Artificial Intelligence Application on Point-of-Care Ultrasound

Application of wireless ultrasound for guided caudal anesthesia in children undergoing concealed penis surgery

Caudal anesthesia alleviates the strong pain endured by children during surgical treatment for concealed penis. In the traditional method, anesthesiologists identify the puncture point using the 'blind probe' method, which leads to anesthesia induction failure in children. Ultrasound has recently gained wide attention for its guidance in peripheral nerve block analgesia. However, the clinical significance of wireless ultrasound - guided caudal anesthesia technology in children remains unexplored. This study investigated the clinical value of wireless ultrasound - guided caudal anesthesia in children undergoing concealed penis surgery. From April 2022 to August 2022, 120 pediatric patients aged 3-10 years were selected for concealed penis surgery. They were divided into the wireless ultrasound - guided sacral block group (group A) and the traditional sacral block group (group B), with 60 children in each group. Children in group A and group B underwent wireless ultrasound - guided caudal anesthesia and traditional caudal anesthesia, respectively. The success rates of the first puncture and total punctures, time taken for the punctures, and number of punctures were compared between the groups. The success rates of the first puncture (95% vs 68.3%) and total puncture (100% vs 90%) were significantly higher in group A than in group B (P<0.05). The average puncture time and the average number of punctures were, respectively, significantly shorter and lesser in group A than in group B (both P<0.05). Compared with the traditional method, wireless ultrasound visualization technology can effectively improve the success rate of sacral block puncture and reduce puncture time, which is worthy of clinical application.

Developing a Canadian artificial intelligence medical curriculum using a Delphi study

The integration of artificial intelligence (AI) education into medical curricula is critical for preparing future healthcare professionals. This research employed the Delphi method to establish an expert-based AI curriculum for Canadian undergraduate medical students. A panel of 18 experts in health and AI across Canada participated in three rounds of surveys to determine essential AI learning competencies. The study identified key curricular components across ethics, law, theory, application, communication, collaboration, and quality improvement. The findings demonstrate substantial support among medical educators and professionals for the inclusion of comprehensive AI education, with 82 out of 107 curricular competencies being deemed essential to address both clinical and educational priorities. It additionally provides suggestions on methods to integrate these competencies within existing dense medical curricula. The endorsed set of objectives aims to enhance AI literacy and application skills among medical students, equipping them to effectively utilize AI technologies in future healthcare settings.

Point-of-care ultrasound training in nephrology: a position statement by the International Alliance for POCUS in Nephrology

Point-of-care ultrasonography (POCUS) has rapidly evolved from a niche technology to an indispensable tool across medical specialties, including nephrology. This evolution is driven by advancements in technology and the visionary efforts of clinicians in emergency medicine and beyond. Recognizing its potential, medical schools are increasingly integrating POCUS into training curricula, emphasizing its role in enhancing diagnostic accuracy and patient care. Despite these advancements, barriers such as limited faculty expertise and 'lack of' standardized guidelines hinder widespread adoption and regulation. The International Alliance for POCUS in Nephrology (IAPN), through this position statement, aims to guide nephrologists in harnessing the diagnostic power of POCUS responsibly and effectively. By outlining core competencies, recommending training modalities and advocating for robust quality assurance measures, we envision a future where POCUS enhances nephrology practice globally, ensuring optimal patient outcomes through informed, evidence-based decision-making. International collaboration and education are essential to overcome current challenges and realize the full potential of POCUS in nephrology and beyond.

Artificial intelligence-assisted focused cardiac ultrasound training: A survey among undergraduate medical students

Objectives

Focused cardiac ultrasound (FoCUS) is increasingly applied in many specialities, and adequate education and training of physicians is therefore mandatory. This study aimed to assess the impact of artificial intelligence (AI)-assisted interactive focused cardiac ultrasound (FoCUS) teaching session on undergraduate medical students' confidence level and knowledge in cardiac ultrasound.

Methods

The AI-assisted interactive FoCUS teaching session was held during the 9th National Undergraduate Cardiovascular Conference in London in March 2023 and all undergraduate medical students were invited to attend, and 79 students enrolled and attended the training. Two workshops were conducted each over 3-hour period. Each workshop consisted of a theoretical lecture followed by a supervised hands-on session by experts, first workshop trained 39 students and the second workshop trained 40 students. The students' pre- and post-session knowledge and confidence levels were assessed by Likert-type-scale questionnaires filled in by the students before and immediately after the workshop.

Results

A total of 61 pre-session and 52 post-session questionnaires were completed. Confidence level in ultrasound skills increased significantly for all six domains after the workshop, with the greatest improvement seen in obtaining basic cardiac views (p < 0.001 for all six domains). Students strongly agreed about the effectiveness of the teaching session and supported the integration of ultrasound training into their medical curriculum.

Conclusions

AI-assisted interactive FoCUS training can be an effective and powerful tool to increase ultrasound skills and confidence levels of undergraduate medical students. Integration of such ultrasound courses into the medical curriculum should therefore be considered.

Continuing professional development for primary care physicians: a pre-post study on lung point-of-care ultrasound curriculum

BACKGROUND

Point-of-care ultrasound is rapidly gaining traction in clinical practice, including primary care. Yet, logistical challenges and geographical isolation hinder skill acquisition. Concurrently, an evidentiary gap exists concerning such guidance's effectiveness and optimal implementation in these settings.

METHODS

We developed a lung point-of-care ultrasound (POCUS) curriculum for primary care physicians in a rural, medically underserved region of the south of Israel. The course included recorded lectures, pre-course assessments, hands-on training, post-workshop lectures, and individual practice. To evaluate our course, we measured learning outcomes and physicians' proficiency in different lung POCUS domains using hands-on technique assessment and gathered feedback on the course with a multi-modal perception approach: an original written pre- and post-perception and usage questionnaire.

RESULTS

Fifty primary care physicians (PCPs) showed significant improvement in hands-on skills, increasing from 6 to 76% proficiency (p < 0.001), and in identifying normal versus abnormal views, improving from 54 to 74% accuracy (p < 0.001). Ten weeks after training, primary care physicians reported greater comfort using lung ultrasound, rising from 10 to 54% (p < 0.001), and improved grasp of its potential and limits, increasing from 27.5% to 84% (p < 0.001). Weekly usage increased from none to 50%, and the number of primary care physicians not using at all decreased from 72 to 26% (p < 0.001).

CONCLUSIONS

A two-day focused in-person and remote self-learning lung-POCUS training significantly improved primary care physicians' lung ultrasound skills, comfort, and implementation.

FLUEnT: Transformer for detecting lung consolidations in videos using fused lung ultrasound encodings

Pneumonia is the leading cause of death among children around the world. According to WHO, a total of 740,180 lives under the age of five were lost due to pneumonia in 2019. Lung ultrasound (LUS) has been shown to be particularly useful for supporting the diagnosis of pneumonia in children and reducing mortality in resource-limited settings. The wide application of point-of-care ultrasound at the bedside is limited mainly due to a lack of training for data acquisition and interpretation. Artificial Intelligence can serve as a potential tool to automate and improve the LUS data interpretation process, which mainly involves analysis of hyper-echoic horizontal and vertical artifacts, and hypo-echoic small to large consolidations. This paper presents, Fused Lung Ultrasound Encoding-based Transformer (FLUEnT), a novel pediatric LUS video scoring framework for detecting lung consolidations using fused LUS encodings. Frame-level embeddings from a variational autoencoder, features from a spatially attentive ResNet-18, and encoded patient information as metadata combiningly form the fused encodings. These encodings are then passed on to the transformer for binary classification of the presence or absence of consolidations in the video. The video-level analysis using fused encodings resulted in a mean balanced accuracy of 89.3 %, giving an average improvement of 4.7 % points in comparison to when using these encodings individually. In conclusion, outperforming the state-of-the-art models by an average margin of 8 % points, our proposed FLUEnT framework serves as a benchmark for detecting lung consolidations in LUS videos from pediatric pneumonia patients.

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.

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.

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.