Effect of computer monitor brightness on visual (subjective) carotid plaque characterization

Effect of different screen brightness and devices on online visual acuity test

PURPOSE

This study aimed to study the difference in test results of online visual acuity (VA) test under different devices and screen brightness conditions and to compare online VA test with Early Treatment Diabetic Retinopathy Study (ETDRS).

METHODS

Healthy volunteers with the best corrected VA of 0.0 LogMAR or higher were recruited. VAs under ETDRS were tested first, and then online VA test (the Stanford Acuity Test, StAT) visual acuities using iPad Air2 and Microsoft Surface pro4 under 50% and 100% screen brightness were performed. The VA results and the testing times were compared between different devices and screen brightness conditions.

RESULTS

A total of 101 eyes were included in this study. The VA results measured by the StAT were better than those of ETDRS. The VA results measured at 100% screen brightness were better than those of 50% brightness (mean difference, 0.013 logMAR at most, less than 1 letter); the VA results measured by iPad Air2 were better than those of Surface pro4 (mean difference, -0.009 logMAR at most, less than 1 letter). Significantly less time was spent on VA testing under StAT than that under ETDRS.

CONCLUSION

The impact of screen brightness and the device on the VA results generated by online VA tests was clinically insignificant. In addition, online VA tests are found to be reliable and more time efficient than ETDRS.

Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application

Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type in these imaging modalities, especially into symptomatic and asymptomatic plaque, helps in the planning of carotid endarterectomy or stenting. It can be challenging to characterize plaque components due to (I) partial volume effect in magnetic resonance imaging (MRI) or (II) varying Hausdorff values in plaque regions in CT, and (III) attenuation of echoes reflected by the plaque during US causing acoustic shadowing. Artificial intelligence (AI) methods have become an indispensable part of healthcare and their applications to the non-invasive imaging technologies such as MRI, CT, and the US. In this narrative review, three main types of AI models (machine learning, deep learning, and transfer learning) are analyzed when applied to MRI, CT, and the US. A link between carotid plaque characteristics and the risk of coronary artery disease is presented. With regard to characterization, we review tools and techniques that use AI models to distinguish carotid plaque types based on signal processing and feature strengths. We conclude that AI-based solutions offer an accurate and robust path for tissue characterization and classification for carotid artery plaque imaging in all three imaging modalities. Due to cost, user-friendliness, and clinical effectiveness, AI in the US has dominated the most.

The Accuracy of Noninvasive Imaging Techniques in Diagnosis of Carotid Plaque Morphology

BACKGROUND

The stroke is leading cause of death and severe disability worldwide. Atherosclerosis is responsible for over 30% of all ischemic strokes. It has been recently discovered that plaque morphology may help predict the clinical behavior of carotid atherosclerosis and determine the risk of stroke. The noninvasive imaging techniques have been developed to evaluate the vascular wall in an attempt to identify "vulnerable plaques".

AIM

The purpose is to investigate the diagnostic accuracy of ultrasound, multidetector computed tomography and magnetic resonance imaging in the identification of plaque components associated with plaque vulnerability.

MATERIAL AND METHODS

One hundred patients were admitted for carotid endarterectomy for high grade carotid stenosis. We defined the diagnostic value of B-mode ultrasound of carotid plaque in a half, and the accuracy of multidetector computed tomography and magnetic resonance imaging, in the other group, for detection of unstable carotid plaque. The reference standard was histology.

RESULTS

Sensitivity of ultrasound, multidetector computed tomography and magnetic resonance imaging is 94%, 83% and 100%, and the specificity is 93%, 73% and 89% for detection of unstable carotid plaque.

CONCLUSION

The ultrasound has high accuracy for diagnostics of carotid plaque morphology, magnetic resonance imaging has high potential for tissue differentiation and multidetector computed tomography determines precisely degree of stenosis and presence of ulceration and calcifications. The three noninvasive imaging modalities are complementary for optimal evaluation of the morphology of carotid plaque. This will help to determine the risk of stroke and to decide on the best treatment - carotid endarterectomy or carotid stenting.