Feasibility of machine integrated point of care lung ultrasound automatic B-lines tool in the Corona-virus 2019 critical care unit

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.

Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses

Throughout the past decades ultrasonography did not prove to be a procedure of choice if regarded as part of the routine bedside examination. The reason was the assumption defining the lungs and the bone structures as impenetrable by ultrasound. Only during the recent several years has the approach to the use of such tool in clinical daily routines changed dramatically to offer so-called point-of-care ultrasonography (POCUS). Both vertical and horizontal artefacts became valuable sources of information about the patient's clinical condition, assisting therefore the medical practitioner in differential diagnosis and monitoring of the patient. What is important is that the information is delivered in real time, and the procedure itself is non-invasive. The next stage marking the progress made in this area of diagnostic imaging is the development of arti-ficial intelligence (AI) based on machine learning algorithms. This article is intended to present the available, innovative solutions of the ultrasound systems, including Smart B-line technology, to ensure automatic identification process, as well as interpretation of B-lines in the given lung area of the examined patient. The article sums up the state of the art in ultrasound artefacts and AI applied in POCUS.

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

BACKGROUND

With the recent developments in automated tools, smaller and cheaper machines for lung ultrasound (LUS) are leading us toward the potential to conduct POCUS tele-guidance for the early detection of pulmonary congestion. This study aims to evaluate the feasibility and accuracy of a self-lung ultrasound study conducted by hemodialysis (HD) patients to detect pulmonary congestion, with and without artificial intelligence (AI)-based automatic tools.

METHODS

This prospective pilot study was conducted between November 2020 and September 2021. Nineteen chronic HD patients were enrolled in the Soroka University Medical Center (SUMC) Dialysis Clinic. First, we examined the patient's ability to obtain a self-lung US. Then, we used interrater reliability (IRR) to compare the self-detection results reported by the patients to the observation of POCUS experts and an ultrasound (US) machine with an AI-based automatic B-line counting tool. All the videos were reviewed by a specialist blinded to the performer. We examined their agreement degree using the weighted Cohen's kappa (Kw) index.

RESULTS

A total of 19 patients were included in our analysis. We found moderate to substantial agreement between the POCUS expert review and the automatic counting both when the patient performed the LUS (Kw = 0.49 [95% CI: 0.05-0.93]) and when the researcher performed it (Kw = 0.67 [95% CI: 0.67-0.67]). Patients were able to place the probe in the correct position and present a lung image well even weeks from the teaching session, but did not show good abilities in correctly saving or counting B-lines compared to an expert or an automatic counting tool.

CONCLUSIONS

Our results suggest that LUS self-monitoring for pulmonary congestion can be a reliable option if the patient's count is combined with an AI application for the B-line count. This study provides insight into the possibility of utilizing home US devices to detect pulmonary congestion, enabling patients to have a more active role in their health care.

Comparison of artificial intelligence versus real-time physician assessment of pulmonary edema with lung ultrasound

BACKGROUND

Lung ultrasound can evaluate for pulmonary edema, but data suggest moderate inter-rater reliability among users. Artificial intelligence (AI) has been proposed as a model to increase the accuracy of B line interpretation. Early data suggest a benefit among more novice users, but data are limited among average residency-trained physicians. The objective of this study was to compare the accuracy of AI versus real-time physician assessment for B lines.

METHODS

This was a prospective, observational study of adult Emergency Department patients presenting with suspected pulmonary edema. We excluded patients with active COVID-19 or interstitial lung disease. A physician performed thoracic ultrasound using the 12-zone technique. The physician recorded a video clip in each zone and provided an interpretation of positive (≥3 B lines or a wide, dense B line) or negative (<3 B lines and the absence of a wide, dense B line) for pulmonary edema based upon the real-time assessment. A research assistant then utilized the AI program to analyze the same saved clip to determine if it was positive versus negative for pulmonary edema. The physician sonographer was blinded to this assessment. The video clips were then reviewed independently by two expert physician sonographers (ultrasound leaders with >10,000 prior ultrasound image reviews) who were blinded to the AI and initial determinations. The experts reviewed all discordant values and reached consensus on whether the field (i.e., the area of lung between two adjacent ribs) was positive or negative using the same criteria as defined above, which served as the gold standard.

RESULTS

71 patients were included in the study (56.3% female; mean BMI: 33.4 [95% CI 30.6-36.2]), with 88.3% (752/852) of lung fields being of adequate quality for assessment. Overall, 36.1% of lung fields were positive for pulmonary edema. The physician was 96.7% (95% CI 93.8%-98.5%) sensitive and 79.1% (95% CI 75.1%-82.6%) specific. The AI software was 95.6% (95% CI 92.4%-97.7%) sensitive and 64.1% (95% CI 59.8%-68.5%) specific.

CONCLUSION

Both the physician and AI software were highly sensitive, though the physician was more specific. Future research should identify which factors are associated with increased diagnostic accuracy.

Towards Accurate Point-of-Care Tests for Tuberculosis in Children

In childhood tuberculosis (TB), with an estimated 69% of missed cases in children under 5 years of age, the case detection gap is larger than in other age groups, mainly due to its paucibacillary nature and children’s difficulties in delivering sputum specimens. Accurate and accessible point-of-care tests (POCTs) are needed to detect TB disease in children and, in turn, reduce TB-related morbidity and mortality in this vulnerable population. In recent years, several POCTs for TB have been developed. These include new tools to improve the detection of TB in respiratory and gastric samples, such as molecular detection of Mycobacterium tuberculosis using loop-mediated isothermal amplification (LAMP) and portable polymerase chain reaction (PCR)-based GeneXpert. In addition, the urine-based detection of lipoarabinomannan (LAM), as well as imaging modalities through point-of-care ultrasonography (POCUS), are currently the POCTs in use. Further to this, artificial intelligence-based interpretation of ultrasound imaging and radiography is now integrated into computer-aided detection products. In the future, portable radiography may become more widely available, and robotics-supported ultrasound imaging is currently being trialed. Finally, novel blood-based tests evaluating the immune response using “omic-“techniques are underway. This approach, including transcriptomics, metabolomic, proteomics, lipidomics and genomics, is still distant from being translated into POCT formats, but the digital development may rapidly enhance innovation in this field. Despite these significant advances, TB-POCT development and implementation remains challenged by the lack of standard ways to access non-sputum-based samples, the need to differentiate TB infection from disease and to gain acceptance for novel testing strategies specific to the conditions and settings of use.