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Objective: To determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data. Methods: Patients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO‐band, which includes accelerometer, gyroscope, and surface electromyogram sensors. Metrics of within‐patient limb progression were created by combining the change in signal waveform features over time. The resulting upper (UE) and lower (LE) extremity metrics' discrimination of progressive versus relapsing MS were evaluated with calculation of AUROC. Comparisons with Expanded Disability Status Scale (EDSS) scores were made with Pearson correlation. Results: Participants included 53 relapsing and 15 progressive MS (72% female, baseline mean age 48 years, median disease duration 11 years, median EDSS 2.5, median 10 months follow‐up). The final summary metrics differentiated relapsing from secondary progressive MS with AUROC UE 0.93 and LE 0.96. The metrics were associated with baseline EDSS (UE P = 0.0003, LE P = 0.0007). While most had no change in EDSS during the short follow‐up, several had evidence of progression by the multisensor metrics. Interpretation: Within a short follow‐up interval, this novel multisensor algorithm distinguished progressive from relapsing MS and captured changes in limb function. Inexpensive, noninvasive and easy to use, this novel outcome is readily adaptable to clinical practice and trials as a MS vital sign. This approach also holds promise to monitor limb dysfunction in other neurological diseases. [ABSTRACT FROM AUTHOR]

Objective: To create a novel neurological vital sign and reliably capture MS‐related limb disability in less than 5 min. Methods: Consecutive patients meeting the 2010 MS diagnostic criteria and healthy controls were offered enrollment. Participants completed finger and foot taps wearing the MYO‐band© (accelerometer, gyroscope, and surface electromyogram sensors). Signal processing was performed to extract spatiotemporal features from raw sensor data. Intraclass correlation coefficients (ICC) assessed intertest reproducibility. Spearman correlation and multivariable regression methods compared extracted features to physician‐ and patient‐reported disability outcomes. Partial least squares regression identified the most informative extracted textural features. Results: Baseline data for 117 participants with MS (EDSS 1.0–7.0) and 30 healthy controls were analyzed. ICCs for final selected features ranged from 0.80 to 0.87. Time‐based features distinguished cases from controls (P = 0.002). The most informative combination of extracted features from all three sensors strongly correlated with physician EDSS (finger taps rs = 0.77, P < 0.0001; foot taps rs = 0.82, P < 0.0001) and had equally strong associations with patient‐reported outcomes (WHODAS, finger taps rs = 0.82, P < 0.0001; foot taps rs = 0.82, P < 0.0001). Associations remained with multivariable modeling adjusted for age and sex. Conclusions: Extracted features from the multi‐sensor demonstrate striking correlations with gold standard outcomes. Ideal for future generalizability, the assessments take only a few minutes, can be performed by nonclinical personnel, and wearing the band is nondisruptive to routine practice. This novel paradigm holds promise as a new neurological vital sign. [ABSTRACT FROM AUTHOR]

Purpose: Contrast‐enhanced ultrasound imaging has expanded the diagnostic potential of ultrasound by enabling real‐time imaging and quantification of tissue perfusion. Several perfusion models and curve fitting methods have been developed to quantify the temporal behavior of tracer signal and standardize perfusion quantification. While the least‐squares approach has traditionally been applied for curve fitting, it can be inadequate for noisy and complex data. Moreover, previous research suggests that certain perfusion models may be more relevant depending on the organ or tissue imaged. We propose a multi‐model framework to select the most appropriate perfusion model and curve fitting method for each diagnostic application. Methods: Our multi‐model approach uses a system identification method, which estimates perfusion parameters from the model with the best fit to a given time–intensity curve. We compared current perfusion quantification methods that use a single perfusion model and curve fitting method and our proposed multi‐model framework on bolus 3D dynamic contrast‐enhanced ultrasound (DCE‐US) in vivo images obtained in mice implanted with a colon cancer, as well as on simulation data. The quality of fit in estimating perfusion parameters was evaluated using the Spearman correlation coefficient, the coefficient of determination (R2), and the normalized root‐mean‐square error (NRMSE) to ensure that the multi‐model framework finds the best perfusion model and curve fitting algorithm. Results: Our multi‐model framework outperforms conventional single perfusion model approaches with least‐squares optimization, providing more robust perfusion parameter estimation. R2 and NRMSE are 0.98 and 0.18, respectively, for our proposed method. By comparison, the performance of the traditional approach is much more dependent upon the selection of the appropriate model. The R2 and NRMSE are 0.91 and 0.31, respectively. Conclusions: The proposed multi‐model framework for perfusion modeling outperforms the current approach of single perfusion modeling using least‐squares optimization and more robustly estimates perfusion parameters when using empiric data labeled by an expert as the gold standard. Our technique is minimally sensitive to issues affecting the accuracy of perfusion parameter estimation, including rise time, noise, region of interest size, and frame rate. This framework could be of key utility in modeling different perfusion systems in different tissues and organs. [ABSTRACT FROM AUTHOR]

Purpose: Visualization of anatomical structures using radiological imaging methods is an important tool in medicine to differentiate normal from pathological tissue and can generate large amounts of data for a radiologist to read. Integrating these large data sets is difficult and time-consuming. A new approach uses both supervised and unsupervised advanced machine learning techniques to visualize and segment radiological data. This study describes the application of a novel hybrid scheme, based on combining wavelet transform and nonlinear dimensionality reduction (NLDR) methods, to breast magnetic resonance imaging (MRI) data using three well-established NLDR techniques, namely, ISOMAP, local linear embedding (LLE), and diffusion maps (DfM), to perform a comparative performance analysis. Methods: Twenty-five breast lesion subjects were scanned using a 3T scanner. MRI sequences used were T1-weighted, T2-weighted, diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE) imaging. The hybrid scheme consisted of two steps: preprocessing and postprocessing of the data. The preprocessing step was applied for B1 inhomogeneity correction, image registration, and wavelet-based image compression to match and denoise the data. In the postprocessing step, MRI parameters were considered data dimensions and the NLDR-based hybrid approach was applied to integrate the MRI parameters into a single image, termed the embedded image. This was achieved by mapping all pixel intensities from the higher dimension to a lower dimensional (embedded) space. For validation, the authors compared the hybrid NLDR with linear methods of principal component analysis (PCA) and multidimensional scaling (MDS) using synthetic data. For the clinical application, the authors used breast MRI data, comparison was performed using the postcontrast DCE MRI image and evaluating the congruence of the segmented lesions. Results: The NLDR-based hybrid approach was able to define and segment both synthetic and clinical data. In the synthetic data, the authors demonstrated the performance of the NLDR method compared with conventional linear DR methods. The NLDR approach enabled successful segmentation of the structures, whereas, in most cases, PCA and MDS failed. The NLDR approach was able to segment different breast tissue types with a high accuracy and the embedded image of the breast MRI data demonstrated fuzzy boundaries between the different types of breast tissue, i.e., fatty, glandular, and tissue with lesions (>86%). Conclusions: The proposed hybrid NLDR methods were able to segment clinical breast data with a high accuracy and construct an embedded image that visualized the contribution of different radiological parameters. [ABSTRACT FROM AUTHOR]