Comparing the Drop Vertical Jump Tracking Performance of the Azure Kinect to the Kinect V2.

Traditional motion analysis systems are impractical for widespread screening of non-contact anterior cruciate ligament (ACL) injury risk. The Kinect V2 has been identified as a portable and reliable alternative but was replaced by the Azure Kinect. We hypothesize that the Azure Kinect will assess drop vertical jump (DVJ) parameters associated with ACL injury risk with similar accuracy to its predecessor, the Kinect V2. Sixty-nine participants performed DVJs while being recorded by both the Azure Kinect and the Kinect V2 simultaneously. Our software analyzed the data to identify initial coronal, peak coronal, and peak sagittal knee angles. Agreement between the two systems was evaluated using the intraclass correlation coefficient (ICC). There was poor agreement between the Azure Kinect and the Kinect V2 for initial and peak coronal angles (ICC values ranging from 0.135 to 0.446), and moderate agreement for peak sagittal angles (ICC = 0.608, 0.655 for left and right knees, respectively). At this point in time, the Azure Kinect system is not a reliable successor to the Kinect V2 system for assessment of initial coronal, peak coronal, and peak sagittal angles during a DVJ, despite demonstrating superior tracking of continuous knee angles. Alternative motion analysis systems should be explored.

Comparing novel smartphone pose estimation frameworks with the Kinect V2 for knee tracking during athletic stress tests.

PURPOSE: To compare the accuracy of the Microsoft Kinect V2 with novel pose estimation frameworks, in assessing knee kinematics during athletic stress tests, for fast and portable risk assessment of anterior cruciate ligament (ACL) injury. METHODS: We captured 254 varsity athletes, using the Kinect V2 and a smartphone application utilizing Google's MediaPipe framework. The devices were placed as close as possible and used to capture a person, facing the cameras, performing one of three athletic stress tests at a distance of 2.5 ms. Custom software translated the results from both frameworks to the same format. We then extracted relevant knee angles at key moments of the jump and compared them, using the Kinect V2 as the ground truth. RESULTS: The results show relatively small angle differences between the two solutions in the coronal plane and moderate angle differences on the sagittal plane. Overall, the MediaPipe framework results seem to underestimate both knee valgus angles and knee sagittal angles compared to the Kinect V2. CONCLUSION: This preliminary study demonstrates the potential for Google's MediaPipe framework to be used for calculating lower limb kinematics during athletic stress test motions, which can run on most modern smartphones, as it produces similar results to the Kinect V2. A smartphone application similar to the one developed could potentially be used for low cost and widespread ACL injury prevention.

Comparing a Portable Motion Analysis System against the Gold Standard for Potential Anterior Cruciate Ligament Injury Prevention and Screening.

Knee kinematics during a drop vertical jump, measured by the Kinect V2 (Microsoft, Redmond, WA, USA), have been shown to be associated with an increased risk of non-contact anterior cruciate ligament injury. The accuracy and reliability of the Microsoft Kinect V2 has yet to be assessed specifically for tracking the coronal and sagittal knee angles of the drop vertical jump. Eleven participants performed three drop vertical jumps that were recorded using both the Kinect V2 and a gold standard motion analysis system (Vicon, Los Angeles, CA, USA). The initial coronal, peak coronal, and peak sagittal angles of the left and right knees were measured by both systems simultaneously. Analysis of the data obtained by the Kinect V2 was performed by our software. The differences in the mean knee angles measured by the Kinect V2 and the Vicon system were non-significant for all parameters except for the peak sagittal angle of the right leg with a difference of 7.74 degrees and a p-value of 0.008. There was excellent agreement between the Kinect V2 and the Vicon system, with intraclass correlation coefficients consistently over 0.75 for all knee angles measured. Visual analysis revealed a moderate frame-to-frame variability for coronal angles measured by the Kinect V2. The Kinect V2 can be used to capture knee coronal and sagittal angles with sufficient accuracy during a drop vertical jump, suggesting that a Kinect-based portable motion analysis system is suitable to screen individuals for the risk of non-contact anterior cruciate ligament injury.

Using an Affordable Motion Capture System to Evaluate the Prognostic Value of Drop Vertical Jump Parameters for Noncontact ACL Injury.

BACKGROUND: Knee kinematic parameters during a drop vertical jump (DVJ) have been demonstrated to be associated with increased risk of noncontact anterior cruciate ligament (ACL) injury. However, standard motion analysis systems are not practical for routine screening. Affordable and practical motion sensor alternatives exist but require further validation in the context of ACL injury risk assessment. PURPOSE/HYPOTHESIS: To prospectively study DVJ parameters as predictors of noncontact ACL injury in collegiate athletes using an affordable motion capture system (Kinect; Microsoft). We hypothesized that athletes who sustained noncontact ACL injury would have larger initial and peak contact coronal abduction angles and smaller peak flexion angles at the knee during a DVJ. STUDY DESIGN: Case-control study; Level of evidence, 3. METHODS: 102 participants were prospectively recruited from a collegiate varsity sports program. A total of 101 of the 102 athletes (99%) were followed for an entire season for noncontact ACL injury. Each athlete performed 3 DVJs, and the data were recorded using the motion capture system. Initial coronal, peak coronal, and peak sagittal angles of the knee were identified by our software. RESULTS: Five of the 101 athletes sustained a noncontact ACL injury. Peak coronal angles were significantly greater and peak sagittal flexion angles were significantly smaller in ACL-injured athletes (P = .049, P = .049, respectively). Receiver operating characteristic (ROC) analysis demonstrated an area under the curve of 0.88, 0.92, and 0.90 for initial coronal, peak coronal, and peak sagittal angle, respectively. An initial coronal angle cutoff of 2.96° demonstrated 80% sensitivity and 72% specificity, a peak coronal angle cutoff of 6.16° demonstrated 80% sensitivity and 72% specificity, and a peak sagittal flexion cutoff of 93.82° demonstrated 80% sensitivity and 74% specificity on the study cohort. CONCLUSION: Increased peak coronal angle and decreased peak sagittal angle during a DVJ were significantly associated with increased risk for noncontact ACL injury. Based on ROC analysis, initial coronal angle showed good prognostic ability, whereas peak coronal angle and peak sagittal flexion provided excellent prognostic ability. Affordable motion capture systems show promise as cost-effective and practical options for large-scale ACL injury risk screening.

HoloPhaseNet: fully automated deep-learning-based hologram reconstruction using a conditional generative adversarial model.

Quantitative phase imaging with off-axis digital holography in a microscopic configuration provides insight into the cells' intracellular content and morphology. This imaging is conventionally achieved by numerical reconstruction of the recorded hologram, which requires the precise setting of the reconstruction parameters, including reconstruction distance, a proper phase unwrapping algorithm, and component of wave vectors. This paper shows that deep learning can perform the complex light propagation task independent of the reconstruction parameters. We also show that the super-imposed twin-image elimination technique is not required to retrieve the quantitative phase image. The hologram at the single-cell level is fed into a trained image generator (part of a conditional generative adversarial network model), which produces the phase image. Also, the model's generalization is demonstrated by training it with holograms of size 512×512 pixels, and the resulting quantitative analysis is shown.

Improving Accuracy and Runtime of Skeletal Tracking of Lower Limbs for Athletic Jump Mechanics Assessment.

Previous studies have shown that athletic jump mechanics assessments are valuable tools for identifying indicators of an individual's anterior cruciate ligament injury risk. These assessments, such as the drop jump test, often relied on camera systems or sensors that are not always accessible nor practical for screening individuals in a sports setting. As human pose estimation deep learning models improve, we envision transitioning biometrical assessments to mobile devices. As such, here we have addressed two of the most preclusive hindrances of the current state-of-the-art models: accuracy of the lower limb joint prediction and the slow run-time of in-the-wild inference. We tackle the issue of accuracy by adding a post-processing step that is compatible with all inference methods that outputs 3D key points. Additionally, to overcome the lengthy inference rate, we propose a depth estimation method that runs in real-time and can function with any 2D human pose estimation model that outputs COCO key points. Our solution, paired with a state-of-the-art model for 3D human pose estimation, significantly increased lower-limb positional accuracy. Furthermore, when paired with our real-time joint depth estimation algorithm, it is a plausible solution for developing the first mobile device prototype for athlete jump mechanics assessments.

Three-Dimensional (3D) Animation and Calculation for the Assessment of Engaging Hill-Sachs Lesions With Computed Tomography 3D Reconstruction.

PURPOSE: To dynamically assess for Hill-Sachs engagement with animated 3-dimensional (3D) shoulder models. METHODS: We created 3D shoulder models from reconstructed computed tomography (CT) images from a consecutive series of patients with recurrent anterior dislocation. They were divided into 2 groups based on the perceived Hill-Sachs severity. For our cohort of 14 patients with recurrent anterior dislocation, 4 patients had undergone osteoarticular allografting of Hill-Sachs lesions and 10 control patients had undergone CT scanning to quantify bone loss but no treatment for bony pathology. A biomechanical analysis was performed to rotate each 3D model using local coordinate systems to the classical vulnerable position of the shoulder (abduction = 90°, external rotation = 0-135°) and through a functional range. A Hill-Sachs lesion was considered "dynamically" engaging if the angle between the lesion's long axis and anterior glenoid was parallel. Results: In the vulnerable position of the shoulder, none of the Hill-Sachs lesions aligned with the anterior glenoid in any of our patients. However, in our simulated physiological shoulder range, all allograft patients and 70% of controls had positions producing alignment. CONCLUSIONS: The technique offers a visual representation of an engaging Hill-Sachs using 3D-animated reconstructions with open-source software and CT images. In our series of patients, we found multiple shoulder positions that align the Hill-Sachs and glenoid axes that do not necessarily meet the traditional definition of engagement. Identifying all shoulder positions at risk of "engaging," in a broader physiological range, may have critical implications toward selecting the appropriate surgical management of bony defects. LEVEL OF EVIDENCE: level III, case-control study.

3D Human Knee Flexion Angle Estimation Using Deep Convolutional Neural Networks.

An Anterior Cruciate Ligament (ACL) injury can cause a serious burden, especially for athletes participating in relatively risky sports. This raises a growing incentive for designing injury-prevention programs. For this purpose, the analysis of the drop jump landing test, for example, can provide a useful asset for recognizing those who are more likely to sustain knee injuries. Knee flexion angle plays a key role within these test scenarios. Multiple research efforts have been conducted on engaging existing technologies such as the Microsoft Kinect sensor and Motion Capture (MoCap) to investigate the connection between the lower limb angle ranges during jump tests and the injury risk associated with them. Even though these technologies provide sufficient capabilities to researchers and clinicians, they need certain levels of knowledge to enable them to utilize these facilities. Moreover, these systems demand special requirements and setup procedures which make them limiting. Due to recent advances in the area of Deep Learning, numerous powerful 3D pose estimation algorithms have been developed over the last few years. Having access to relatively reliable and accurate 3D body keypoint information can lead to successful detection and prevention of injury. The idea of combining temporal convolutions in video sequences with deep Convolutional Neural Networks (CNNs) offer a substantial opportunity to tackle the challenging task of accurate 3D human pose estimation. Using the Microsoft Kinect sensor as our ground truth, we analyze the performance of CNN-based 3D human pose estimation in everyday settings. The qualitative and quantitative results are convincing enough to give an incentive to pursue further improvements, especially in the task of lower extremity kinematics estimation. In addition to the performance comparison between Kinect and CNN, we have also verified the high-margin of consistency between two Kinect sensors.

Influence of feature set reduction on breast cancer malignancy classification of fine needle aspiration biopsies.

Grading of breast cancer malignancy is a key step in its diagnosis, which in turn helps to determine its prognosis and a course of treatment. In this paper, we consider the application of pattern recognition and image processing techniques to perform computer-assisted automatic breast cancer malignancy grading from cytological slides of fine needle aspiration biopsies. To determine a classification of the malignancy of the slide, a feature set is first determined from imagery of the slides. In this paper we investigated the nature of a wide set of features extracted from biopsy images to determine their discriminatory power and cross-correlation. Feature vector reduction is studied using a correlation map of the features, determining discriminatory power using the Kolmogorov-Smirnov test, significant feature selection, and stepwise feature selection. The reduction of the feature vector simplifies the complexity of classification scheme and does not impair the classification accuracy. In some cases a decrease of the error rate is noted. Based on this analysis, we present an improved classification system for cancer malignancy grading.

Computer-Aided Breast Cancer Diagnosis Based on the Analysis of Cytological Images of Fine Needle Biopsies.

The effectiveness of the treatment of breast cancer depends on its timely detection. An early step in the diagnosis is the cytological examination of breast material obtained directly from the tumor. This work reports on advances in computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies to characterize these biopsies as either benign or malignant. Instead of relying on the accurate segmentation of cell nuclei, the nuclei are estimated by circles using the circular Hough transform. The resulting circles are then filtered to keep only high-quality estimations for further analysis by a support vector machine which classifies detected circles as correct or incorrect on the basis of texture features and the percentage of nuclei pixels according to a nuclei mask obtained using Otsu's thresholding method. A set of 25 features of the nuclei is used in the classification of the biopsies by four different classifiers. The complete diagnostic procedure was tested on 737 microscopic images of fine needle biopsies obtained from patients and achieved 98.51% effectiveness. The results presented in this paper demonstrate that a computerized medical diagnosis system based on our method would be effective, providing valuable, accurate diagnostic information.

An automatic variational level set segmentation framework for computer aided dental X-rays analysis in clinical environments.

An automatic variational level set segmentation framework for Computer Aided Dental X-rays Analysis (CADXA) in clinical environments is proposed. Designed for clinical environments, the segmentation contains two stages: a training stage and a segmentation stage. During the training stage, first, manually chosen representative images are segmented using hierarchical level set region detection. Then the window based feature extraction followed by principal component analysis (PCA) is applied and results are used to train a support vector machine (SVM) classifier. During the segmentation stage, dental X-rays are classified first by the trained SVM. The classifier provides initial contours which are close to correct boundaries for three coupled level sets driven by a proposed pathologically variational modeling which greatly accelerates the level set segmentation. Based on the segmentation results and uncertainty maps that are built based on a proposed uncertainty measurement, a computer aided analysis scheme is applied. The experimental results show that the proposed method is able to provide an automatic pathological segmentation which naturally segments those problem areas. Based on the segmentation results, the analysis scheme is able to provide indications of possible problem areas of bone loss and decay to the dentists. As well, the experimental results show that the proposed segmentation framework is able to speed up the level set segmentation in clinical environments.

Fast and robust clinical triple-region image segmentation using one level set function.

This paper proposes a novel method for clinical triple-region image segmentation using a single level set function. Triple-region image segmentation finds wide application in the computer aided X-ray, CT, MRI and ultrasound image analysis and diagnosis. Usually multiple level set functions are used consecutively or simultaneously to segment triple-region medical images. These approaches are either time consuming or suffer from the convergence problems. With the new proposed triple-regions level set energy modelling, the triple-region segmentation is handled within the two region level set framework where only one single level set function needed. Since only a single level set function is used, the segmentation is much faster and more robust than using multiple level set functions. Adapted to the clinical setting, individual principal component analysis and a support vector machine classifier based clinical acceleration scheme are used to accelerate the segmentation. The clinical acceleration scheme takes the strengths of both machine learning and the level set method while limiting their weaknesses to achieve automatic and fast clinical segmentation. Both synthesized and practical images are used to test the proposed method. These results show that the proposed method is able to successfully segment the triple-region using a single level set function. Also this segmentation is very robust to the placement of initial contour. While still quickly converging to the final image, with the clinical acceleration scheme, our proposed method can be used during pre-processing for automatic computer aided diagnosis and surgery.

Toward automatic computer aided dental X-ray analysis using level set method.

A Computer Aided Dental X-rays Analysis (CADXA) framework is proposed to semi-automatically detect areas of bone loss and root decay in digital dental X-rays. In this framework, first, a new proposed competitive coupled level set method is proposed to segment the image into three pathologically meaningful regions using two coupled level set functions. Tailored for the dental clinical environment, the segmentation stage uses a trained support vector machine (SVM) classifier to provide initial contours. Then, based on the segmentation results, an analysis scheme is applied. First, the scheme builds an uncertainty map from which those areas with bone loss will be automatically detected. Secondly, the scheme employs a method based on the SVM and the average intensity profile to isolate the teeth and detect root decay. Experimental results show that our proposed framework is able to automatically detect the areas of bone loss and, when given the orientation of the teeth, it is able to automatically detect the root decay with a seriousness level marked for diagnosis.