Can Dialysis Patients Identify and Diagnose Pulmonary Congestion Using Self-Lung Ultrasound?

AI-enhanced guidance demonstrated improvement in novices' Apical-4-chamber and Apical-5-chamber views

INTRODUCTION

Artificial Intelligence (AI) modules might simplify the complexities of cardiac ultrasound (US) training by offering real-time, step-by-step guidance on probe manipulation for high-quality diagnostic imaging. This study investigates real-time AI-based guidance tool in facilitating cardiac US training and its impact on novice users' proficiency.

METHODS

This independent, prospective randomized controlled trial enrolled participants who completed a six-hour cardiac US course, followed by a designated cardiac US proficiency exam. Both groups received in-person guided training using the same devices, with the AI-enhanced group receiving additional real-time AI feedback on probe navigation and image quality during both training and testing, while the non-AI group relied solely on the instructor's guidance.

RESULTS

Data were collected from 44 participants: 21 in the AI-enhanced group and 23 in the non-AI group. Improvement was observed in the assessment of the AI-enhanced group compared to the non-AI in acquiring the Apical-4-chamber and the Apical-5- chamber views [mean 88% (± SD 10%) vs. mean 76% (± SD 17%), respectively; p = 0.016]. On the other hand, a slower time to complete the echocardiography exam was observed by the AI-enhanced group [mean 401 s (± SD 51) vs. 348 s (± SD 81) respectively; p = 0.038].

DISCUSSION

The addition of real-time, AI-based feedback demonstrated benefits in the cardiac POCUS teaching process for the more challenging echocardiography four- and five- chamber views. It also has the potential to surpass challenges related to in-person POCUS training. Additional studies are required to explore the long-term effect of this training approach.

CLINICAL TRIAL NUMBER

Not applicable.

Dialysis Patients' Evaluation of Lung Edema at Home Using a Mobile Phone Tele-Ultrasound Application: A Pilot Study

Background: Home rehabilitation improves patient satisfaction and reduces the need for specialist consultations. Hemodialysis is a costly post-ICU service that requires frequent monitoring. Previous studies have demonstrated the feasibility and accuracy of patients self-scanning their lungs with an ultrasound device within the hospital. Methods: In this single-center, prospective pilot study, we compared the quality of high-risk elderly patient-generated lung ultrasound images against physician-generated images as our primary outcome. The secondary outcome assessed image quality and B-line quantification between a home device and a gold standard device, when operated by the same clinician. Results: We enrolled nine participants (66% male, median age 76 years [IQR 66,79]). Analysis included 402 ultrasound clips (163 patient-generated, 239 physician-generated, and 237 in-clinic gold standard clips). Patient-generated images demonstrated high reliability (92% highly reliable or reliable) and were non-inferior to physician-generated images (p < 0.001). There was substantial agreement in B-line classification (Kw = 0.64, 95% CI: 0.46-0.82). The home device, when operated by the same physician, showed non-inferiority to the gold standard device (p < 0.001) with substantial B-line classification agreement (Kw = 0.64, 95% CI: 0.51-0.78). Conclusions: High-risk elderly patients can successfully generate self-scanned lung ultrasound images comparable to those produced by physicians. These promising results warrant further investigation through larger-scale and long-term studies.

Using augmented intelligence to improve long term outcomes

PURPOSE OF REVIEW

For augmented intelligence (AI) tools to realize their potential, critical care clinicians must ensure they are designed to improve long-term outcomes. This overview is intended to align professionals with the state-of-the art of AI.

RECENT FINDINGS

Many AI tools are undergoing preliminary assessment of their ability to support the care of survivors and their caregivers at multiple time points after intensive care unit (ICU) discharge. The domains being studied include early identification of deterioration (physiological, mental), management of impaired physical functioning, pain, sleep and sexual dysfunction, improving nutrition and communication, and screening and treatment of cognitive impairment and mental health disorders.Several technologies are already being marketed and many more are in various stages of development. These technologies mostly still require clinical trials outcome testing. However, lacking a formal regulatory approval process, some are already in use.

SUMMARY

Plans for long-term management of ICU survivors must account for the development of a holistic follow-up system that incorporates AI across multiple platforms. A tiered post-ICU screening program may be established wherein AI tools managed by ICU follow-up clinics provide appropriate assistance without human intervention in cases with less pathology and refer severe cases to expert treatment.

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.

Revolutionizing Bladder Health: Artificial-Intelligence-Powered Automatic Measurement of Bladder Volume Using Two-Dimensional Ultrasound

INTRODUCTION

Measuring elevated post-void residual volume is important for diagnosing urinary outflow tract obstruction and cauda equina syndrome. Catheter placement is exact but painful, invasive, and may cause infection, whereas an ultrasound is accurate, painless, and safe.

AIM

The purpose of this single-center study is to evaluate the accuracy of a module for artificial-intelligence (AI)-based fully automated bladder volume (BV) prospective measurement using two-dimensional ultrasound images, as compared with manual measurement by expert sonographers.

METHODS

Pairs of transverse and longitudinal bladder images were obtained from patients evaluated in an urgent care clinic. The scans were prospectively analyzed by the automated module using the prolate ellipsoid method. The same examinations were manually measured by a blinded expert sonographer. The two methods were compared using the Pearson correlation, kappa coefficients, and the Bland-Altman method.

RESULTS

A total of 111 pairs of transverse and longitudinal views were included. A very strong correlation was found between the manual BV measurements and the AI-based module with r = 0.97 [95% CI: 0.96-0.98]. The specificity and sensitivity for the diagnosis of an elevated post-void residual volume using a threshold ≥200 mL were 1.00 and 0.82, respectively. An almost-perfect agreement between manual and automated methods was obtained (kappa = 0.85). Perfect reproducibility was found for both inter- and intra-observer agreements.

CONCLUSION

This AI-based module provides an accurate automated measurement of the BV based on ultrasound images. This novel method demonstrates a very strong correlation with the gold standard, making it a potentially valuable decision-support tool for non-experts in acute settings.

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.

Exploring the Feasibility of At-Home Lung Ultra-Portable Ultrasound: Parent-Performed Pediatric Lung Imaging

OBJECTIVE

To determine if caregivers would be able to successfully perform in home lung ultrasounds on their children without direct supervision after undergoing a basic tutorial that would allow for expert interpretation.

METHODS

A prospective exploratory single-center cohort study was conducted on patients (0-18 years) presenting to a pediatric emergency department with a respiratory complaint or COVID-related illness. Caregivers underwent a brief hands-on session and were instructed to scan the lungs daily for 7 days. Images were assessed using a modified POCUS IQ score. Descriptive statistics were used to describe the data and bivariate analysis was used to compare groups.

RESULTS

Eighteen patients were enrolled; the average age of the parent scanner was 31.9 years and 78% were female. Of all participants, 77.8% scanned on day one. Parents were able to successfully perform some part of the daily scan session for an average of 3.8 out of 7 days. The average POCUS IQ score overall was 6.7 (out of 12).

CONCLUSION

Our study demonstrates the feasibility and acceptability of caregiver ability to obtain adequate lung ultrasound images, at home under no guidance, using the Butterfly iQ probe. Further studies are needed to investigate the accessibility of ultra-portable ultrasound and the ability to integrate with the at-home hospital model, specifically in the pediatric population.

The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students

PURPOSE

Point-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance.

METHODS

The authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups.

RESULTS

The results showed an advantage in quality indictor users' median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views.

CONCLUSIONS

The AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.