Reproducibility of sonographic measurements of carotid intima-media thickness

Deep Learning-Based Approaches for Enhanced Diagnosis and Comprehensive Understanding of Carpal Tunnel Syndrome

Carpal tunnel syndrome (CTS) is a prevalent medical condition resulting from compression of the median nerve in the hand, often caused by overuse or age-related factors. In this study, a total of 160 patients participated, including 80 individuals with CTS presenting varying levels of severity across different age groups. Numerous studies have explored the use of machine learning (ML) and deep learning (DL) techniques for CTS diagnosis. However, further research is required to fully leverage the potential of artificial intelligence (AI) technology in CTS diagnosis, addressing the challenges and limitations highlighted in the existing literature. In our work, we propose a novel approach for CTS diagnosis, prediction, and monitoring disease progression. The proposed framework consists of three main layers. Firstly, we employ three distinct DL models for CTS diagnosis. Through our experiments, the proposed approach demonstrates superior performance across multiple evaluation metrics, with an accuracy of 0.969%, precision of 0.982%, and recall of 0.963%. The second layer focuses on predicting the cross-sectional area (CSA) at 1, 3, and 6 months using ML models, aiming to forecast disease progression during therapy. The best-performing model achieves an accuracy of 0.9522, an R2 score of 0.667, a mean absolute error (MAE) of 0.0132, and a median squared error (MdSE) of 0.0639. The highest predictive performance is observed after 6 months. The third layer concentrates on assessing significant changes in the patients' health status through statistical tests, including significance tests, the Kruskal-Wallis test, and a two-way ANOVA test. These tests aim to determine the effect of injections on CTS treatment. The results reveal a highly significant reduction in symptoms, as evidenced by scores from the Symptom Severity Scale and Functional Status Scale, as well as a decrease in CSA after 1, 3, and 6 months following the injection. SHAP is then utilized to provide an understandable explanation of the final prediction. Overall, our study presents a comprehensive approach for CTS diagnosis, prediction, and monitoring, showcasing promising results in terms of accuracy, precision, and recall for CTS diagnosis, as well as effective prediction of disease progression and evaluation of treatment effectiveness through statistical analysis.

Imaging Early Life Cardiovascular Phenotype

The growing epidemics of obesity, hypertension, and diabetes, in addition to worsening environmental factors such as air pollution, water scarcity, and climate change, have fueled the continuously increasing prevalence of cardiovascular diseases (CVDs). This has caused a markedly increasing burden of CVDs that includes mortality and morbidity worldwide. Identification of subclinical CVD before overt symptoms can lead to earlier deployment of preventative pharmacological and nonpharmacologic strategies. In this regard, noninvasive imaging techniques play a significant role in identifying early CVD phenotypes. An armamentarium of imaging techniques including vascular ultrasound, echocardiography, magnetic resonance imaging, computed tomography, noninvasive computed tomography angiography, positron emission tomography, and nuclear imaging, with intrinsic strengths and limitations can be utilized to delineate incipient CVD for both clinical and research purposes. In this article, we review the various imaging modalities used for the evaluation, characterization, and quantification of early subclinical cardiovascular diseases.

Predicting CTS Diagnosis and Prognosis Based on Machine Learning Techniques

Carpal tunnel syndrome (CTS) is a clinical disease that occurs due to compression of the median nerve in the carpal tunnel. The determination of the severity of carpal tunnel syndrome is essential to provide appropriate therapeutic interventions. Machine learning (ML)-based modeling can be used to classify diseases, make decisions, and create new therapeutic interventions. It is also used in medical research to implement predictive models. However, despite the growth in medical research based on ML and Deep Learning (DL), CTS research is still relatively scarce. While a few studies have developed models to predict diagnosis of CTS, no ML model has been presented to classify the severity of CTS based on comprehensive clinical data. Therefore, this study developed new classification models for determining CTS severity using ML algorithms. This study included 80 patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy, and 80 CTS patients who underwent ultrasonography (US)-guided median nerve hydrodissection. CTS severity was classified into mild, moderate, and severe grades. In our study, we aggregated the data from CTS patients and patients with other diseases that have an overlap in symptoms with CTS, such as cervical radiculopathysasas, de quervian tendinopathy, and peripheral neuropathy. The dataset was randomly split into training and test data, at 70% and 30%, respectively. The proposed model achieved promising results of 0.955%, 0.963%, and 0.919% in terms of classification accuracy, precision, and recall, respectively. In addition, we developed a machine learning model that predicts the probability of a patient improving after the hydro-dissection injection process based on the aggregated data after three different months (one, three, and six). The proposed model achieved accuracy after six months of 0.912%, after three months of 0.901%, and after one month 0.877%. The overall performance for predicting the prognosis after six months outperforms the prediction after one and three months. We utilized statistics tests (significance test, Spearman's correlation test, and two-way ANOVA test) to determine the effect of injection process in CTS treatment. Our data-driven decision support tools can be used to help determine which patients to operate on in order to avoid the associated risks and expenses of surgery.

Is periodontitis an independent risk factor for subclinical atherosclerosis?

OBJECTIVES

The aim of this study was to assess the interrelationship between periodontitis and atherosclerosis by comparing the ultrasound and clinical markers of atherosclerosis in systemically healthy patients with and without periodontitis and whether periodontitis can be an independent risk factor for atherosclerosis.

MATERIALS AND METHODS

Total 40 subjects, of same socioeconomic status, belonging to age group of 35-65 years, were recruited and divided into two groups - Group I (Chronic Generalised Periodontitis without any systemic disease: CP-SH), Group II (Normal healthy patients without periodontitis and any systemic disease - SH). Clinical measurements and ultrasound examinations were carried out. Qualitative variables were analyzed using Chi square test and qualitative variables using Unpaired Student t test. Statistical significance was accepted for p≤0.05.

RESULTS

Carotid ultrasound revealed right and left intima media thickness (IMT) of 0.626±0.016mm and 0.715±0.037mm respectively in cases versus 0.495±0.009mm and 0.518±0.009mm respectively in controls, with the difference being statistically significant. In cases, mean diastolic blood pressure (DBP) was 83.45±4.07mmHg versus 79.25±3.63mmHg in controls, with the difference being statistically significant.

CONCLUSION

In this study, we found statistically significant differences in carotid IMT and DBP values between cases and controls. These findings suggest independent role of periodontal disease in subclinical atherosclerosis.

Carotid Intima-Media Thickness Manual Measurements: Intraoperator and Interoperator Agreements Under A Strict Protocol in a Large Sample

PURPOSE

The aim of this study was to assess the intraoperator and interoperator agreement for manual measurements of intima-media thickness (IMT) performed under a strict carotid ultrasound technical protocol.

METHODS

Two blinded experienced operators independently performed an ultrasound examination at the distal common carotid of 242 subjects in the same patient's position, diastolic phase, probe type, zooming, and depth. Thirty-six subjects were reevaluated in another time point. Three different-angle manual measurements (IMTindiv) were obtained. Interoperator agreements for each IMTindiv, and their mean (IMTmean) and maximum (IMTmax) values, were assessed with the intraclass correlation coefficient and Bland-Altman analysis. Intraoperator agreement was tested taking advantage of the second ultrasound round in 36 subjects.

RESULTS

IMTmean agreements (intraoperator, 0.665-0.913; interoperator, 0.856-0.897) were higher than IMTmax (intraoperator, 0.435-0.793; interoperator, 0.631-0.718) and any IMTindiv (intraoperator, 0.355-0.676; interoperator, 0.590-0.717). Despite the small systematic error for IMTmean (intraoperator, ≤0.03; interoperator, ≤0.02 mm), at best of times, the sampling error size reached at least 0.28 and 0.25 mm for intraoperator and interoperator agreements, respectively, and was never less than 0.13 mm.

CONCLUSIONS

Although IMTmean agreement is excellent under a strict protocol, limits of agreement might be too wide to consider carotid ultrasound a robust cardiovascular risk biomarker.

Imaging-based characterization of cardiometabolic phenotypes focusing on whole-body MRI--an approach to disease prevention and personalized treatment

Metabolic syndrome and cardiovascular disorders pose a challenge to global healthcare systems. Too often, patients with metabolic syndrome are diagnosed in advanced disease stages, where disease-associated damage is irreversible and treatment options are limited. Thus, prevention plays an increasingly important role in the management of cardiometabolic disorders. The main challenge of prevention is to identify patient groups who are at risk for developing overt disease and who might benefit from early therapeutic intervention. In this context, imaging-based phenotyping can add significant information to clinical evaluations, revealing anatomical and physiological changes that reflect intrinsic and extrinsic risk factors. The purpose of this review article was to provide an overview of the current state of imaging-based phenotyping of metabolic syndrome and cardiovascular disorders and to discuss current and potential developments in this field.

Segmentation of the common carotid artery walls based on a frequency implementation of active contours: segmentation of the common carotid artery walls

Atherosclerosis is one of the most extended cardiovascular diseases nowadays. Although it may be unnoticed during years, it also may suddenly trigger severe illnesses such as stroke, embolisms or ischemia. Therefore, an early detection of atherosclerosis can prevent adult population from suffering more serious pathologies. The intima-media thickness (IMT) of the common carotid artery (CCA) has been used as an early and reliable indicator of atherosclerosis for years. The IMT is manually computed from ultrasound images, a process that can be repeated as many times as necessary (over different ultrasound images of the same patient), but also prone to errors. With the aim to reduce the inter-observer variability and the subjectivity of the measurement, a fully automatic computer-based method based on ultrasound image processing and a frequency-domain implementation of active contours is proposed. The images used in this work were obtained with the same ultrasound scanner (Philips iU22 Ultrasound System) but with different spatial resolutions. The proposed solution does not extract only the IMT but also the CCA diameter, which is not as relevant as the IMT to predict future atherosclerosis evolution but it is a statistically interesting piece of information for the doctors to determine the cardiovascular risk. The results of the proposed method have been validated by doctors, and these results are visually and numerically satisfactory when considering the medical measurements as ground truth, with a maximum deviation of only 3.4 pixels (0.0248 mm) for IMT.

Intima media thickness of the common carotid artery in vascular surgery patients: a predictor of postoperative cardiovascular events

BACKGROUND

Cardiovascular (CV) complications are the leading cause of morbidity and mortality in vascular surgery patients. The Revised Cardiac Risk (RCR) index, identifying cardiac risk factors, is commonly used for preoperative risk stratification. However, a more direct marker of the underlying atherosclerotic disease, such as the common carotid artery intimamedia thickness (CCA-IMT) may be of predictive value as well. The current study evaluated the prognostic value of the CCA-IMT for postoperative CV outcome.

METHODS

In 508 vascular surgery patients, the CCA-IMT was measured using high-resolution B-mode ultrasonography. We recorded the RCR factors: ischemic heart disease, heart failure, cerebrovascular disease, diabetes mellitus, and renal dysfunction. Repeated Troponin T measurements and electrocardiograms were performed postoperatively. The study end point was the composite of 30-day CV events and long-term CV mortality. Multivariable regression analyses were used to assess the additional value of CCA-IMT for the prediction of cardiac events.

RESULTS

In total, 30-day events and long-term cardiovascular mortality were noted in 122 (24%) and 81 (16%) patients, respectively. The optimal predictive value of CCA-IMT, using receiver-operating characteristic curve analysis, for the prediction of CV events was calculated to be 1.25 mm (sensitivity 70%, specificity 80%). An increased CCA-IMT was independently associated with 30-day CV events (OR 2.20, 95% CI 1.38-3.52) and long-term CV mortality (HR 6.88, 95% CI 4.11-11.50), respectively.

CONCLUSIONS

This study shows that an increased CCA-IMT has prognostic value in vascular surgery patients to predict 30-day CV events and long-term CV mortality, incremental to the RCR index.