Annals of Biomedical Engineering最新文献

Instrumented mouthguard systems (iMGs) are commonly used to study rigid body head kinematics across a variety of athletic environments. Previous work has found good fidelity for iMGs rigidly fixed to anthropomorphic test device (ATD) headforms when compared to reference systems, but few validation studies have focused on iMG performance in human cadaver heads. Here, we examine the performance of two boil-and-bite style iMGs in helmeted cadaver heads. Three unembalmed human cadaver heads were fitted with two instrumented boil-and-bite mouthguards [Prevent Biometrics and Diversified Technical Systems (DTS)] per manufacturer instructions. Reference sensors were rigidly fixed to each specimen. Specimens were fitted with a Riddell SpeedFlex American football helmet and impacted with a rigid impactor at three velocities and locations. All impact kinematics were compared at the head center of gravity. The Prevent iMG performed comparably to the reference system up to ~ 60 g in linear acceleration, but overall had poor correlation (CCC = 0.39). Prevent iMG angular velocity and BrIC generally well correlated with the reference, while underestimating HIC and overestimating HIC duration. The DTS iMG consistently overestimated the reference across all measures, with linear acceleration error ranging from 10 to 66%, and angular acceleration errors greater than 300%. Neither iMG demonstrated consistent agreement with the reference system. While iMG validation efforts have utilized ATD testing, this study highlights the need for cadaver testing and validation of devices intended for use in-vivo, particularly when considering realistic (non-idealized) sensor-skull coupling, when accounting for interactions with the mandible and when subject-specific anatomy may affect device performance.

In order to improve the ability of clinical diagnosis to differentiate articular cartilage (AC) injury of different origins, this study explores the sensitivity of mid-infrared (MIR) spectroscopy for detecting structural, compositional, and functional changes in AC resulting from two injury types. Three grooves (two in parallel in the palmar-dorsal direction and one in the mediolateral direction) were made via arthrotomy in the AC of the radial facet of the third carpal bone (middle carpal joint) and of the intermediate carpal bone (the radiocarpal joint) of nine healthy adult female Shetland ponies (age = 6.8 ± 2.6 years; range 4–13 years) using blunt and sharp tools. The defects were randomly assigned to each of the two joints. Ponies underwent a 3-week box rest followed by 8 weeks of treadmill training and 26 weeks of free pasture exercise before being euthanized for osteochondral sample collection. The osteochondral samples underwent biomechanical indentation testing, followed by MIR spectroscopic assessment. Digital densitometry was conducted afterward to estimate the tissue's proteoglycan (PG) content. Subsequently, machine learning models were developed to classify the samples to estimate their biomechanical properties and PG content based on the MIR spectra according to injury type. Results show that MIR is able to discriminate healthy from injured AC (91%) and between injury types (88%). The method can also estimate AC properties with relatively low error (thickness = 12.7% mm, equilibrium modulus = 10.7% MPa, instantaneous modulus = 11.8% MPa). These findings demonstrate the potential of MIR spectroscopy as a tool for assessment of AC integrity changes that result from injury.

Machine learning (ML) has led to significant advances in dentistry, easing the workload of professionals and improving the performance of various medical processes. The fields of periodontology and implantology can profit from these advances for tasks such as determining periodontally compromised teeth, assisting doctors in the implant planning process, determining types of implants, or predicting the occurrence of peri-implantitis. The current paper provides an overview of recent ML techniques applied in periodontology and implantology, aiming to identify popular models for different medical tasks, to assess the impact of the training data on the success of the automatic algorithms and to highlight advantages and disadvantages of various approaches. 48 original research papers, published between 2016 and 2023, were selected and divided into four classes: periodontology, implant planning, implant brands and types, and success of dental implants. These papers were analyzed in terms of aim, technical details, characteristics of training and testing data, results, and medical observations. The purpose of this paper is not to provide an exhaustive survey, but to show representative methods from recent literature that highlight the advantages and disadvantages of various approaches, as well as the potential of applying machine learning in dentistry.

Recently, the interest in the Helmet interface during non-invasive respiratory support (NIRS) has increased due to the COVID-19 pandemic. During NIRS, positive end-expiratory pressure (PEEP) can be given as continuous positive airway pressure (CPAP), which maintains a positive airway pressure throughout the whole respiratory cycle with Helmet as an interface (H-CPAP). The main disadvantage of the H-CPAP is the inability to measure tidal volume (V_T). Opto-electronic plethysmography (OEP) is a non-invasive technique that is not sensitive to gas compression/expansion inside the helmet. OEP acquisitions were performed on 28 healthy volunteers (14 females and 14 males) at baseline and during Helmet CPAP. The effect of posture (semi-recumbent vs. prone), flow (50 vs. 60 L/min), and PEEP (0 vs. 5 vs. 10 cmH₂O) on the ventilatory and thoracic-abdominal pattern and the operational volumes were investigated. Prone position limited vital capacity, abdominal expansion and chest wall recruitment. A constant flow of 60 L/min reduced the need for the subject to ventilate while having a slight recruitment effect (100 mL) in the semi-recumbent position. A progressive increasing recruitment was found with higher PEEP but limited by the prone position. It is possible to accurately measure tidal volume during H-CPAP to deliver non-invasive ventilatory support using opto-electronic plethysmography during different clinical settings.

Porous nickel-titanium (NiTi) manufactured using metal injection molding (MIM) has emerged as an innovative generation of drug-loaded stent materials. However, an increase in NiTi porosity may compromise its mechanical properties and cytocompatibility. This study aims to explore the potential of porous NiTi as a vascular drug delivery material and evaluate the impact of porosity on its drug loading and release, mechanical properties, and cytocompatibility. MIM, combined with the powder space-holder method, was used to fabricate porous NiTi alloys with three porosity levels. The mechanical properties of porous NiTi were assessed, as well as the surface cell growth capability. Furthermore, by loading rapamycin nanoparticles onto the surface and within the pores of porous NiTi, we evaluated the in vitro drug release behavior, inhibitory effect on cell proliferation, and inhibition of neointimal hyperplasia in vivo. The results demonstrated that an increase in porosity led to a decrease in the mechanical properties of porous NiTi, including hardness, tensile strength, and elastic modulus, and a decrease in the surface cell growth capability, affecting both cell proliferation and morphology. Concurrently, the loading capacity and release duration of rapamycin were extended with increasing porosity, resulting in enhanced inhibitory effects on cell proliferation in vitro and inhibition of neointimal hyperplasia in vivo. In conclusion, porous NiTi holds promise as a desirable vascular drug delivery material, but a balanced consideration of the influence of porosity on both mechanical properties and cytocompatibility is necessary to achieve an optimal balance among drug-loading and release performance, mechanical properties, and cytocompatibility.

Aortic valve (AV) disease is a common valvular lesion in the United States, present in about 5% of the population at age 65 with increasing prevalence with advancing age. While current replacement heart valves have extended life for many, their long-term use remains hampered by limited durability. Non-surgical treatments for AV disease do not yet exist, in large part because our understanding of AV disease etiology remains incomplete. The direct study of human AV disease remains hampered by the fact that clinical data is only available at the time of treatment, where the disease is at or near end stage and any time progression information has been lost. Large animal models, long used to assess replacement AV devices, cannot yet reproduce AV disease processes. As an important alternative mouse animal models are attractive for their ability to perform genetic studies of the AV disease processes and test potential pharmaceutical treatments. While mouse models have been used for cellular and genetic studies of AV disease, their small size and fast heart rates have hindered their use for tissue- and organ-level studies. We have recently developed a novel ex vivo micro-CT-based methodology to 3D reconstruct murine heart valves and estimate the leaflet mechanical behaviors (Feng et al. in Sci Rep 13(1):12852, 2023). In the present study, we extended our approach to 3D reconstruction of the in vivo functional murine AV (mAV) geometry using high-frequency four-dimensional ultrasound (4DUS). From the resulting 4DUS images we digitized the mAV mid-surface coordinates in the fully closed and fully opened states. We then utilized matched high-resolution µCT images of ex vivo mouse mAV to develop mAV NURBS-based geometric model. We then fitted the mAV geometric model to the in vivo data to reconstruct the 3D in vivo mAV geometry in the closed and open states in n = 3 mAV. Results demonstrated high fidelity geometric results. To our knowledge, this is the first time such reconstruction was ever achieved. This robust assessment of in vivo mAV leaflet kinematics in 3D opens up the possibility for longitudinal characterization of murine models that develop aortic valve disease.

The goal of this work was to develop a general blueprint for a semi-automated image processing tool (SIPT) to measure small, complex features of stent prototypes that can replace the current gold standard of manual measurements. The stents were designed using CAD software and manufactured via laser cutting. Stent prototypes were imaged using a Keyence microscope in top and side view orientations. The SIPT algorithm was developed in MATLAB to extract and measure 4 dimensions of the stent (inner and outer diameter, spring bend outer radius, spring bend width). The same dimensions were also manually measured by an experienced metrology technician as a gold standard comparison. We successfully made over 5000 unique measurements across the 4 key dimensions of 15 stents using the SIPT algorithm. Compared to the gold standard manual method, SIPT reduced measurement time by nearly 90% and increased the total number of measurements captured by over 2300%. The two one-sided test and Bland–Altman analysis demonstrated that SIPT achieved equivalency against the manual method of measurement for all 4 dimensions. In summary, we found that our SIPT software could be used to replace manual measurements and provided substantial time savings with consistent accuracy. Overall, this paper presents a generalizable workflow to isolate and measure critical features of stent prototypes that we believe will provide a valuable, cost-effective tool to other medical device designers seeking to rapidly iterate on unique stent designs or other manufactured parts with small and complex structures.

Rotational mechanics is a fundamental determinant of left ventricular ejection fraction (LVEF). The coding system currently employed in clinical practice does not distinguish between rotational patterns. We propose an alternative coding system that makes possible to identify the rotational pattern of the LV and relate it to myocardial function. Echocardiographic images were used to generate speckle tracking-derived transmural global longitudinal strain (tGLS) and rotational parameters. The existence of twist (basal and apical rotations in opposite directions) is expressed as a rotational gradient with a positive value that is the sum of the basal and apical rotation angles. Conversely, when there is rigid rotation (basal and apical rotations in the same direction) the resulting gradient is assigned a negative value that is the subtraction between the two rotation angles. The rotational patterns were evaluated in 87 healthy subjects and 248 patients with LV hypertrophy (LVH) and contrasted with their myocardial function. Our approach allowed us to distinguish between the different rotational patterns. Twist pattern was present in healthy controls and 104 patients with LVH and normal myocardial function (tGLS ≥ 17%, both). Among 144 patients with LVH and myocardial dysfunction (tGLS < 17%), twist was detected in 83.3% and rigid rotation in 16.7%. LVEF was < 50% in 34.7%, and all patients with rigid rotation had a LVEF < 50%. The gradient rotational values showed a close relationship with LVEF (r = 0.73; p < 0.001). The proposed coding system allows us to identify the rotational patterns of the LV and to relate their values with LVEF.