Comparing machine learning algorithms for non-invasive detection and classification of failure in piezoresistive bone cement via electrical impedance tomography.

At an estimated cost of $8 billion annually in the United States, revision surgeries to total joint replacements represent a substantial financial burden to the health care system and a tremendous mental and physical burden on patients and their caretakers. Fixation failures, such as implant loosening, wear, and mechanical instability of the poly(methyl methacrylate) (PMMA) cement, which bonds the implant to the bone, are the main causes of long-term implant failure. Early and accurate diagnosis of cement failure is critical for developing novel therapeutic strategies and reducing the high risk of a misjudged revision. Unfortunately, prevailing imaging modalities, notably plain radiographs, struggle to detect the precursors of implant failure and are often interpreted incorrectly. Our prior work has shown that the modification of PMMA bone cement with low concentrations of conductive fillers makes it piezoresistive and therefore self-sensing. When combined with a conductivity imaging modality such as electrical impedance tomography (EIT), it is possible to monitor load transfer across the PMMA using cost-effective, physiologically benign, non-contact, and real-time electrical measurements. Despite the ability of EIT for monitoring load transfer across self-sensing PMMA bone cement, it is unable to accurately characterize failure mechanisms. Overcoming this challenge is critical to the success of this technology in practice. Therefore, we herein expand upon our previous results by integrating machine learning techniques with EIT for cement condition characterization with the goal of establishing the feasibility of even off-the-shelf machine learning algorithms to address this important problem. We survey a wide variety of different machine learning algorithms for application to this problem, including neural networks on voltage readings of an EIT phantom for tracking the spatial position of a sample, specifying defect orientation within a sample, and classifying defect types, including cracks and delaminations. In addition, we explore the utilization of principal component analysis (PCA) for pre-treating impedance signals in each of these problems. Within the tested algorithms, our results show clear advantages of neural networks, support vector machines, and K-nearest neighbor algorithms for interpreting EIT signals. We also show that PCA is an effective addition to machine learning. These preliminary results demonstrate that the combination of smart materials, EIT, and machine learning may be a powerful instrumentation tool for diagnosing the origin and evolution of mechanical failure in joint replacements.

Structural engineering from an inverse problems perspective.

The field of structural engineering is vast, spanning areas from the design of new infrastructure to the assessment of existing infrastructure. From the onset, traditional entry-level university courses teach students to analyse structural responses given data including external forces, geometry, member sizes, restraint, etc.-characterizing a forward problem (structural causalities → structural response). Shortly thereafter, junior engineers are introduced to structural design where they aim to, for example, select an appropriate structural form for members based on design criteria, which is the inverse of what they previously learned. Similar inverse realizations also hold true in structural health monitoring and a number of structural engineering sub-fields (response → structural causalities). In this light, we aim to demonstrate that many structural engineering sub-fields may be fundamentally or partially viewed as inverse problems and thus benefit via the rich and established methodologies from the inverse problems community. To this end, we conclude that the future of inverse problems in structural engineering is inexorably linked to engineering education and machine learning developments.

The influence of nanofiller alignment on transverse percolation and conductivity.

Nanocomposites have unprecedented potential for conductivity-based damage identification when used as matrices in structural composites. Recent research has investigated nanofiller alignment in structural composites, but because damage identification often requires in-plane measurements, percolation and conductivity transverse to the alignment direction become crucial considerations. We herein contribute indispensable guidance to the development of nanocomposites with aligned nanofiller networks and insights into percolation trends transverse to the alignment direction by studying the influence of alignment on transverse critical volume fraction, conductivity, and rate of transition from non-percolating to percolating in three-dimensional carbon nanotube composite systems.

Ruptured isolated true atherosclerotic aneurysm of the deep femoral artery.

Aneurysms isolated to the deep femoral artery are unusual. Thirty-five cases have been reported in the world literature, however many of these were associated with aneurysms occurring elsewhere. Spontaneous rupture of true isolated deep femoral artery aneurysms is rare, with only a few reported cases. This report presents a case of a ruptured isolated true aneurysm of the deep femoral artery that was diagnosed by duplex ultrasound and treated with resection and end-to-end grafting.

Coarctation and hypoplasia of the aortic arch: will the arch grow?

Hypoplasia of the transverse aortic arch of various degrees of severity is commonly seen in infants who have coarctation of the aorta. It is more often present when the coarctation is associated with intracardiac lesions that diminish or limit forward flow in the ascending aorta and promote right to left flow through an arterial duct. The increased frequency of surgical treatment of infants with complex coarctation, which is in part related to the ability to stabilize their condition with prostaglandin E1, has posed the question of the potential for growth and development of the originally hypoplastic aortic arch after conventional repair of aortic coarctation. Review of our experience with transverse aortic arch hypoplasia, found in 33 (32%) of 102 infants undergoing coarctation repair by subclavian flap aortoplasty or classic resection and end-to-end anastomosis, revealed excellent growth of the transverse arch after repair in all patients available for linear follow-up. The currently proposed extended arch repair should be reserved for the small group of infants with transverse aortic arch to ascending aorta diameter ratios (arch indices) of less than 0.25.

The functional heterogeneity of CD8+ cells defined by anti-CD45RA (2H4) and anti-CD29 (4B4) antibodies.

The monoclonal antibodies, anti-2H4(CD45RA), and anti-4B4(CD29), along with UCHL1-(CD45RO), identify reciprocal populations of CD4 cells with distinct suppressor inducer (CD45RA+CD29-CD45RO-) and helper inducer (CD45RA-CD29+CD45RO+) functions. Although the CD8+ population is known to contain precytotoxic, cytotoxic, suppressor, and some natural killer cells, the exact phenotypic identities of these functional CD8 subsets has not been established. In this study, we tried to determine whether these monoclonal antibodies could distinguish functionally distinct subsets of cells within the CD8+ population. For this purpose, whole T cells or fractionated T cells were sensitized with irradiated allogeneic non-T cells for 6 days, following which, CD8+ or CD8+CD11b- cells were isolated and cellular functions such as suppressor, killer precursor, and killer effector activity were assessed. The results showed that both class I-restricted alloantigen-specific killer effector and killer precursor cells belonged to the CD8+CD11b-CD45RA-CD29+ population. Moreover, these killer effector cells expressed the CTL-associated S6F1 molecule, an epitope of the LFA-1 antigen. In contrast, suppressor effector cells belonged to the CD8+CD11b-CD45RA+CD29- cell population. Although the UCHL1 antigen has been reported to define the CD4+CD29+ helper inducer cell, over 90% of allo-activated CD8+ cells expressed this antigen, whereas only 40-60% of these cells expressed either CD45RA or CD29 antigens. These results suggest that anti-CD45RA and anti-CD29 antibodies may provide useful tools for distinguishing between suppressor effector versus killer effector and killer precursor cells within the CD8+CD11b- population.