Biomechanical comparison of two surgical methods for Hallux Valgus deformity: Exploring the use of artificial neural networks as a decision-making tool for orthopedists.

Hallux Valgus foot deformity affects gait performance. Common treatment options include distal oblique metatarsal osteotomy and chevron osteotomy. Nonetheless, the current process of selecting the appropriate osteotomy method poses potential biases and risks, due to its reliance on subjective human judgment and interpretation. The inherent variability among clinicians, the potential influence of individual clinical experiences, or inherent measurement limitations may contribute to inconsistent evaluations. To address this, incorporating objective tools like neural networks, renowned for effective classification and decision-making support, holds promise in identifying optimal surgical approaches. The objective of this cross-sectional study was twofold. Firstly, it aimed to investigate the feasibility of classifying patients based on the type of surgery. Secondly, it sought to explore the development of a decision-making tool to assist orthopedists in selecting the optimal surgical approach. To achieve this, gait parameters of twenty-three women with moderate to severe Hallux Valgus were analyzed. These patients underwent either distal oblique metatarsal osteotomy or chevron osteotomy. The parameters exhibiting differences in preoperative and postoperative values were identified through various statistical tests such as normalization, Shapiro-Wilk, non-parametric Wilcoxon, Student t, and paired difference tests. Two artificial neural networks were constructed for patient classification based on the type of surgery and to simulate an optimal surgery type considering postoperative walking speed. The results of the analysis demonstrated a strong correlation between surgery type and postoperative gait parameters, with the first neural network achieving a remarkable 100% accuracy in classification. Additionally, cases were identified where there was a mismatch with the surgeon's decision. Our findings highlight the potential of artificial neural networks as a complementary tool for surgeons in making informed decisions. Addressing the study's limitations, future research may investigate a wider range of orthopedic procedures, examine additional gait parameters and use more diverse and extensive datasets to enhance statistical robustness.

Isodoses-a set theory-based patient-specific QA measure to compare planned and delivered isodose distributions in photon radiotherapy.

BACKGROUND: The gamma index and dose-volume histogram (DVH)-based patient-specific quality assurance (QA) measures commonly applied in radiotherapy planning are unable to simultaneously deliver detailed locations and magnitudes of discrepancy between isodoses of planned and delivered dose distributions. By exploiting statistical classification performance measures such as sensitivity or specificity, compliance between a planned and delivered isodose may be evaluated locally, both for organs-at-risk (OAR) and the planning target volume (PTV), at any specified isodose level. Thus, a patient-specific QA tool may be developed to supplement those presently available in clinical radiotherapy. MATERIALS AND METHODS: A method was developed to locally establish and report dose delivery errors in three-dimensional (3D) isodoses of planned (reference) and delivered (evaluated) dose distributions simultaneously as a function the dose level and of spatial location. At any given isodose level, the total volume of delivered dose containing the reference and the evaluated isodoses is locally decomposed into four subregions: true positive-subregions within both reference and evaluated isodoses, true negative-outside of both of these isodoses, false positive-inside the evaluated isodose but not the reference isodose, and false negatives-inside the reference isodose but not the evaluated isodose. Such subregions may be established over the whole volume of delivered dose. This decomposition allows the construction of a confusion matrix and calculation of various indices to quantify the discrepancies between the selected planned and delivered isodose distributions, over the complete range of values of dose delivered. The 3D projection and visualization of the spatial distribution of these discrepancies facilitates the application of the developed method in clinical practice. RESULTS: Several clinical photon radiotherapy plans were analyzed using the developed method. In some plans at certain isodose levels, dose delivery errors were found at anatomically significant locations. These errors were not otherwise highlighted-neither by gamma analysis nor by DVH-based QA measures. A specially developed 3D projection tool to visualize the spatial distribution of such errors against anatomical features of the patient aids in the proposed analysis of therapy plans. CONCLUSIONS: The proposed method is able to spatially locate delivery errors at selected isodose levels and may supplement the presently applied gamma analysis and DVH-based QA measures in patient-specific radiotherapy planning.

Do commonly used postoperative orthopaedic shoes help off-load the forefoot?

PURPOSE: The aim of the experiment presented in this study was to determine the pressure distribution within the forefoot depending on the type of orthopaedic footwear used. METHODS: The study included 27 women aged 20 to 25. The Zebris FDM-2 dynamographic platform was used in foot pressure measurements. The load distribution was measured in three types of orthopaedic footwear: MedSurg, MedSurgPro and OrthoWedge. The full gait cycle was recorded. The Cavanagh masks were applied to the load distribution results processed into a graphic form. The data were analysed using Statistica v.13.1. RESULTS: In the forefoot area, i.e. the metatarsal bones and toes 1-5, the lowest loads were reported in the shoes that off-load the forefoot (0.2 N/cm², p < 0.001). In the area of the first to fifth metatarsal bones and the hallux, the highest load was observed in the rocker shoe, accounting for 19.7 N/cm² ( p < 0.001). For comparison, high pressure in the flat shoe was found in the area of toes 2 to 5 ( p < 0.001). CONCLUSIONS: In the area of the metatarsal and toe bones, the pressure exerted was highest in the commonly used rocker shoe. The flat shoe provides an even and uniform load in all areas of the forefoot, while this type of shoe does not significantly reduce the pressure forces on the forefoot. The shoe that was the most effective in off-loading was the forefoot off-loading shoe (OrthoWedge). Barefoot walking puts less load on the forefoot compared to the flat and rocker shoes used after orthopaedic procedures.

Are gamma passing rate and dose-volume histogram QA metrics correlated?

PURPOSE: The quality of a measured distribution of dose delivered against its corresponding radiotherapy plan is routinely assessed by gamma index (GI) and dose-volume histogram (DVH) metrics. Any correlation between error detection rates, as based on either of these approaches, while argued, has never been convincingly demonstrated. The dependence of the strength of correlation between the GI passing rate ( γ P ) and DVH quality assurance (QA) metrics on various elements of the therapy plan has not been systematically investigated. METHODS: A formal analysis of the relation between γ P and DVH metrics has been undertaken, leading to a relationship which may partly approximate γ P with respect to the DVH. This relationship was further validated by studying examples of simulated clinical radiotherapy plans and by studying the correlation between γ P and the derived relationship using a simple two-dimensional representations of the planning target volume (PTV) and organs at risk (OAR), where penumbra regions, distance-to-agreement tolerances and dose delivery errors were systematically varied. RESULTS: It is shown formally that there cannot be any correlation between γ P and other commonly applied DVH-derived QA measures. However, γ P may be partly approximated given the planned and measured DVH. The derived γ P approximation (the " γ -slope indicator") may be clinically useful in some practical cases of radiotherapy plan QA. CONCLUSIONS: In formal terms, there cannot be any correlation between γ P and any common DVH-calculated patient-specific measures, with respect to PTV or OAR. However, as demonstrated analytically and further confirmed in our simulation studies, the γ P approximation derived in this study (the " γ -slope indicator") may in some cases offer a degree of correlation between γ P and the PTV and OAR DVH QA metrics in measured and planned patient-specific dose distributions-which may be potentially useful in clinical practice.

Statistical approach to the selection of the tolerances for distance to agreement improves the quality control of the dose delivery in radiotherapy.

In the study, a local approach to setting reference tolerance values for the distance-to-agreement (DTA) component of the gamma index is proposed. The reference tolerance values are calculated in simulations, following a dose delivery model presented in a previous work. An analytical model for determining the quantiles of DTA distribution is also proposed and verified. It is shown that the distributions of DTA values normalized with either quantiles or standard deviation of DTA distributions are universal over analyzed plans and points within a single plan. This enables statistically sound inference about the quality of dose delivery. In particular, based on the normalized distributions the comparison of planned and delivered doses can be formulated within the framework of statistical inference as a problem of multiple statistical testing. For every evaluated point P of a plan, one may formulate and test a null hypothesis that there is no delivery error against an alternative hypothesis that there is a delivery error in P. It is also shown that the proposed approach is more sensitive than the current standard approach to shift errors in high dose gradient regions.

A simulation-based method for evaluating geometric tests of a linac c-arm in quality control in radiotherapy.

PURPOSE: Assessment of the accuracy of geometric tests of a linac used in external beam therapy is crucial for ensuring precise dose delivery. In this paper, a new simulation-based method for assessing accuracy of such geometric tests is proposed and evaluated on a set of testing procedures. METHODS: Linac geometry testing methods used in this study are based on an established design of a two-module phantom. Electronic portal imaging device (EPID) images of fiducial balls contained in these modules can be used to automatically reconstruct linac geometry. The projection of the phantom modules fiducial balls onto the EPID detector plane is simulated for assumed nominal geometry of a linac. Then, random errors are added to the coordinates of the projections of the centers of the fiducial balls and the linac geometry is reconstructed from these data. RESULTS: Reconstruction is performed for a set of geometric test designs and it is shown how the dispersion of the reconstructed values of geometric parameters depends on the design of a geometric test. Assuming realistic accuracy of EPID image analysis, it is shown that for selected testing plans the reconstruction accuracy of geometric parameters can be significantly better than commonly used action thresholds for these parameters. CONCLUSIONS: Proposed solution has the potential to improve geometric testing design and practice. It is an important part of a fully automated geometric testing solution.

Use of statistical approaches to improve the quality control of the dose delivery in radiotherapy.

The gamma index is a measure used routinely for the quality control of dose delivery in radiotherapy, implemented in commercial systems for the verification of treatment plans. It involves comparison of the difference between planned and delivered doses to a single reference. The same reference value is selected for all points in the plan that can potentially hide dose delivery errors, especially in medium and low dose areas. In this study, a receiver operating characteristic analysis is used to demonstrate the limits of the performance of the global gamma index as a method for detecting dose delivery errors. The performance of a global gamma index is compared with two approaches based on statistical tests for outlier detection. Two statistical approaches are considered: according to the first, the distribution of the delivered doses is estimated based on an appropriate calibration procedure. According to the second, the distribution of the delivered doses is estimated based on the detection of relatively homogeneous regions of a plan and analyzing the distributions of planned doses within these regions. The performance of the three approaches is compared based on analytical considerations and in simulations in which errors are intentionally introduced to the plan delivery and noise related to dose delivery is modeled. We have shown that a statistics-based approach to gamma analysis generally leads to better detection of true delivery errors. The results of analytical consideration coincide with the simulations. In simulations, we observe that both statistical approaches are better detectors of true delivery errors than the global method for the gamma-index passing rate in the range from 0.9-1.0. It is shown that the global gamma index is a weak detector of dose delivery errors, which in some circumstances behaves only slightly better than a purely random classifier.

Application of Computational Intelligence Methods for the Automated Identification of Paper-Ink Samples Based on LIBS.

Laser-induced breakdown spectroscopy (LIBS) is an important analysis technique with applications in many industrial branches and fields of scientific research. Nowadays, the advantages of LIBS are impaired by the main drawback in the interpretation of obtained spectra and identification of observed spectral lines. This procedure is highly time-consuming since it is essentially based on the comparison of lines present in the spectrum with the literature database. This paper proposes the use of various computational intelligence methods to develop a reliable and fast classification of quasi-destructively acquired LIBS spectra into a set of predefined classes. We focus on a specific problem of classification of paper-ink samples into 30 separate, predefined classes. For each of 30 classes (10 pens of each of 5 ink types combined with 10 sheets of 5 paper types plus empty pages), 100 LIBS spectra are collected. Four variants of preprocessing, seven classifiers (decision trees, random forest, k-nearest neighbor, support vector machine, probabilistic neural network, multi-layer perceptron, and generalized regression neural network), 5-fold stratified cross-validation, and a test on an independent set (for methods evaluation) scenarios are employed. Our developed system yielded an accuracy of 99.08%, obtained using the random forest classifier. Our results clearly demonstrates that machine learning methods can be used to identify the paper-ink samples based on LIBS reliably at a faster rate.