Design optimization procedure for an orthopedic insole having a continuously variable stiffness/shape to reduce the plantar pressure in the foot of a diabetic patient

Advancements in diabetic foot insoles: a comprehensive review of design, manufacturing, and performance evaluation

The escalating prevalence of diabetes has accentuated the significance of addressing the associated diabetic foot problem as a major public health concern. Effectively offloading plantar pressure stands out as a crucial factor in preventing diabetic foot complications. This review comprehensively examines the design, manufacturing, and evaluation strategies employed in the development of diabetic foot insoles. Furthermore, it offers innovative insights and guidance for enhancing their performance and facilitating clinical applications. Insoles designed with total contact customization, utilizing softer and highly absorbent materials, as well as incorporating elliptical porous structures or triply periodic minimal surface structures, prove to be more adept at preventing diabetic foot complications. Fused Deposition Modeling is commonly employed for manufacturing; however, due to limitations in printing complex structures, Selective Laser Sintering is recommended for intricate insole designs. Preceding clinical implementation, in silico and in vitro testing methodologies play a crucial role in thoroughly evaluating the pressure-offloading efficacy of these insoles. Future research directions include advancing inverse design through machine learning, exploring topology optimization for lightweight solutions, integrating flexible sensor configurations, and innovating new skin-like materials tailored for diabetic foot insoles. These endeavors aim to further propel the development and effectiveness of diabetic foot management strategies. Future research avenues should explore inverse design methodologies based on machine learning, topology optimization for lightweight structures, the integration of flexible sensors, and the development of novel skin-like materials specifically tailored for diabetic foot insoles. Advancements in these areas hold promise for further enhancing the effectiveness and applicability of diabetic foot prevention measures.

Plantar pressure gradient and pressure gradient angle are affected by inner pressure of air insole

Clinically, air insoles may be applied to shoes to decrease plantar pressure gradient (PPG) and increase plantar gradient angle (PGA) to reduce foot ulcers. PPG and PGA may cause skin breakdown. The effects of different inner pressures of inflatable air insoles on dynamic PPG and PGA distributions are largely unknown in non-diabetics and people with diabetes. This study aimed to explore the impact of varying inner air insole pressures on PPG and PGA to establish early mitigation strategies for people at risk of foot ulcers. A repeated measures study design, including three air insoles (80 mmHg, 160 mmHg, and 240 mmHg) and two walking durations (10 and 20 min) for a total of six walking protocols, was tested on 13 healthy participants (height, 165.8 ± 8.4 cm; age, 27.0 ± 7.3 years; and weight, 56.0 ± 7.9 kg, BMI: 20.3 ± 1.7 kg/m^2) over three consecutive weeks. PPG, a measurement of the spatial variation in plantar pressure around the peak plantar pressure (PPP) and PGA, a variation in the gradient direction values at the three plantar regions, big toe (T1), first metatarsal head (M1), and second metatarsal head (M2), were calculated. This study indicated that PPG was lower at 80 mmHg air insoles after 20 min of walking in the M1 region (p = 0.010). The PGA in the M2 increased at an air insole of 80 mmHg compared to 240 mmHg (p = 0.015). Compared to 20 min, the 10 min walking duration at 240 mmHg of air insole had the lowest PPG in the M1 (p = 0.015) and M2 (p = 0.034) regions. The 80 mmHg air insole significantly lowered the PPG compared to a 160 mmHg and 240 mmHg air insole. Moreover, the 80 mmHg air insole significantly decreased PPP and increased PGA compared to the 160 mmHg and 240 mmHg air insole. A shorter walking period (10 min) significantly lowered PPG. The findings of this study suggest that people with a higher risk of foot ulcers should wear softer air insoles to have a lower PPG, as well as an increased PGA.

Surrogate optimization of a lattice foot orthotic

BACKGROUND

Additive manufacturing enables to print patient-specific Foot Orthotics (FOs). In FOs featuring lattice structures, the variation of the cell's dimensions provides a locally variable stiffness to meet the therapeutic needs of each patient. In an optimization problem, however, using explicit Finite Element (FE) simulation of lattice FOs with converged 3D elements is computationally prohibitive. This paper presents a framework to efficiently optimize the cell's dimensions of a honeycomb lattice FO for flat foot condition.

METHODS

We built a surrogate based on shell elements whose mechanical properties were computed by the numerical homogenization technique. The model was submitted to a static pressure distribution of a flat foot and it predicted the displacement field for a given set of geometrical parameters of the honeycomb FO. This FE simulation was considered as a black-box and a derivative-free optimization solver was employed. The cost function was defined based on the difference between the predicted displacement by the model against a therapeutic target displacement.

RESULTS

Using the homogenized model as a surrogate significantly accelerated the stiffness optimization of the lattice FO. The homogenized model could predict the displacement field 78 times faster than the explicit model. When 2000 evaluations were required in an optimization problem, the computational time was reduced from 34 days to 10 hours using the homogenized model rather than explicit model. Moreover, in the homogenized model, there was no need to re-create and re-mesh the insole's geometry in each iteration of the optimization. It was only required to update the effective properties.

CONCLUSION

The presented homogenized model can be used as a surrogate within an optimization framework to customize cell's dimensions of honeycomb lattice FO in a computationally efficient manner.

Magnetic Resonance Imaging Data Features to Evaluate the Efficacy of Compound Skin Graft for Diabetic Foot

This study aimed to analyze the role of magnetic resonance imaging (MRI) data characteristics based on the deep learning algorithm in evaluating the treatment of diabetic foot (DF) with composite skin graft. In this study, 78 patients with DF were randomly rolled into the experimental group (composite skin graft) and control group (autologous skin graft) with 39 patients in each group. MRI scans were performed before and after treatment to compare the changes of experimental observation indicators such as healing time, recurrence rate, and scar score. The results showed that T1-weighted imaging (T1WI) of the scanning sequence was considerably increased in the experimental group after treatment. The signal intensity of fat-suppressed T2-weighted imaging (T2WI) and fat-suppressed T1WI enhancement sequences was considerably decreased $\left(P<0.05\right)$ . In addition, compared with the control group, the recurrence rate, healing time, and scar score in the experimental group were considerably decreased $\left(P<0.05\right)$ . The accuracy, specificity, and sensitivity of MRI imaging information in evaluating the therapeutic effect of DF patients were 85.2%, 89.75%, and 86.47%, respectively. According to the specificity and sensitivity, the subject operating characteristic curve was drawn, and the area under the curve was determined to be 0.838. In summary, MRI image data characteristics based on the deep learning algorithm can provide auxiliary reference information for the efficacy evaluation of compound skin transplantation for DF.