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Purpose
The aim of this study was to compare several osteosynthesis techniques (intramedullary headless compression screws, T-plates, and Kirschner wires) for distal epiphyseal fractures of proximal phalanges in a human cadaveric model.
Methods
A total of 90 proximal phalanges from 30 specimens (index, ring, and middle fingers) were used for this study. After stripping off all soft tissue, a transverse distal epiphyseal fracture was simulated at the proximal phalanx. The 30 specimens were randomly assigned to 1 fixation technique (30 per technique), either a 3.0-mm intramedullary headless compression screw, locking plate fixation with a 2.0-mm T-plate, or 2 oblique 1.0-mm Kirschner wires. Displacement analysis (bending, distraction, and torsion) was performed using optical tracking of an applied random speckle pattern after osteosynthesis. Biomechanical testing was performed with increasing cyclic loading and with cyclic load to failure using a biaxial torsion-tension testing machine.
Results
Cannulated intramedullary compression screws showed significantly less displacement at the fracture site in torsional testing. Furthermore, screws were significantly more stable in bending testing. Kirschner wires were significantly less stable than plating or screw fixation in any cyclic load to failure test setup.
Conclusions
Intramedullary compression screws are a highly stable alternative in the treatment of transverse distal epiphyseal phalangeal fractures. Kirschner wires seem to be inferior regarding displacement properties and primary stability.
Clinical relevance
Fracture fixation of phalangeal fractures using plate osteosynthesis may have the advantage of a very rigid reduction, but disadvantages such as stiffness owing to the more invasive surgical approach and soft tissue irritation should be taken into account. Headless compression screws represent a minimally invasive choice for fixation with good biomechanical properties.
Searching optimal interplanetary trajectories for low-thrust spacecraft is usually a difficult and time-consuming task that involves much experience and expert knowledge in astrodynamics and optimal control theory. This is because the convergence behavior of traditional local optimizers, which are based on numerical optimal control methods, depends on an adequate initial guess, which is often hard to find, especially for very-low-thrust trajectories that necessitate many revolutions around the sun. The obtained solutions are typically close to the initial guess that is rarely close to the (unknown) global optimum. Within this paper, trajectory optimization problems are attacked from the perspective of artificial intelligence and machine learning. Inspired by natural archetypes, a smart global method for low-thrust trajectory optimization is proposed that fuses artificial neural networks and evolutionary algorithms into so-called evolutionary neurocontrollers. This novel method runs without an initial guess and does not require the attendance of an expert in astrodynamics and optimal control theory. This paper details how evolutionary neurocontrol works and how it could be implemented. The performance of the method is assessed for three different interplanetary missions with a thrust to mass ratio <0.15mN/kg (solar sail and nuclear electric).