Reliable Ground Research Articles

Validation of expert system enhanced deep learning algorithm for automated screening for COVID-Pneumonia on chest X-rays

SARS-CoV2 pandemic exposed the limitations of artificial intelligence based medical imaging systems. Earlier in the pandemic, the absence of sufficient training data prevented effective deep learning (DL) solutions for the diagnosis of COVID-19 based on X-Ray data. Here, addressing the lacunae in existing literature and algorithms with the paucity of initial training data; we describe CovBaseAI, an explainable tool using an ensemble of three DL models and an expert decision system (EDS) for COVID-Pneumonia diagnosis, trained entirely on pre-COVID-19 datasets. The performance and explainability of CovBaseAI was primarily validated on two independent datasets. Firstly, 1401 randomly selected CxR from an Indian quarantine center to assess effectiveness in excluding radiological COVID-Pneumonia requiring higher care. Second, curated dataset; 434 RT-PCR positive cases and 471 non-COVID/Normal historical scans, to assess performance in advanced medical settings. CovBaseAI had an accuracy of 87% with a negative predictive value of 98% in the quarantine-center data. However, sensitivity was 0.66–0.90 taking RT-PCR/radiologist opinion as ground truth. This work provides new insights on the usage of EDS with DL methods and the ability of algorithms to confidently predict COVID-Pneumonia while reinforcing the established learning; that benchmarking based on RT-PCR may not serve as reliable ground truth in radiological diagnosis. Such tools can pave the path for multi-modal high throughput detection of COVID-Pneumonia in screening and referral.

Bus journey simulation to develop public transport predictive algorithms

Encouraging the use of public transport is essential to combat congestion and pollution in an urban environment. To achieve this, the reliability of arrival time prediction should be improved as this is one area of improvement frequently requested by passengers. The development of accurate predictive algorithms requires good quality data, which is often not available. Here we demonstrate a method to synthesise data using a reference curve approach derived from very limited real world data without reliable ground truth. This approach allows the controlled introduction of artefacts and noise to simulate their impact on prediction accuracy. To illustrate these impacts, a recurrent neural network next-step prediction is used to compare different scenarios in two different UK cities. The results show that a realistic data synthesis is possible, allowing for controlled testing of predictive algorithms. It also highlights the importance of reliable data transmission to gain such data from real world sources. Our main contribution is the demonstration of a synthetic data generator for public transport data, which can be used to compensate for low data quality. We further show that this data generator can be used to develop and enhance predictive algorithms in the context of urban bus networks if high-quality data is limited, by mixing synthetic and real data. • Method to generate synthetic bus journeys based on very limited and unreliable data. • Insights into data quality challenges in public transport systems. • Algorithms can be improved by training on a mixture of synthetic and real-world data. • The demonstrated data synthesis could be the base for a public transport benchmarking dataset.

LiDAR-based Structure Tracking for Agricultural Robots: Application to Autonomous Navigation in Vineyards

Autonomous navigation is a key defining feature that allows agricultural robots to perform automated farming tasks. Global navigation satellite system (GNSS) technology is providing autonomous navigation solutions for current commercial robotic platforms that can achieve centimeter-level accuracy when real-time kinematic (RTK) corrections are available. However, GNSS-based solutions are expensive and require a long preparation phase where the field has to be surveyed with a GNSS rover to collect waypoints for the navigation path. An alternative navigation approach can be provided by Local perception sensors, such as LiDAR scanners, by tracking geometric features in the perceived scene. This paper presents a robust LiDAR-based solution for structure tracking along vine rows. The proposed method does not require prior field surveying, and it is insensitive to crop characteristics such as row width and spacing. Moreover, the proposed algorithm identifies and builds an online regression model of the structure. This is done by applying the Hough transform with a parameterization and search method motivated by a practical interpretation of point cloud statistics. The proposed method was tested on a commercial robotic platform in two configurations of vineyards. The experiments show that the proposed algorithm achieves consistent and accurate row tracking, which was validated against a reliable RTK-GNSS ground truth.

Island Mammal Extinctions are Determined by Interactive Effects of Life History, Island Biogeography, Mesopredator Suppression

Island Mammal Extinctions are Determined by Interactive Effects of Life History, Island Biogeography, Mesopredator Suppression

Semi-Automated Ground Truth Segmentation and Phenotyping of Plant Structures Using k-Means Clustering of Eigen-Colors (kmSeg)

Background. Efficient analysis of large image data produced in greenhouse phenotyping experiments is often challenged by a large variability of optical plant and background appearance which requires advanced classification model methods and reliable ground truth data for their training. In the absence of appropriate computational tools, generation of ground truth data has to be performed manually, which represents a time-consuming task. Methods. Here, we present a efficient GUI-based software solution which reduces the task of plant image segmentation to manual annotation of a small number of image regions automatically pre-segmented using k-means clustering of Eigen-colors (kmSeg). Results. Our experimental results show that in contrast to other supervised clustering techniques k-means enables a computationally efficient pre-segmentation of large plant images in their original resolution. Thereby, the binary segmentation of plant images in fore- and background regions is performed within a few minutes with the average accuracy of 96–99% validated by a direct comparison with ground truth data. Conclusions. Primarily developed for efficient ground truth segmentation and phenotyping of greenhouse-grown plants, the kmSeg tool can be applied for efficient labeling and quantitative analysis of arbitrary images exhibiting distinctive differences between colors of fore- and background structures.

Low-Energy Electron Elastic Collisions with Actinide Atoms Am, Cm, Bk, Es, No and Lr: Negative-Ion Formation

The rigorous Regge-pole method is used to investigate negative-ion formation in actinide atoms through electron elastic total cross sections (TCSs) calculation. The TCSs are found to be characterized generally by negative-ion formations, shape resonances and Ramsauer-Townsend(R-T) minima, and they exhibit both atomic and fullerene molecular behavior near the threshold. Additionally, a polarization-induced metastable cross section with a deep R-T minimum is identified near the threshold in the Am, Cm and Bk TCSs, which flips over to a shape resonance appearing very close to the threshold in the TCSs for Es, No and Lr. We attribute these new manifestations to size effects and orbital collapse significantly impacting the polarization interaction. From the TCSs unambiguous and reliable ground, metastable and excited states negative-ion binding energies (BEs) for Am−, Cm−, Bk−, Es−, No− and Lr− anions formed during the collisions are extracted and compared with existing electron affinities (EAs) of the atoms. The novelty of the Regge-pole approach is in the extraction of the negative-ion BEs from the TCSs. We conclude that the existing theoretical EAs of the actinide atoms and the recently measured EA of Th correspond to excited anionic BEs.

Prediction of Cumulative Absolute Velocity Based on Refined Second-order Deep Neural Network

ABSTRACT This study aims to develop a reliable ground motion model (GMM) for CAV by using ground motion (GM) recordings from the PEER NGA-West2 database. A total of 17,684 GM recordings are chosen and randomly separated into the training, validation, and testing datasets. The DNN is advanced by incorporating the refined second-order (RSO) neuron. The effect of seismological and site-specific parameters on the predicted CAV is investigated. The comparative assessment of four existing models with the RSO-DNN model of this study highlights the superior prediction skill of the latter one since the RSO-DNN model is found to be associated with considerably less error.

OSTEOPATHIC CORRECTION IN TREATING PATIENTS WITH TENSION HEADACHE SYMPTOM AGAINST TMJ DYSFUNCTION

Patients may have headache due to pathological issues occurring in the cervical spine and resulting in compression of the vertebral artery passing nearby. This comes along with disturbed blood supply to the brain. The aim of this study is to offer reliable grounds, and evaluate osteopathic correction when treating headache of tension (HAT) caused by a dysfunction of the temporomandibular joint (TMJ). A comprehensive examination involved 26 patients aged 18 to 65. The study outcomes allow recommending to include soft osteopathic techniques in the set of therapeutic measures. In order to make osteopathic treatment available for patients with HAT caused by the TMJ dysfunction, computed tomography (CT) of the TMJ and cranio-cervical junction is a typical choice. Given the high efficiency of the combined work of an osteopath joining effort with an orthopedic dentist and an orthodontist who has a good command of neuromuscular correction approaches, it appears a feasible option to use such combined treatment in patients with the TMJ dysfunction-caused HAT. To improve the quality of osteopathic correction and maintain the positive outcome we recommend using a supporting teeth protector — an elastopositioning corrector or TMJ trainer. This enables to decompress the TMJ.

TRIPOD—A Treadmill Walking Dataset with IMU, Pressure-Distribution and Photoelectric Data for Gait Analysis

Inertial measurement units (IMUs) enable easy to operate and low-cost data recording for gait analysis. When combined with treadmill walking, a large number of steps can be collected in a controlled environment without the need of a dedicated gait analysis laboratory. In order to evaluate existing and novel IMU-based gait analysis algorithms for treadmill walking, a reference dataset that includes IMU data as well as reliable ground truth measurements for multiple participants and walking speeds is needed. This article provides a reference dataset consisting of 15 healthy young adults who walked on a treadmill at three different speeds. Data were acquired using seven IMUs placed on the lower body, two different reference systems (Zebris FDMT-HQ and OptoGait), and two RGB cameras. Additionally, in order to validate an existing IMU-based gait analysis algorithm using the dataset, an adaptable modular data analysis pipeline was built. Our results show agreement between the pressure-sensitive Zebris and the photoelectric OptoGait system (r = 0.99), demonstrating the quality of our reference data. As a use case, the performance of an algorithm originally designed for overground walking was tested on treadmill data using the data pipeline. The accuracy of stride length and stride time estimations was comparable to that reported in other studies with overground data, indicating that the algorithm is equally applicable to treadmill data. The Python source code of the data pipeline is publicly available, and the dataset will be provided by the authors upon request, enabling future evaluations of IMU gait analysis algorithms without the need of recording new data.

Predicting rice yield at pixel scale through synthetic use of crop and deep learning models with satellite data in South and North Korea

Prediction of rice yields at pixel scale rather than county scale can benefit crop management and scientific understanding because it is useful for monitoring how crop yields respond to various agricultural systems and environmental factors. In this study, we propose a methodology for the early prediction of rice yield at pixel scale combining a crop model and a deep learning model for different agricultural systems throughout South and North Korea. Initially, satellite-integrated crop models were applied to obtain a pixel-scale reference rice yield. Then, the pixel-scale reference rice yields were used as target labels in the deep learning model to leverage the advantages of crop models. Models of five different deep learning network architectures were employed to help determine the hybrid structure of long-short term memory (LSTM) and one-dimensional convolutional neural network (1D-CNN) layers by predicting the optimal model about two months ahead of harvest time. The suggested model showed good performance [R2 = 0.859, Nash-Sutcliffe model efficiency = 0.858, root mean squared error = 0.605 Mg ha−1], with specific spatial patterns of rice yields for South and North Korea. Analysis of the relative importance of the input variables showed the water-related index and maximum temperature in North Korea and the vegetation indices and geographic variables in South Korea to be crucial for predicting rice yields. The proposed approach successfully predicted and diagnosed rice yield at the pixel scale for inaccessible locations where reliable ground measurements are not available, especially North Korea.

A Pressure-Pad-Embedded Treadmill Yields Time-Dependent Errors in Estimating Ground Reaction Force during Walking.

Accurate and reliable vertical ground reaction force (VGRF) measurement is essential in various biomechanical and clinical studies. Recently, pressure–pad-embedded treadmills have been widely used for VGRF measurement as a relatively less expensive option than the force platform-mounted treadmills. Prior studies have shown that the popular Zebris treadmill is reliable when used to measure peak VGRF for short walking sessions. However, comprehensive evaluation of human walking requires information of gait parameters over sufficient gait cycles. In this study, we quantify the long-term temporal changes in VGRF values measured by the Zebris treadmill. Twenty participants walked on the treadmill for 10 min twice, with 10 min rest between trials. We found an evident decline in the measured VGRF and impulse over time for both trials. The Zebris system also consistently yielded the lower VGRF values during the second trials. These results indicate that the Zebris treadmill is unreliable in measuring VGRF during walking, and a 10 min break is not enough for the embedded sensors to recover their sensitivity. We provided a way to resolve these time-dependent errors; using the impulse-momentum theorem and collected kinematics of the participants, we formulated a curve-fitting model encapsulating the growing VGRF estimation error.

A rapid review of influential factors and appraised solutions on organ delineation uncertainties reduction in radiotherapy

Background and purpose. Accurate volume delineation plays an essential role in radiotherapy. Contouring is a potential source of uncertainties in radiotherapy treatment planning that could affect treatment outcomes. Therefore, reducing the degree of contouring uncertainties is crucial. The role of utilized imaging modality in the organ delineation uncertainties has been investigated. This systematic review explores the influential factors on inter-and intra-observer uncertainties of target volume and organs at risk (OARs) delineation focusing on the used imaging modality for these uncertainties reduction and the reported subsequent histopathology and follow-up assessment. Methods and materials. An inclusive search strategy has been conducted to query the available online databases (Scopus, Google Scholar, PubMed, and Medline). ‘Organ at risk’, ‘target’, ‘delineation’, ‘uncertainties’, ‘radiotherapy’ and their relevant terms were utilized using every database searching syntax. Final article extraction was performed following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline. Included studies were limited to the ones published in English between 1995 and 2020 and that just deal with computed tomography (CT) and magnetic resonance imaging (MRI) modalities. Results. A total of 923 studies were screened and 78 were included of which 31 related to the prostate 20 to the breast, 18 to the head and neck, and 9 to the brain tumor site. 98% of the extracted studies performed volumetric analysis. Only 24% of the publications reported the dose deviations resulted from variation in volume delineation Also, heterogeneity in studied populations and reported geometric and volumetric parameters were identified such that quantitative synthesis was not appropriate. Conclusion. This review highlightes the inter- and intra-observer variations that could lead to contouring uncertainties and impede tumor control in radiotherapy. For improving volume delineation and reducing inter-observer variability, the implementation of well structured training programs, homogeneity in following consensus and guidelines, reliable ground truth selection, and proper imaging modality utilization could be clinically beneficial.

Registered report protocol: Developing an artifact index for capacitive electrocardiography signals acquired with an armchair.

Continuous monitoring of an electrocardiogram (ECG) in private diagnostic spaces such as vehicles or apartments allows early detection of cardiovascular diseases. We will use an armchair with integrated capacitive electrodes to record the capacitive electrocardiogram (cECG) during everyday activities. However, movements and other artifacts affect the signal quality. Therefore, an artifact index is needed to detect artifacts and classify the cECG. The unavailability of cECG data and reliable ground truth information requires new recordings to develop an artifact index. This study is designed to test the hypothesis: an artifact index can be devised, which intends to estimate the signal quality of segments and classify signals. In a single-arm study with 44 subjects, we will record two activities of 11-minute duration: reading and watching television. During recording, we will capture cECG, ECG, and oxygen saturation (SpO2) with time synchronization as well as keypoint-based movement indicators obtained from a video camera. SpO2 provides additional information on the subject’s health status. The keypoint-based movements indicate artifacts in the cECG. We will combine all ground truth data to evaluate the index. In the future, we aim at using the artifact index to exclude cECG segments with artifacts from further analysis. This will improve cECG technology for the measurement of cardiovascular parameters.

An efficient encoder–decoder model for portrait depth estimation from single images trained on pixel-accurate synthetic data

Depth estimation from a single image frame is a fundamental challenge in computer vision, with many applications such as augmented reality, action recognition, image understanding, and autonomous driving. Large and diverse training sets are required for accurate depth estimation from a single image frame. Due to challenges in obtaining dense ground-truth depth, a new 3D pipeline of 100 synthetic virtual human models is presented to generate multiple 2D facial images and corresponding ground truth depth data, allowing complete control over image variations. To validate the synthetic facial depth data, we propose an evaluation of state-of-the-art depth estimation algorithms based on single image frames on the generated synthetic dataset. Furthermore, an improved encoder–decoder based neural network is presented. This network is computationally efficient and shows better performance than current state-of-the-art when tested and evaluated across 4 public datasets. Our training methodology relies on the use of synthetic data samples which provides a more reliable ground truth for depth estimation. Additionally, using a combination of appropriate loss functions leads to improved performance than the current state-of-the-art network performances. Our approach clearly outperforms competing methods across different test datasets, setting a new state-of-the-art for facial depth estimation from synthetic data.

Tafsirlarda Iskandar mavzusiga oid talqinlar

The teachings of Islam had a significant impact on the further development of the theme of Alexander, the development in the East. This great influence rests on three important foundations – the Qur'an, hadiths and interpretations. The tradition of writing works on the theme of Alexander was enriched with new subject lines, images, episodes and motives under their influence. It has improved in content and acquired original interpretations. In particular, the Qur'anic surah "Kahf" made a turning point in the evolution of the theme and tradition in the East. Commentaries written on this surah are distinguished by their own character. In them, the details of the story of Zulqarnayn were added with views and interpretations related to the story of Alexander. That is, in the interpretations, the theme of Alexander was received, two traditions were mixed.This article examines the same aspect of interpretations and draws conclusions based on the works of famous commentators of the East. It is stated that the traditions of Alexander and Zulqarnay were erroneously added, and such a view does not have any reliable grounds.

Utilizing a responsive web portal for studying disc tracing agreement in retinal images.

Glaucoma is a leading cause of blindness worldwide whose detection is based on multiple factors, including measuring the cup to disc ratio, retinal nerve fiber layer and visual field defects. Advances in image processing and machine learning have allowed the development of automated approached for segmenting objects from fundus images. However, to build a robust system, a reliable ground truth dataset is required for proper training and validation of the model. In this study, we investigate the level of agreement in properly detecting the retinal disc in fundus images using an online portal built for such purposes. Two Doctors of Optometry independently traced the discs for 159 fundus images obtained from publicly available datasets using a purpose-built online portal. Additionally, we studied the effectiveness of ellipse fitting in handling misalignments in tracing. We measured tracing precision, interobserver variability, and average boundary distance between the results provided by ophthalmologists, and optometrist tracing. We also studied whether ellipse fitting has a positive or negative impact on properly detecting disc boundaries. The overall agreement between the optometrists in terms of locating the disc region in these images was 0.87. However, we found that there was a fair agreement on the disc border with kappa = 0.21. Disagreements were mainly in fundus images obtained from glaucomatous patients. The resulting dataset was deemed to be an acceptable ground truth dataset for training a validation of models for automatic detection of objects in fundus images.

Hopes and Hypes for Artificial Intelligence in Colorectal Cancer Screening

Hopes and Hypes for Artificial Intelligence in Colorectal Cancer Screening

Interpreting Deep Learning Studies in Glaucoma: Unresolved Challenges.

Deep learning algorithms as tools for automated image classification have recently experienced rapid growth in imaging-dependent medical specialties, including ophthalmology. However, only a few algorithms tailored to specific health conditions have been able to achieve regulatory approval for autonomous diagnosis. There is now an international effort to establish optimized thresholds for algorithm performance benchmarking in a rapidly evolving artificial intelligence field. This review examines the largest deep learning studies in glaucoma, with special focus on identifying recurrent challenges and limitations within these studies which preclude widespread clinical deployment. We focus on the 3 most common input modalities when diagnosing glaucoma, namely, fundus photographs, spectral domain optical coherence tomography scans, and standard automated perimetry data. We then analyze 3 major challenges present in all studies: defining the algorithm output of glaucoma, determining reliable ground truth datasets, and compiling representative training datasets.

A flexible ChIP-sequencing simulation toolkit

BackgroundA major challenge in evaluating quantitative ChIP-seq analyses, such as peak calling and differential binding, is a lack of reliable ground truth data. Accurate simulation of ChIP-seq data can mitigate this challenge, but existing frameworks are either too cumbersome to apply genome-wide or unable to model a number of important experimental conditions in ChIP-seq.ResultsWe present ChIPs, a toolkit for rapidly simulating ChIP-seq data using statistical models of key experimental steps. We demonstrate how ChIPs can be used for a range of applications, including benchmarking analysis tools and evaluating the impact of various experimental parameters. ChIPs is implemented as a standalone command-line program written in C++ and is available from https://github.com/gymreklab/chips.ConclusionsChIPs is an efficient ChIP-seq simulation framework that generates realistic datasets over a flexible range of experimental conditions. It can serve as an important component in various ChIP-seq analyses where ground truth data are needed.

Bio-inspired optimization of weighted-feature machine learning for strength property prediction of fiber-reinforced soil

The fiber-reinforcement of soil is an effective and reliable ground improvement technique for increasing the strength and stability of soil for various purposes (including retaining structures, embankments, foundations, slopes and pavements). Numerous scholars have developed methods to identify factors that influence the shear strength and to predict the peak friction angle of fiber-reinforced soil (FRS). The accuracy of theoretical and empirical models for predicting the shear strength (peak friction angle) of FRS is questionable because of the difficulty of using these simplified models to describe the complex mechanism of soil-fiber interaction. Solutions to this problem require ever-increasing predictive accuracy, and ML-based methods have been confirmed to provide potential solutions to real-world engineering problems. Therefore, this study develops weighted-feature least squares support vector regression (WFLSSVR) that is optimized by a novel metaheuristic algorithm, jellyfish search (JS) algorithm, to predict the peak friction angle of FRS. Analytical results demonstrate that JS-WFLSSVR outperforms baseline, ensemble, and hybrid machine learning models as well as empirical methods in literature. Notably, analysis of the weight values that were obtained by JS-WFLSSVR enables the identification of new feature combinations that provide much higher accuracy than current models. Therefore, the JS-WFLSSVR model not only significantly provides better predictive accuracy than methods in the literature; it is also a good feature selection method, and can help geotechnical engineers in estimating the shear strength of FRS. Geotechnical engineers can use the proposed model to predict the shear strength and control the quality of FRS structures.