Biomechanical stability of a supra-acetabular pedicle screw Internal Fixation device (INFIX) vs External Fixation and plates for vertically unstable pelvic fractures

Abstract Background We have recently developed a subcutaneous anterior pelvic fixation technique (INFIX). This internal fixator permits patients to sit, roll over in bed and lie on their sides without the cumbersome external appliances or their complications. The purpose of this study was to evaluate the biomechanical stability of this novel supraacetabular pedicle screw internal fixation construct (INFIX) and compare it to standard internal fixation and external fixation techniques in a single stance pelvic fracture model. Methods Nine synthetic pelves with a simulated anterior posterior compression type III injury were placed into three groups (External Fixator, INFIX and Internal Fixation). Displacement, total axial stiffness, and the stiffness at the pubic symphysis and SI joint were calculated. Displacement and stiffness were compared by ANOVA with a Bonferroni adjustment for multiple comparisons Results The mean displacement at the pubic symphysis was 20, 9 and 0.8 mm for external fixation, INFIX and internal fixation, respectively. Plate fixation was significantly stiffer than the INFIX and external Fixator (P = 0.01) at the symphysis pubis. The INFIX device was significantly stiffer than external fixation (P = 0.017) at the symphysis pubis. There was no significant difference in SI joint displacement between any of the groups. Conclusions Anterior plate fixation is stiffer than both the INFIX and external fixation in single stance pelvic fracture model. The INFIX was stiffer than external fixation for both overall axial stiffness, and stiffness at the pubic symphysis. Combined with the presumed benefit of minimizing the complications associated with external fixation, the INFIX may be a more preferable option for temporary anterior pelvic fixation in situations where external fixation may have otherwise been used

Synthesizing Compact Hardware for Accelerating Inference from Physical Signals in Sensors

We present dimensional circuit synthesis, a new method for generating digital logic circuits that improve the efficiency of training and inference of machine learning models from sensor data. The hardware accelerators that the method generates are compact enough (a few thousand gates) to allow integration within low-cost miniaturized sensor integrated circuits, right next to the sensor transducer. The method takes as input a description of physical properties of relevant signals in the sensor transduction process and generates as output a Verilog register transfer level (RTL) description for a circuit that computes low-level features that exploit the units of measure of the signals in the system. We implement dimensional circuit synthesis as a backend to the compiler for Newton, a language for describing physical systems. We evaluate the backend implementation and the hardware it generates, on descriptions of 7 physical systems. The results show that our implementation of dimensional circuit synthesis generates circuits of as little as 1662 logic cells / 1239 gates for the systems we evaluate. We synthesize the designs generated by the dimensional circuit synthesis compilation backend for a low-power miniature FPGA targeted by its manufacturer at sensor interface applications. The circuits which the method generated use as little as 27% of the resources of the 2.15x2.5 mm FPGA. We measure the power dissipation of the FPGA's isolated core supply rail and show that, driven with a pseudorandom signal input stream, the synthesized designs use as little as 1.0 mW and no more than 5.8 mW. These results show the feasibility of integrating physics-inspired machine learning methods within low-cost miniaturized sensor integrated circuits, right next to the sensor transducer