A Multi-armed Unfurling Actuator for Airway Lumen Measurement.

Objective measurement of the lumen area demands an intraoperative diagnostic tool to aid on-site decision-making. We present a compliant mechanism-based unfurling actuator assembly integrated with a shaft connected to a motorized encoder to translate torque from the user at the proximal end to the actuator at the distal end. The actuator assembly has flexible arms coiled inside a cylindrical casing that moves radially outward upon actuation. Leveraging 3D printing of flexible materials, the unfurling actuator's four-arm design enables patency measurements in circumferential tracheal stenosis of varying grades. The rotary encoder output is correlated with the radially outward movement of the unfurling arms to estimate the lumen diameter. The measurement stability is analyzed using process control charts; data distribution over ten iterations reveals nearly 100% of process data falls between ±3 sigma (Upper and Lower control limits). Comparing measurements from the tool with direct measurement (vernier caliper) and ImageJ analysis, one-way ANOVA for circular morphology yields no significant differences in diameter p = 0.974 and area measurements p = 0.975.Clinical Relevance- Central airway narrowing reduces the effective lumen area in the tracheal and bronchial segments. Grading the degree of narrowing is often based on a suspicion index. A quick but thorough assessment of the airway caliber is essential in emergent or planned intubation, whether congenital, iatrogenic, or idiopathic tracheal stenosis.

Electromechanical Characterization of Human Brain Tissues: A Potential Biomarker for Tumor Delineation.

OBJECTIVE: Accurate identification of surgical margins in brain tumors is of significant prognostic importance. Despite the availability of methods such as 5-ALA and image guidance, recognizing tumor boundary is highly subjective, dependant on recognizing subtle changes in tissue characteristics including texture and color to aid distinction. METHOD: Design and development of a semi-automated system integrated with MEMS-based electromechanical sensors to enable an objective and reliable method of distinguishing tumors from normal brain tissue. Simultaneous electrical impedance and viscoelastic characterization of three types of freshly excised gliomas (glioblastoma (GBM), astrocytoma (AST), and oligodendroglioma (OLI)) (N = 8 each) and seventeen different normal brain regions (N = 6 each) obtained postmortem. RESULTS: The electrical impedance of gliomas (462±56Ω) was found to be significantly lower than corresponding normal (1267±515Ω) regions at 100kHz (p = 7.46e-11). The difference in the impedance between individual tumor types and corresponding normal regions was also statistically significant (p = 1e-8), suggesting accurate tumor delineation. There were distinct differences in the viscoelastic relaxation responses of high-grade and low-grade gliomas. White matter regions demonstrated higher impedance and faster stress relaxation compared to grey matter regions as a characteristic of their structural composition. CONCLUSION: We demonstrate that simultaneous electromechanical characterization of brain tumors and normal brain tissues can be an effective biomarker for tumor delineation, grading, and studying heterogeneity between the brain regions. SIGNIFICANCE: The observations suggest the potential use of the technology in a clinical setting to achieve gross total resection and improve treatment outcomes by helping surgeons perform real-time risk evaluation during surgery.

Engineering approaches for characterizing soft tissue mechanical properties: A review.

From cancer diagnosis to detailed characterization of arterial wall biomechanics, the elastic property of tissues is widely studied as an early sign of disease onset. The fibrous structural features of tissues are a direct measure of its health and functionality. Alterations in the structural features of tissues are often manifested as local stiffening and are early signs for diagnosing a disease. These elastic properties are measured ex vivo in conventional mechanical testing regimes, however, the heterogeneous microstructure of tissues can be accurately resolved over relatively smaller length scales with enhanced spatial resolution using techniques such as micro-indentation, microelectromechanical (MEMS) based cantilever sensors and optical catheters which also facilitate in vivo assessment of mechanical properties. In this review, we describe several probing strategies (qualitative and quantitative) based on the spatial scale of mechanical assessment and also discuss the potential use of machine learning techniques to compute the mechanical properties of soft tissues. This work details state of the art advancement in probing strategies, associated challenges toward quantitative characterization of tissue biomechanics both from an engineering and clinical standpoint.