Novel robotic TCD ultrasound with bubbles versus standard care to detect right to left shunt: Study methods

An Artificial Intelligence-Driven Approach for Automatic Evaluation of Right-to-Left Shunt Grades in Saline-Contrasted Transthoracic Echocardiography

BACKGROUND

Intracardiac or pulmonary right-to-left shunt (RLS) is a common cardiac anomaly associated with an increased risk of neurological disorders, specifically cryptogenic stroke. Saline-contrasted transthoracic echocardiography (scTTE) is often used for RLS diagnosis. However, the identification of saline microbubbles in the left heart can be challenging for novice residents, potentially leading to a delay in diagnosis and treatment. In this study, we proposed an artificial intelligence (AI)-based algorithm designed to automatically detect microbubbles in scTTE images and evaluate right-to-left shunt grades. This tool aims to support residency training and decrease the workload of cardiologists.

METHODS

A dataset of 23,665 scTTE images obtained from 174 individuals was included in this study. This dataset was partitioned into a training set (n = 20,475) and an internal validation set (n = 3,190) on a patient-level basis. An additional cohort of 33 patients diagnosed with cryptogenic ischemic stroke was enrolled as an external validation set. The proposed algorithm for right-to-left shunt degree classification employed the EfficientNet-b4 model, and the model's performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity, and compared to the performance of residents.

RESULTS

Our AI model demonstrated robust performance with an accuracy of 0.926, sensitivity of 0.827, and specificity of 0.951 on the internal testing dataset. In the external validation set, our AI model exhibited diagnostic accuracy, sensitivity, and specificity of 0.864, 0.818, and 0.909, respectively. In comparison, residents achieved values of 0.727, 0.636, and 0.818, respectively.

CONCLUSION

Our AI model provides a swift, precise, and easily deployable methodology for grading the degree of right-to-left shunt in scTTE, carrying substantial implications for routine clinical practice. Residents can benefit from our artificial intelligence-based algorithm, enhancing both the accuracy and efficiency of RLS diagnosis.

Neuromonitoring in Children with Cerebrovascular Disorders

BACKGROUND

Cerebrovascular disorders are an important cause of morbidity and mortality in children. The acute care of a child with an ischemic or hemorrhagic stroke or cerebral sinus venous thrombosis focuses on stabilizing the patient, determining the cause of the insult, and preventing secondary injury. Here, we review the use of both invasive and noninvasive neuromonitoring modalities in the care of pediatric patients with arterial ischemic stroke, nontraumatic intracranial hemorrhage, and cerebral sinus venous thrombosis.

METHODS

Narrative review of the literature on neuromonitoring in children with cerebrovascular disorders.

RESULTS

Neuroimaging, near-infrared spectroscopy, transcranial Doppler ultrasonography, continuous and quantitative electroencephalography, invasive intracranial pressure monitoring, and multimodal neuromonitoring may augment the acute care of children with cerebrovascular disorders. Neuromonitoring can play an essential role in the early identification of evolving injury in the aftermath of arterial ischemic stroke, intracranial hemorrhage, or sinus venous thrombosis, including recurrent infarction or infarct expansion, new or recurrent hemorrhage, vasospasm and delayed cerebral ischemia, status epilepticus, and intracranial hypertension, among others, and this, is turn, can facilitate real-time adjustments to treatment plans.

CONCLUSIONS

Our understanding of pediatric cerebrovascular disorders has increased dramatically over the past several years, in part due to advances in the neuromonitoring modalities that allow us to better understand these conditions. We are now poised, as a field, to take advantage of advances in neuromonitoring capabilities to determine how best to manage and treat acute cerebrovascular disorders in children.

Asymptomatic carotid artery stenosis: a summary of current state of evidence for revascularization and emerging high-risk features

Carotid artery stenosis is a leading cause of ischemic stroke. While management of symptomatic carotid stenosis is well established, the optimal approach in asymptomatic carotid artery stenosis (aCAS) remains controversial. The rapid evolution of medical therapies within the time frame of existing landmark aCAS surgical revascularization trials has rendered their findings outdated. In this review, we sought to summarize the controversies in the management of aCAS by providing the most up-to-date medical and surgical evidence. Subsequently, we compile the evidence surrounding high-risk clinical and imaging features that might identify higher-risk lesions. With this, we aim to provide a practical framework for a precision medicine approach to the management of aCAS.