Evaluation of Scapholunate Injury and Repair with Dynamic (4D) CT: A Preliminary Report of Two Cases.

Background In predynamic or dynamic scapholunate (SL) instability, standard diagnostic imaging may not identify SL interosseous ligament (SLIL) injury, leading to delayed detection and intervention. This study describes the use of four-dimensional computed tomography (4DCT) in identifying early SLIL injury and following injured wrists to 1-year postoperatively. Description of Technique 4DCT acquires a series of three-dimensional volume data with high temporal resolution (66 ms). 4DCT-derived arthrokinematic data can be used as biomarkers of ligament integrity. Patients and Methods This study presents the use of 4DCT in a two-participant case series to assess changes in arthrokinematics following unilateral SLIL injury preoperatively and 1-year postoperatively. Patients were treated with volar ligament repair with volar capsulodesis and arthroscopic dorsal capsulodesis. Arthrokinematics were compared between uninjured, preoperative injured, and postoperative injured (repaired) wrists. Results 4DCT detected changes in interosseous distances during flexion-extension and radioulnar deviation. Generally, radioscaphoid joint distances were greatest in the uninjured wrist during flexion-extension and radioulnar deviation, and SL interval distances were smallest in the uninjured wrist during flexion-extension and radioulnar deviation. Conclusion 4DCT provides insight into carpal arthrokinematics during motion. Distances between the radioscaphoid joint and SL interval can be displayed as proximity maps or as simplified descriptive statistics to facilitate comparisons between wrists and time points. These data offer insight into areas of concern for decreased interosseous distance and increased intercarpal diastasis. This method may allow surgeons to assess whether (1) injury can be visualized during motion, (2) surgery repaired the injury, and (3) surgery restored normal carpal motion. Level of Evidence Level IV, Case series.

AI-Enabled Advanced Development for Assessing Low Circulating Blood Volume for Emergency Medical Care: Comparison of Compensatory Reserve Machine-Learning Algorithms.

The application of artificial intelligence (AI) has provided new capabilities to develop advanced medical monitoring sensors for detection of clinical conditions of low circulating blood volume such as hemorrhage. The purpose of this study was to compare for the first time the discriminative ability of two machine learning (ML) algorithms based on real-time feature analysis of arterial waveforms obtained from a non-invasive continuous blood pressure system (Finometer®) signal to predict the onset of decompensated shock: the compensatory reserve index (CRI) and the compensatory reserve metric (CRM). One hundred ninety-one healthy volunteers underwent progressive simulated hemorrhage using lower body negative pressure (LBNP). The least squares means and standard deviations for each measure were assessed by LBNP level and stratified by tolerance status (high vs. low tolerance to central hypovolemia). Generalized Linear Mixed Models were used to perform repeated measures logistic regression analysis by regressing the onset of decompensated shock on CRI and CRM. Sensitivity and specificity were assessed by calculation of receiver-operating characteristic (ROC) area under the curve (AUC) for CRI and CRM. Values for CRI and CRM were not distinguishable across levels of LBNP independent of LBNP tolerance classification, with CRM ROC AUC (0.9268) being statistically similar (p = 0.134) to CRI ROC AUC (0.9164). Both CRI and CRM ML algorithms displayed discriminative ability to predict decompensated shock to include individual subjects with varying levels of tolerance to central hypovolemia. Arterial waveform feature analysis provides a highly sensitive and specific monitoring approach for the detection of ongoing hemorrhage, particularly for those patients at greatest risk for early onset of decompensated shock and requirement for implementation of life-saving interventions.

Real-Time Quality Assessment of Long-Term ECG Signals Recorded by Wearables in Free-Living Conditions.

OBJECTIVE: Nowadays, methods for ECG quality assessment are mostly designed to binary distinguish between good/bad quality of the whole signal. Such classification is not suitable to long-term data collected by wearable devices. In this paper, a novel approach to estimate long-term ECG signal quality is proposed. METHODS: The real-time quality estimation is performed in a local time window by calculation of continuous signal-to-noise ratio (SNR) curve. The layout of the data quality segments is determined by analysis of SNR waveform. It is distinguished between three levels of ECG signal quality: signal suitable for full wave ECG analysis, signal suitable only for QRS detection, and signal unsuitable for further processing. RESULTS: The SNR limits for reliable QRS detection and full ECG waveform analysis are 5 and 18 dB respectively. The method was developed and tested using synthetic data and validated on real data from wearable device. CONCLUSION: The proposed solution is a robust, accurate and computationally efficient algorithm for annotation of ECG signal quality that will facilitate the subsequent tailored analysis of ECG signals recorded in free-living conditions. SIGNIFICANCE: The field of long-term ECG signals self-monitoring by wearable devices is swiftly developing. The analysis of massive amount of collected data is time consuming. It is advantageous to characterize data quality in advance and thereby limit consequent analysis to useable signals.

An interpolation technique to enable accurate three-dimensional joint kinematic analyses using asynchronous biplane fluoroscopy.

Biplane 2D-3D model-based registration and radiostereometric analysis (RSA) approaches have been commonly used for measuring three-dimensional, in vivo joint kinematics. However, in clinical biplane systems, the x-ray images are acquired asynchronously, which introduces registration errors. The present study introduces an interpolation technique to reduce image registration error by generating synchronous fluoroscopy image estimates. A phantom study and cadaveric shoulder study were used to evaluate the level of improvement in image registration that could be obtained as a result of using our interpolation technique. Our phantom study results show that the interpolated bead tracking technique was in better agreement with the true bead positions than when asynchronous images were used alone. The overall RMS error of glenohumeral kinematics for interpolated biplane registration was reduced by 1.27 mm, 0.40 mm, and 0.47 mm in anterior-posterior, superior-inferior, and medial-lateral translation, respectively; and 0.47°, 0.67°, and 0.19° in ab-adduction, internal-external rotation and flexion-extension, respectively, compared to asynchronous registration. The interpolated biplane registration results were consistent with previously reported studies using custom synchronous biplane fluoroscopy technology. This approach will be particularly useful for improving the kinematic accuracy of high velocity activities when using clinical biplane fluoroscopes or two independent c-arms, which are available at a number of institutions.

Anatomic surface reconstruction from sampled point cloud data and prior models.

In this paper, we propose an approach for reconstruction of an anatomic surface model from point cloud data using the Screened Poisson Surface Reconstruction algorithm, which requires a collection of points and their normal vectors. Various algorithms exist for estimating normal vectors for point cloud data; however, in this work we describe a novel approach to estimating the normal vectors from a high-resolution prior model. In many medical applications, a preoperative high-resolution scan is acquired for diagnostic and planning purposes, whereas intraoperative, lower fidelity imaging is utilized during the procedure. This approach assumes an already existing registration between intra-operatively acquired data and the preoperative model. We conducted simulation experiments to evaluate the effect of registration error, point sampling rate, and noise levels on the acquired point cloud data samples. In addition, we evaluated the effect of using both the closest point, as well as a neighborhood of closest points on the prior model for estimating the normal. Our results showed that surface reconstruction error increases with higher registration error; however, acceptable performance was achieved with clinically-acceptable registration error. In addition, the best reconstruction was obtained when estimating the normal using only the closest point on the prior model, as opposed to utilizing a neighborhood of points. When combining the effect of all factors (Gaussian sampling noise of zero mean and σ=1.8mm; Gaussian translational error of zero mean and σ=2.0mm; and Gaussian rotational error of zero mean and σ=3°) the overall RMS reconstruction error was 0.88±0.03mm.