Monitoring the progress of medical treatment is paramount to its success and the patient's quality-of-life. An efficient, cost-effective, and accurate assessment of the treatment's progress that illustrates its tangible impact can motivate patients and guide the procedure. This work discusses such an assessment tool for both surgical and nonsurgical interventions to correct Pectus Excavatum(PE) - a chest wall deformity characterized by depression of the sternum. We previously proposed a novel approach for measuring and evaluating the severity of pectus deformities. Our method uses 3-D optical scans which are analyzed to compute surface deformation via registration-based point comparisons, and a distance map is generated, providing a quantitative representation of chest wall improvements. The work presented in this paper expands on this effort by adding a module to objectively evaluate the aesthetic outcomes. In this study, we developed a statistical 3D chest model based on the Gaussian Process Morphable Model (GPMM) using a limited number of optical surface scans (n=20) of healthy male subjects. The model builds an average chest shape which can be used to evaluate the aesthetic results of PE treatment. We analyzed the impact of different kernel functions in the modeling of the chest variation and report on the models generalization, specificity, and compactness.

Cardiac auscultation (CA) is the auditory detection of heart sounds to diagnose abnormalities, a crucial skill that is both efficient and cost-effective in medical practice. However, due to increased prevalence and usage of expensive cardiac technologies, many new physicians and trainees have difficulty performing essential cardiac examinations on their patients, particularly diagnosing abnormalities through auscultation using a stethoscope. A virtual pathology stethoscope is a simulation-based solution that train students to perform cardiac examinations by listening to abnormal heart sounds in otherwise healthy standardized patients (SPs). This study reports the accuracy of an electrocardiogram (ECG)-based stethoscope tracking method for placing virtual symptoms in correct auscultation regions. A modified stethoscope head with two electrodes was used to pick up ECG signals at the four primary auscultation sites. A one-dimensional convolutional neural network (CNN) is then modeled to classify the location of the stethoscope by taking advantage of subtle differences in the ECG signals. A 91% accuracy was obtained, showing promising performance gain over our previous methods. This finding would significantly extend the simulation capabilities of SPs by allowing trainees to perform realistic CA and hear CA in clinical environment.

Task engagement is delined as loadings on energetic arousal (affect), task motivation, and concentration (cognition) [1]. It is usually challenging and expensive to label cognitive state data, and traditional computational models trained with limited label information for engagement assessment do not perform well because of overlitting. In this paper, we proposed two deep models (i.e., a deep classilier and a deep autoencoder) for engagement assessment with scarce label information. We recruited 15 pilots to conduct a 4-h flight simulation from Seattle to Chicago and recorded their electroencephalograph (EEG) signals during the simulation. Experts carefully examined the EEG signals and labeled 20 min of the EEG data for each pilot. The EEG signals were preprocessed and power spectral features were extracted. The deep models were pretrained by the unlabeled data and were line-tuned by a different proportion of the labeled data (top 1%, 3%, 5%, 10%, 15%, and 20%) to learn new representations for engagement assessment. The models were then tested on the remaining labeled data. We compared performances of the new data representations with the original EEG features for engagement assessment. Experimental results show that the representations learned by the deep models yielded better accuracies for the six scenarios (77.09%, 80.45%, 83.32%, 85.74%, 85.78%, and 86.52%), based on different proportions of the labeled data for training, as compared with the corresponding accuracies (62.73%, 67.19%, 73.38%, 79.18%, 81.47%, and 84.92%) achieved by the original EEG features. Deep models are effective for engagement assessment especially when less label information was used for training.

We present a three-step method to predict Prostate cancer (PCa) regions on biopsy tissue samples based on high confidence, low resolution PCa regions marked by a pathologist. First, we apply a texture analysis technique on a high magnification optical image to predict PCa regions on an adjacent tissue slice. Second, we design a prediction model for the same purpose using matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) tissue imaging data from the adjacent slice. Finally, we fuse those two results to obtain the PCa regions that will assist MALDI imaging biomarker identification. Experiment results show that the texture analysis based prediction is sensitive (sen. 87.45%) but not specific (spe. 75%), and the prediction based on the MALDI spectra data is specific (spe. 100%) but less sensitive (sen. 50.98%). By combining those two results, a much better prediction for PCa regions on the adjacent slice can be achieved (sen. 80.39%, spe. 93.09%).

Currently there are little objective parameters that can quantify the success of one form of prostate surgical removal over another. Parameters such as the percent of coverage and depth of extra-capsular soft tissue removed with the prostate by the various surgical approaches can aid in quantifying prostate surgery success. Previously, we presented visualization methods as well as measurement algorithms to determine extra- capsular tissue coverage. In this paper, we present a modification to the coverage algorithm where we changed the way of mapping the reconstructed model as well as applying different neighborhood check approaches.

Standardized patients (SPs), individuals who realistically portray patients, are widely used in medical education to teach and assess communication skills, eliciting a history, performing a physical exam, and other important clinical skills. One limitation is that each SP can only portray a limited set of physical symptoms. Finding SPs with the abnormalities students need to encounter is typically not feasible. This project augments the SP by permitting the learner to hear abnormal heart and lung sounds in a normal SP.

Currently there are little objective parameters that can quantify the success of one form of prostate surgical removal over another. Accordingly, at Old Dominion University (ODU) we have been developing a process resulting in the use of software algorithms to assess the coverage and depth of extra-capsular soft tissue removed with the prostate by the various surgical approaches. Parameters such as the percent of capsule that is bare of soft tissue and where present the depth and extent of coverage have been assessed. First, visualization methods and tools are developed for images of prostate slices that are provided to ODU by the Pathology Department at Eastern Virginia Medical School (EVMS). The visualization tools interpolate and present 3D models of the prostates. Measurement algorithms are then applied to determine statistics about extra-capsular tissue coverage. This paper addresses the modeling, visualization, and analysis of prostate gland tissue to aid in quantifying prostate surgery success. Particular attention is directed towards the accuracy of these measurements and is addressed in the analysis discussions.

Automated control of a large electrical power distribution network through a single controller can provide advantages in efficiency and reliability as well as reduction in maintenance costs. For control to be most effective, it is necessary that a global view of the entire network be had by the controller, so that it can reason as to the cause of the readings of the various sensing devices located throughout the network, Traditional approaches to power system control have involved a set of local devices (i.e., protective relays) that base their decision on the instantaneous reading of a single sensor. These single-parameter decisions can sometimes be incorrect due to sensor failures. Furthermore, a special type of fault called a soft fault, where a fault impedance limits the current to a value below the relay operating point, are nearly impossible to detect with decisions based on a single local parameter. By reasoning over an entire suit of sensing devices spread throughout the entire network, protection decisions based on a global view can become more reliable as well as comprehensive. Some previous approaches have implemented global control with varying degrees of success through the use of rule-based knowledge-based systems. This paper describes an alternative knowledge-based approach that makes use of so-called models of structure-and-behavior, to which model-based diagnosis is applied. The objective of this approach is to develop a system that can reliably diagnose faults in power distribution networks (especially soft faults), identify sensor failures, and carry out appropriate corrective action automatically. An intelligent power controller (IPC) which has these capabilities is described. This IPC was rigorously tested in an DC electrical power distribution system testbed and found to successfully carry out the required functions. This paper also describes in detail the tests and the conclusions drawn from their results.

Modeling and simulation have always been highly useful techniques to use when designing, analyzing, or diagnosing an engineered system. Development of an appropriate model, in terms of accuracy, granularity, and complexity, has typically been the burden of the designer, analyst, or troubleshooter. It would naturally be advantageous if a model could be developed automatically, with the user supplying only some final minor refinements. The fact that most modern systems are designed in a computer-aided design (CAD) environment makes this a realistic prospect, because much of the data necessary for the model is already in electronic form. This article outlines a system called the automated knowledge generator (AKG), which embodies techniques that automatically create a model of an engineered system directly from its CAD representation, and describes the extensive testing process followed in order to validate its performance.>