Model-based tracking of the movement of the tibiofemoral joint via a biplane X-ray imaging system has been commonly used to reproduce its accurate, three-dimensional kinematics. To accommodate the approaches to existing clinical asynchronous biplane fluoroscopy systems and achieve comparable accuracy, this study proposed an automated model-based interleaved biplane fluoroscopy image tracking scheme (MIBFT) by incorporating information of adjacent image frames. The MIBFT was evaluated with a cadaveric study conducted on a knee specimen. The MIBFT reproduced skeletal poses and tibiofemoral kinematics that were in good agreement with the standard reference kinematics provided by an optical motion capture system, in which the root-mean-squared (Rms) errors of the skeletal pose parameters ranged from 0.11 to 0.35 mm in translation and 0.18 to 0.49° in rotation. The influences of rotation speed on the pose errors were below 0.23 mm and 0.26°. The MIBFT-determined bias, precision, and Rms error were comparable to those of the reported model-based tracking techniques using custom-made synchronous biplane fluoroscopy. The results suggested that the further use of the clinical imaging system is feasible for the noninvasive and precise examination of dynamic joint functions and kinematics in clinical practice and biomechanical research.

The current study aimed to compare the measurements of the mandible morphology using 3D cone beam computed tomography (CBCT) images with those using 2D CBCT-synthesized cephalograms; to quantify errors in measurements based on 2D synthesized cephalograms; and to clarify the effects such errors have on the description of the mandibular growth. Mandibles of six miniature pigs were scanned monthly using CBCT over 12 months and the data were used to reconstruct the 3D bone models. Five anatomical landmarks were identified on each bone model, and the inter-marker distances and monthly distance changes were calculated and taken as the gold standard. Synthetic 2D cephalograms were also generated for each bone model using a digitally reconstructed radiography (DRR)-generation method. Errors in cephalogram measurements were determined as the differences between the calculated variables in cephalograms and the gold standard. The variations between cephalograms and the gold standard were also compared using paired t-tests. While the inter-marker distance increases varied among the marker pairs, all marker pairs increased their inter-marker distances gradually every month, reaching 50% of the total annual increases during the fourth and fifth months, and then slowing down in the subsequent months. The 2D measurements significantly underestimated most of the inter-marker distances throughout the monitoring period, in most of the monthly inter-marker distance changes during the first four months, and in the total growth (p < 0.05). Significant errors exist in the measurements using 2D synthesized cephalogram, underestimating the mandibular dimensions and their monthly changes in the early stages of growth, as well as the total annual growth. These results should be considered in dental treatment planning at the beginning of the treatment in order to control more precisely the treatment process and outcome.

Model-based 3D/2D image registration using single-plane fluoroscopy is a common setup to determine knee joint kinematics, owing to its markerless aspect. However, the approach was subjected to lower accuracies in the determination of out-of-plane motion components. Introducing additional kinematic constraints with an appropriate anatomical representation may help ameliorate the reduced accuracy of single-plane image registration. Therefore, this study aimed to develop and evaluate a multibody model-based tracking (MbMBT) scheme, embedding a personalized kinematic model of the tibiofemoral joint for the measurement of tibiofemoral kinematics. The kinematic model was consisted of three ligaments and an articular contact mechanism. The knee joint activities in six volunteers during isolated knee flexion, lunging, and sit-to-stand motions were recorded with a biplane X-ray imaging system. The tibiofemoral kinematics determined with the MbMBT and mediolateral view fluoroscopic images were compared against those determined using biplane fluoroscopic images. The MbMBT was demonstrated to yield tibiofemoral kinematics with precision values in the range from 0.1 mm to 1.1 mm for translations and from 0.2° to 1.3° for rotations. The constraints provided by the kinematic model were shown to effectively amend the nonphysiological tibiofemoral motion and not compromise the image registration accuracy with the proposed MbMBT scheme.

For an emergency department (ED), patient readmissions after ED discharges in a short period are usually considered as an indicator to evaluate the quality of medical services, representing whether the patients had received appropriate treatments. Moreover, these unplanned revisits may cause ED crowding, leading frustrating patient experiences. In order to prevent readmission, many studies had tried to build predictive models for the further intervention for high-risk populations. However, the binary prediction of readmission is much harder than other tasks. Usually, predicting readmission is binary classification task, and people intuitively build a single model to solve the problem. Yet, as the purpose of multi-label learning, that may be helpful for model performance by providing additional helpful information between these related labels. Specifically, this study extends the prediction of readmission to a multi-label form through assembling another two related labels, then applies problem transform methods for pursuing a better prediction on the original task. For verifying the idea, four empirical tree-based machine learning methods were trained and evaluated with more than four 40,000 ED admission records, either in a simple binary classification form or a multi-label form. The results demonstrate a notable improvement of F1-scores and precision for predicting readmissions through a problem transformation pipeline. The improvement is attributed to the proposed pipeline that utilizes the mutual information for classifying through gathering additional related labels. In this study, we provided alternative solution for the further studies that aim to improve the performance of binary classification with a potential mutual related dataset.