Predicting acetabular version in native hip joints through plain x-ray radiographs: a comparative analysis of convolutional neural network model and the current gold standard, with insights and implications for hip arthroplasty.

Introduction: This study presents the development and validation of a Deep Learning Convolutional Neural Network (CNN) model for estimating acetabular version (AV) from native hip plain radiographs. Methods: Utilizing a dataset comprising 300 participants with unrelated pelvic complaints, the CNN model was trained and evaluated against CT-Scans, considered the gold standard, using a 5-fold cross-validation. Results: Notably, the CNN model exhibited a robust performance, demonstrating a strong Pearson correlation with CT-Scans (right hip: r = 0.70, p < 0.001; left hip: r = 0.71, p < 0.001) and achieving a mean absolute error of 2.95°. Remarkably, over 83% of predictions yielded errors ≤5°, highlighting the model's high precision in AV estimation. Discussion: The model holds promise in preoperative planning for hip arthroplasty, potentially reducing complications like recurrent dislocation and component wear. Future directions include further refinement of the CNN model, with ongoing investigations aimed at enhancing preoperative planning potential and ensuring comprehensive assessment across diverse patient populations, particularly in diseased cases. Additionally, future research could explore the model's potential value in scenarios necessitating minimized ionizing radiation exposure, such as post-operative evaluations.

Concurrent Learning Approach for Estimation of Pelvic Tilt from Anterior-Posterior Radiograph.

Accurate and reliable estimation of the pelvic tilt is one of the essential pre-planning factors for total hip arthroplasty to prevent common post-operative complications such as implant impingement and dislocation. Inspired by the latest advances in deep learning-based systems, our focus in this paper has been to present an innovative and accurate method for estimating the functional pelvic tilt (PT) from a standing anterior-posterior (AP) radiography image. We introduce an encoder-decoder-style network based on a concurrent learning approach called VGG-UNET (VGG embedded in U-NET), where a deep fully convolutional network known as VGG is embedded at the encoder part of an image segmentation network, i.e., U-NET. In the bottleneck of the VGG-UNET, in addition to the decoder path, we use another path utilizing light-weight convolutional and fully connected layers to combine all extracted feature maps from the final convolution layer of VGG and thus regress PT. In the test phase, we exclude the decoder path and consider only a single target task i.e., PT estimation. The absolute errors obtained using VGG-UNET, VGG, and Mask R-CNN are 3.04 ± 2.49, 3.92 ± 2.92, and 4.97 ± 3.87, respectively. It is observed that the VGG-UNET leads to a more accurate prediction with a lower standard deviation (STD). Our experimental results demonstrate that the proposed multi-task network leads to a significantly improved performance compared to the best-reported results based on cascaded networks.

Effect of triangular electrode schemes on Broca's cortical stimulation: conventional and HD-tDCS study.

Transcranial direct current stimulation (tDCS) is a therapeutic and complementary treatment in several cognitive diseases, psychiatric disorders, and disabilities that occur due to an accident or stroke. In the current research, we aimed to boost some parts of the stimulation structure and proposed a new electrode scheme in the mentioned approach. After segmenting magnetic resonance imaging (MRI) scans and using a tissue correction routine algorithm, we attempted to create an appropriate head model and electrode placement according to electric stimulation, whereby we completed tDCS processing. The considered electrodes are divided into two general categories. All the considered electrodes consist of rectangular, circular, triangular, and empty triangular patches with specific dimensions. We investigated common electrode schemes and introduced better electrode schemes for more effective cortical stimulation. We observed that the triangular electrodes in the conventional and anodal arrangement in the triangular 4 × 1 HD-tDCS create more electric field than others. Also, we calculated the current density and attempted to substantially improve it. Therefore, we recommended the empty triangular schemes. We investigated the designed model thoroughly and observed that it increased the current density not only in the conventional but also in the HD-tDCS. Graphical abstract.

Optimization of montages and electric currents in tDCS.

In order to optimize the capability of transcranial Direct Current Stimulation (tDCS), electrode arrangements and the inward current stimulation are taken into account as two crucial factors. In this contribution, in order to specify the electrode positions, a detailed protocol is investigated, in which regarding the intended targeted regions, the optimal montages with an arbitrary number of electrodes are developed. After designing the positions of all active and returned electrodes, the corresponding inward current density is determined for each electrode. The outcomes of the simulation and the electric field distributions in the hand cortex prove that the proposed protocol is capable of improving the tDCS efficiency substantially in all head layers. Furthermore, in order to compare our approach with the other works found in the literature, a performance evaluation is curried out by calculating the maximum electric field distribution in the targeted region. This study shows that it improves tDCS efficiency virtually 2.5 times in comparison to High Definition (HD) montages in the gray matter and nearly 1.5 times in comparison to the other inner layers. Such an outstanding achievement in the gray matter can be regarded as an interesting standpoint in tDCS-rehabilitation studies.

EEG-based single-channel authentication systems with optimum electrode placement for different mental activities.

BACKGROUND: Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authentication and security is a very important issue in our life and brainwave-based authentication is an addition to biometric authentication systems, which has many advantages over others. In this paper, we study the performance of a single channel brainwave-based authentication systems and select optimum channels based on mental activities. METHODS: In this study, we used a dataset with five mental activities with seven subjects (325 samples). The EEG based authentication system includes three pre-processing steps, feature extraction, and classification. Features for Subject Authentication, are obtained from discrete Fourier transform, discrete wavelet transform, autoregressive modeling, and entropy features. Then these features are classified using the Neural Network, Bayesian network and Support Vector Machine. RESULTS: We achieved accuracy in the range of 97-98% mean accuracy with Neural Network classifier for single-channel authentication system with optimum electrode placement for mental activity. We also analyzed the authentication system independently from the type of mental activity and chose channel O2 as the optimum channel with an accuracy of 95%. CONCLUSIONS: Channel optimization can obtain higher performance by reducing the number of EEG channels and defined the optimum electrode placement for different mental activities.

Dental Segmentation in Cone-beam Computed Tomography Images Using Watershed and Morphology Operators.

Teeth segmentation is an important task in computer-aided procedures and clinical diagnosis. In this paper, we propose an accurate and robust algorithm based on watershed and morphology operators for teeth and pulp segmentation and a new approach for enamel segmentation in cone-beam computed tomography (CBCT) images. Proposed method consists of five steps: acquiring appropriate CBCT image, image enhancement, teeth segmentation using the marker-controlled watershed (MCW), enamel segmentation by global threshold, and finally, utilizing the MCW for pulp segmentation. Proposed algorithms evaluated on a dataset consisting 69 patient images. Experimental results show a high accuracy and specificity for teeth, enamel, and pulp segmentation. MCW algorithm and local threshold are accurate and robust approaches to segment tooth, enamel, and pulp tissues. Methods overcome the over-segmentation phenomenon and artifacts reduction.

Fusion of ECG and ABP signals based on wavelet transform for cardiac arrhythmias classification.

BACKGROUND AND OBJECTIVE: Each of Electrocardiogram (ECG) and Atrial Blood Pressure (ABP) signals contain information of cardiac status. This information can be used for diagnosis and monitoring of diseases. The majority of previously proposed methods rely only on ECG signal to classify heart rhythms. In this paper, ECG and ABP were used to classify five different types of heart rhythms. To this end, two mentioned signals (ECG and ABP) have been fused. METHODS: These physiological signals have been used from MINIC physioNet database. ECG and ABP signals have been fused together on the basis of the proposed Discrete Wavelet Transformation fusion technique. Then, some frequency features were extracted from the fused signal. To classify the different types of cardiac arrhythmias, these features were given to a multi-layer perceptron neural network. RESULTS: In this study, the best results for the proposed fusion algorithm were obtained. In this case, the accuracy rates of 96.6%, 96.9%, 95.6% and 93.9% were achieved for two, three, four and five classes, respectively. However, the maximum classification rate of 89% was obtained for two classes on the basis of ECG features. CONCLUSIONS: It has been found that the higher accuracy rates were acquired by using the proposed fusion technique. The results confirmed the importance of fusing features from different physiological signals to gain more accurate assessments.

PET and MRI image fusion based on combination of 2-D Hilbert transform and IHS method.

BACKGROUND: The process of medical image fusion is combining two or more medical images such as Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) and mapping them to a single image as fused image. So purpose of our study is assisting physicians to diagnose and treat the diseases in the least of the time. METHODS: We used Magnetic Resonance Image (MRI) and Positron Emission Tomography (PET) as input images, so fused them based on combination of two dimensional Hilbert transform (2-D HT) and Intensity Hue Saturation (IHS) method. Evaluation metrics that we apply are Discrepancy (Dk) as an assessing spectral features and Average Gradient (AGk) as an evaluating spatial features and also Overall Performance (O.P) to verify properly of the proposed method. RESULTS: In this paper we used three common evaluation metrics like Average Gradient (AGk) and the lowest Discrepancy (Dk) and Overall Performance (O.P) to evaluate the performance of our method. Simulated and numerical results represent the desired performance of proposed method. CONCLUSIONS: Since that the main purpose of medical image fusion is preserving both spatial and spectral features of input images, so based on numerical results of evaluation metrics such as Average Gradient (AGk), Discrepancy (Dk) and Overall Performance (O.P) and also desired simulated results, it can be concluded that our proposed method can preserve both spatial and spectral features of input images.

A comparative study on preprocessing techniques in diabetic retinopathy retinal images: illumination correction and contrast enhancement.

To investigate the effect of preprocessing techniques including contrast enhancement and illumination correction on retinal image quality, a comparative study was carried out. We studied and implemented a few illumination correction and contrast enhancement techniques on color retinal images to find out the best technique for optimum image enhancement. To compare and choose the best illumination correction technique we analyzed the corrected red and green components of color retinal images statistically and visually. The two contrast enhancement techniques were analyzed using a vessel segmentation algorithm by calculating the sensitivity and specificity. The statistical evaluation of the illumination correction techniques were carried out by calculating the coefficients of variation. The dividing method using the median filter to estimate background illumination showed the lowest Coefficients of variations in the red component. The quotient and homomorphic filtering methods after the dividing method presented good results based on their low Coefficients of variations. The contrast limited adaptive histogram equalization increased the sensitivity of the vessel segmentation algorithm up to 5% in the same amount of accuracy. The contrast limited adaptive histogram equalization technique has a higher sensitivity than the polynomial transformation operator as a contrast enhancement technique for vessel segmentation. Three techniques including the dividing method using the median filter to estimate background, quotient based and homomorphic filtering were found as the effective illumination correction techniques based on a statistical evaluation. Applying the local contrast enhancement technique, such as CLAHE, for fundus images presented good potentials in enhancing the vasculature segmentation.

Discriminant analysis between myocardial infarction patients and healthy subjects using wavelet transformed signal averaged electrocardiogram and probabilistic neural network.

There are a variety of electrocardiogram based methods to detect myocardial infarction (MI) patients. This study used the signal averaged electrocardiogram (SAECG) and its wavelet coefficient as an index to detect MI. Orthogonal leads signals from 50 acute myocardial infarction (AMI) and 50 healthy subjects were selected from the national metrology institute of Germany (PTB diagnostic database). They were filtered and discrete wavelet transformed was exerted on them. Four conventional features and two new features introduced in this study were extracted from SAECG and its wavelet decompositions. Finally for data classification, probabilistic neural network were used. This method was able to detect and discriminate AMI patients from healthy subjects using the probabilistic neural network, which shows 93.0% sensitivity at 86.0% specificity with 89.5% accuracy. This technique and the new extracted features showed good promise in the identification of MI patients. However, the sensitivity and specificity is comparable with other findings and has high accuracy although we extracted only 6 features.