Towards the generation of a parametric foot model using principal component analysis: A pilot study

Virtual Surgical Planning: Modeling from the Present to the Future

Virtual surgery planning is a non-invasive procedure, which uses digital clinical data for diagnostic, procedure selection and treatment planning purposes, including the forecast of potential outcomes. The technique begins with 3D data acquisition, using various methods, which may or may not utilize ionizing radiation, such as 3D stereophotogrammetry, 3D cone-beam CT scans, etc. Regardless of the imaging technique selected, landmark selection, whether it is manual or automated, is the key to transforming clinical data into objects that can be interrogated in virtual space. As a prerequisite, the data require alignment and correspondence such that pre- and post-operative configurations can be compared in real and statistical shape space. In addition, these data permit predictive modeling, using either model-based, data-based or hybrid modeling. These approaches provide perspectives for the development of customized surgical procedures and medical devices with accuracy, precision and intelligence. Therefore, this review briefly summarizes the current state of virtual surgery planning.

Prediction of Diabetic Foot Ulceration: The Value of Using Microclimate Sensor Arrays

BACKGROUND

Accurately predicting the risk of diabetic foot ulceration (DFU) could dramatically reduce the enormous burden of chronic wound management and amputation. Yet, the current prognostic models are unable to precisely predict DFU events. Typically, efforts have focused on individual factors like temperature, pressure, or shear rather than the overall foot microclimate.

METHODS

A systematic review was conducted by searching PubMed reports with no restrictions on start date covering the literature published until February 20, 2019 using relevant keywords, including temperature, pressure, shear, and relative humidity. We review the use of these variables as predictors of DFU, highlighting gaps in our current understanding and suggesting which specific features should be combined to develop a real-time microclimate prognostic model.

RESULTS

The current prognostic models rely either solely on contralateral temperature, pressure, or shear measurement; these parameters, however, rarely reach 50% specificity in relation to DFU. There is also considerable variation in methodological investigation, anatomical sensor configuration, and resting time prior to temperature measurements (5-20 minutes). Few studies have considered relative humidity and mean skin resistance.

CONCLUSION

Very limited evidence supports the use of single clinical parameters in predicting the risk of DFU. We suggest that the microclimate as a whole should be considered to predict DFU more effectively and suggest nine specific features which appear to be implicated for further investigation. Technology supports real-time in-shoe data collection and wireless transmission, providing a potentially rich source of data to better predict the risk of DFU.

Development of a simplified, reproducible, parametric 3D model of the talus

Computational foot models have significant application in surgical decision making, injury and disease diagnosis and prevention, sports performance analysis and footwear engineering. However, due to the substantial time in model building and the heavy computational costs from the complexity of the models, daily clinical application of these foot models has yet to be achieved. Much of the previous research adopted a detailed-geometry approach in modeling bones that potentially contributed to the heavy computational costs. In this research, we developed a computational talus model based on CT section image data, image reconstruction and segmentation, contact surface identification, standard shape fitting, and finite element auto meshing algorithms. Modeling the bones as rigid is common, and modeling the contact surfaces only for the rigid body saves additional computational resources. Priority, therefore, in the shape fitting with optimization is given to the contact surfaces of the talus. Thirteen sets (9 males and 4 females) of CT section data were obtained. Image reconstruction, segmentation and bone labeling were conducted on each set of CT data to identify talus and its adjacent bones. Contact surfaces of the talus were then identified based on bone spatial relationships. Apart from the talar dome surface which was fitted by a 3rd-order polynomial, standard shapes such as ellipsoids and planes were used to fit the selected contact surfaces so that the geometrical parameters maintain physical significance. Based on these parameters, we automatically recreated and meshed the least-squares fitted shapes rapidly with limited elements. Last, mean major contact surfaces of the talus were obtained and fitted by standard shapes. Although the number of samples in this study was relatively small, our method provides sufficient and accurate geometric parameters of these contact surfaces to completely describe and reproduce the talus, on both a subject specific and average basis. The method for describing the talus here helps to parametrize computational models using planes and ellipsoids, improves surgical decision making and implants with a more precise and physically significant measures, and the description provides bone geometric parameters which can later be used to relate risk analysis for bone shape specific injury rates.

Hind- and midfoot bone morphology varies with foot type and sex

Foot type has been associated with pain, injury, and altered gait mechanics. Morphological variations in foot bones due to foot type variation may impact surgical and therapeutic treatments. The purpose of this study was to utilize principal component analysis (PCA) to determine how morphology of the hind- and midfoot bones differs among foot types and sex. The calcaneus, talus, navicular, and cuboid were segmented using previously obtained computed tomography (CT) scans and converted to surface models. The CTs were sorted into four foot types-cavus, neutrally aligned, asymptomatic planus, and symptomatic planus. Morphometric shape analysis software (Geomorph) was used to perform a PCA to determine which components varied between foot types and between sexes. The calcaneus showed planus feet of both types to have calcanei that have decreased height and increased length compared to neutrally aligned feet. The talus demonstrated increased posterior mass for cavus feet compared to neutrally aligned feet. For the navicular, symptomatic planus had a more posteriorly positioned tuberosity and were wider than asymptomatic planus feet. The cuboid did not exhibit any differences between foot types. Sex differences, found only at the talus and navicular, were subtle. PCA is an objective technique that helped elucidate differences in bone morphology between foot types and sex without needing to determine the features of interest before comparing groups. Understanding these variations can help inform diagnosis of foot pathologies and surgical protocols as well as improve computer models of the foot. Published 2018. This article is a U.S. Government work and is in the public domain in the USA. J Orthop Res 9999:1-16, 2019.

A spatial filtering approach to environmental emotion perception based on electroencephalography

Studies have demonstrated that visual built environments can affect the emotions of individuals, which can be recorded and investigated using electroencephalography (EEG). To study emotional intensity in adolescents exposed to different visual built environments, we proposed an EEG-based spatial filtering method using Independent Component Analysis (ICA). Specifically, to identify effective video stimuli to induce emotions, we first developed a stimulus selection strategy using the normalized valence/arousal space model. Subsequently, we designed an optimum ICA-based spatial filter by analyzing independent component-to-electrode mapping patterns in different emotional states. Based on this, EEG signals with five emotional intensities in terms of arousal and valence dimensions were linearly projected by the designed filter to extract feature parameters. Finally, we used the Support Vector Model as the classifier to recognize emotions. In the laboratory environment, the average recognition accuracy ratios for the valence and arousal dimensions were 73.35% and 68.54% (within-participant test) and 66.98% and 62.62% (between-participant test), respectively, for the 10 participants. The experimental results validated the feasibility of the proposed ICA-based spatial filtering algorithm for emotional intensity recognition.