.