Significant effect of image contrast enhancement on weld defect detection.

Weld defect inspection is an essential aspect of testing in industries field. From a human viewpoint, a manual inspection can make appropriate justification more difficult and lead to incorrect identification during weld defect detection. Weld defect inspection uses X-radiography testing, which is now mostly outdated. Recently, numerous researchers have utilized X-radiography digital images to inspect the defect. As a result, for error-free inspection, an autonomous weld detection and classification system are required. One of the most difficult issues in the field of image processing, particularly for enhancing image quality, is the issue of contrast variation and luminosity. Enhancement is carried out by adjusting the brightness of the dark or bright intensity to boost segmentation performance and image quality. To equalize contrast variation and luminosity, many different approaches have recently been put forth. In this research, a novel approach called Hybrid Statistical Enhancement (HSE), which is based on a direct strategy using statistical data, is proposed. The HSE method divided each pixel into three groups, the foreground, border, and problematic region, using the mean and standard deviation of a global and local neighborhood (luminosity and contrast). To illustrate the impact of the HSE method on the segmentation or detection stage, the datasets, specifically the weld defect image, were used. Bernsen and Otsu's methods are the two segmentation techniques utilized. The findings from the objective and visual elements demonstrated that the HSE approach might automatically improve segmentation output while effectively enhancing contrast variation and normalizing luminosity. In comparison to the Homomorphic Filter (HF) and Difference of Gaussian (DoG) approaches, the segmentation results for HSE images had the lowest result according to Misclassification Error (ME). After being applied to the HSE images during the segmentation stage, every quantitative result showed an increase. For example, accuracy increased from 64.171 to 84.964. In summary, the application of the HSE method has resulted in an effective and efficient outcome for background correction as well as improving the quality of images.

Cervical Cancer Detection Techniques: A Chronological Review.

Cervical cancer is known as a major health problem globally, with high mortality as well as incidence rates. Over the years, there have been significant advancements in cervical cancer detection techniques, leading to improved accuracy, sensitivity, and specificity. This article provides a chronological review of cervical cancer detection techniques, from the traditional Pap smear test to the latest computer-aided detection (CAD) systems. The traditional method for cervical cancer screening is the Pap smear test. It consists of examining cervical cells under a microscope for abnormalities. However, this method is subjective and may miss precancerous lesions, leading to false negatives and a delayed diagnosis. Therefore, a growing interest has been in shown developing CAD methods to enhance cervical cancer screening. However, the effectiveness and reliability of CAD systems are still being evaluated. A systematic review of the literature was performed using the Scopus database to identify relevant studies on cervical cancer detection techniques published between 1996 and 2022. The search terms used included "(cervix OR cervical) AND (cancer OR tumor) AND (detect* OR diagnosis)". Studies were included if they reported on the development or evaluation of cervical cancer detection techniques, including traditional methods and CAD systems. The results of the review showed that CAD technology for cervical cancer detection has come a long way since it was introduced in the 1990s. Early CAD systems utilized image processing and pattern recognition techniques to analyze digital images of cervical cells, with limited success due to low sensitivity and specificity. In the early 2000s, machine learning (ML) algorithms were introduced to the CAD field for cervical cancer detection, allowing for more accurate and automated analysis of digital images of cervical cells. ML-based CAD systems have shown promise in several studies, with improved sensitivity and specificity reported compared to traditional screening methods. In summary, this chronological review of cervical cancer detection techniques highlights the significant advancements made in this field over the past few decades. ML-based CAD systems have shown promise for improving the accuracy and sensitivity of cervical cancer detection. The Hybrid Intelligent System for Cervical Cancer Diagnosis (HISCCD) and the Automated Cervical Screening System (ACSS) are two of the most promising CAD systems. Still, deeper validation and research are required before being broadly accepted. Continued innovation and collaboration in this field may help enhance cervical cancer detection as well as ultimately reduce the disease's burden on women worldwide.

Automated Detection of Corneal Ulcer Using Combination Image Processing and Deep Learning.

A corneal ulcers are one of the most common eye diseases. They come from various infections, such as bacteria, viruses, or parasites. They may lead to ocular morbidity and visual disability. Therefore, early detection can reduce the probability of reaching the visually impaired. One of the most common techniques exploited for corneal ulcer screening is slit-lamp images. This paper proposes two highly accurate automated systems to localize the corneal ulcer region. The designed approaches are image processing techniques with Hough transform and deep learning approaches. The two methods are validated and tested on the publicly available SUSTech-SYSU database. The accuracy is evaluated and compared between both systems. Both systems achieve an accuracy of more than 90%. However, the deep learning approach is more accurate than the traditional image processing techniques. It reaches 98.9% accuracy and Dice similarity 99.3%. However, the first method does not require parameters to optimize an explicit training model. The two approaches can perform well in the medical field. Moreover, the first model has more leverage than the deep learning model because the last one needs a large training dataset to build reliable software in clinics. Both proposed methods help physicians in corneal ulcer level assessment and improve treatment efficiency.

A lightweight hybrid deep learning system for cardiac valvular disease classification.

Cardiovascular diseases (CVDs) are a prominent cause of death globally. The introduction of medical big data and Artificial Intelligence (AI) technology encouraged the effort to develop and deploy deep learning models for distinguishing heart sound abnormalities. These systems employ phonocardiogram (PCG) signals because of their lack of sophistication and cost-effectiveness. Automated and early diagnosis of cardiovascular diseases (CVDs) helps alleviate deadly complications. In this research, a cardiac diagnostic system that combined CNN and LSTM components was developed, it uses phonocardiogram (PCG) signals, and utilizes either augmented or non-augmented datasets. The proposed model discriminates five heart valvular conditions, namely normal, Aortic Stenosis (AS), Mitral Regurgitation (MR), Mitral Stenosis (MS), and Mitral Valve Prolapse (MVP). The findings demonstrate that the suggested end-to-end architecture yields outstanding performance concerning all important evaluation metrics. For the five classes problem using the open heart sound dataset, accuracy was 98.5%, F1-score was 98.501%, and Area Under the Curve (AUC) was 0.9978 for the non-augmented dataset and accuracy was 99.87%, F1-score was 99.87%, and AUC was 0.9985 for the augmented dataset. Model performance was further evaluated using the PhysioNet/Computing in Cardiology 2016 challenge dataset, for the two classes problem, accuracy was 93.76%, F1-score was 85.59%, and AUC was 0.9505. The achieved results show that the proposed system outperforms all previous works that use the same audio signal databases. In the future, the findings will help build a multimodal structure that uses both PCG and ECG signals.

Intelligent Diagnosis and Classification of Keratitis.

A corneal ulcer is an open sore that forms on the cornea; it is usually caused by an infection or injury and can result in ocular morbidity. Early detection and discrimination between different ulcer diseases reduces the chances of visual disability. Traditional clinical methods that use slit-lamp images can be tiresome, expensive, and time-consuming. Instead, this paper proposes a deep learning approach to diagnose corneal ulcers, enabling better, improved treatment. This paper suggests two modes to classify corneal images using manual and automatic deep learning feature extraction. Different dimensionality reduction techniques are utilized to uncover the most significant features that give the best results. Experimental results show that manual and automatic feature extraction techniques succeeded in discriminating ulcers from a general grading perspective, with ~93% accuracy using the 30 most significant features extracted using various dimensionality reduction techniques. On the other hand, automatic deep learning feature extraction discriminated severity grading with a higher accuracy than type grading regardless of the number of features used. To the best of our knowledge, this is the first report to ever attempt to distinguish corneal ulcers based on their grade grading, type grading, ulcer shape, and distribution. Identifying corneal ulcers at an early stage is a preventive measure that reduces aggravation and helps track the efficacy of adapted medical treatment, improving the general public health in remote, underserved areas.

Waning efficacy in a long-term AAV-mediated gene therapy study in the murine model of Krabbe disease.

Neonatal AAV9-gene therapy of the lysosomal enzyme galactosylceramidase (GALC) significantly ameliorates central and peripheral neuropathology, prolongs survival, and largely normalizes motor deficits in Twitcher mice. Despite these therapeutic milestones, new observations identified the presence of multiple small focal demyelinating areas in the brain after 6-8 months. These lesions are in stark contrast to the diffuse, global demyelination that affects the brain of naive Twitcher mice. Late-onset lesions exhibited lysosomal alterations with reduced expression of GALC and increased psychosine levels. Furthermore, we found that lesions were closely associated with the extravasation of plasma fibrinogen and activation of the fibrinogen-BMP-SMAD-GFAP gliotic response. Extravasation of fibrinogen correlated with tight junction disruptions of the vasculature within the lesioned areas. The lesions were surrounded by normal appearing white matter. Our study shows that the dysregulation of therapeutic GALC was likely driven by the exhaustion of therapeutic AAV episomal DNA within the lesions, paralleling the presence of proliferating oligodendrocyte progenitors and glia. We believe that this is the first demonstration of diminishing expression in vivo from an AAV gene therapy vector with detrimental effects in the brain of a lysosomal storage disease animal model. The development of this phenotype linking localized loss of GALC activity with relapsing neuropathology in the adult brain of neonatally AAV-gene therapy-treated Twitcher mice identifies and alerts to possible late-onset reductions of AAV efficacy, with implications to other genetic leukodystrophies.

AAV-Mediated GALC Gene Therapy Rescues Alpha-Synucleinopathy in the Spinal Cord of a Leukodystrophic Lysosomal Storage Disease Mouse Model.

Krabbe's disease (KD) is primarily a demyelinating disorder, but recent studies have identified the presence of neuronal protein aggregates in the brain, at least partially composed by alpha-synuclein (α-syn). The role of this protein aggregation in the pathogenesis of KD is largely unknown, but it has added KD to a growing list of lysosomal storage diseases that can be also be considered as proteinopathies. While the presence of these protein aggregates within the KD brain is now appreciated, the remainder of the central nervous system (CNS) remains uncharacterized. This study is the first to report the presence of thioflavin-S reactive inclusions throughout the spinal cord of both murine and human spinal tissue. Stereological analysis revealed the temporal and spatial accumulation of these inclusions within the neurons of the ventral spinal cord vs. those located in the dorsal cord. This study also confirmed that these thio-S positive accumulations are present within neuronal populations and are made up at least in part by α-syn in both the twitcher mouse and cord autopsied material from affected human patients. Significantly, neonatal gene therapy for galactosylceramidase, a treatment that strongly improves the survival and health of KD mice, but not bone marrow transplantation prevents the formation of these inclusions in spinal neurons. These results expand the understanding of α-syn protein aggregation within the CNS of individuals afflicted with KD and underlines the tractability of this problem via early gene therapy, with potential impact to other synucleinopathies such as PD.

eHealth Cloud Security Challenges: A Survey.

Cloud computing is a promising technology that is expected to transform the healthcare industry. Cloud computing has many benefits like flexibility, cost and energy savings, resource sharing, and fast deployment. In this paper, we study the use of cloud computing in the healthcare industry and different cloud security and privacy challenges. The centralization of data on the cloud raises many security and privacy concerns for individuals and healthcare providers. This centralization of data (1) provides attackers with one-stop honey-pot to steal data and intercept data in-motion and (2) moves data ownership to the cloud service providers; therefore, the individuals and healthcare providers lose control over sensitive data. As a result, security, privacy, efficiency, and scalability concerns are hindering the wide adoption of the cloud technology. In this work, we found that the state-of-the art solutions address only a subset of those concerns. Thus, there is an immediate need for a holistic solution that balances all the contradicting requirements.

α-Synuclein interacts directly but reversibly with psychosine: implications for α-synucleinopathies.

Aggregation of α-synuclein, the hallmark of α-synucleinopathies such as Parkinson's disease, occurs in various glycosphingolipidoses. Although α-synuclein aggregation correlates with deficiencies in the lysosomal degradation of glycosphingolipids (GSL), the mechanism(s) involved in this aggregation remains unclear. We previously described the aggregation of α-synuclein in Krabbe's disease (KD), a neurodegenerative glycosphingolipidosis caused by lysosomal deficiency of galactosyl-ceramidase (GALC) and the accumulation of the GSL psychosine. Here, we used a multi-pronged approach including genetic, biophysical and biochemical techniques to determine the pathogenic contribution, reversibility, and molecular mechanism of aggregation of α-synuclein in KD. While genetic knock-out of α-synuclein reduces, but does not completely prevent, neurological signs in a mouse model of KD, genetic correction of GALC deficiency completely prevents α-synuclein aggregation. We show that psychosine forms hydrophilic clusters and binds the C-terminus of α-synuclein through its amino group and sugar moiety, suggesting that psychosine promotes an open/aggregation-prone conformation of α-synuclein. Dopamine and carbidopa reverse the structural changes of psychosine by mediating a closed/aggregation-resistant conformation of α-synuclein. Our results underscore the therapeutic potential of lysosomal correction and small molecules to reduce neuronal burden in α-synucleinopathies, and provide a mechanistic understanding of α-synuclein aggregation in glycosphingolipidoses.

Long-Term Improvement of Neurological Signs and Metabolic Dysfunction in a Mouse Model of Krabbe's Disease after Global Gene Therapy.

We report a global adeno-associated virus (AAV)9-based gene therapy protocol to deliver therapeutic galactosylceramidase (GALC), a lysosomal enzyme that is deficient in Krabbe's disease. When globally administered via intrathecal, intracranial, and intravenous injections to newborn mice affected with GALC deficiency (twitcher mice), this approach largely surpassed prior published benchmarks of survival and metabolic correction, showing long-term protection of demyelination, neuroinflammation, and motor function. Bone marrow transplantation, performed in this protocol without immunosuppressive preconditioning, added minimal benefits to the AAV9 gene therapy. Contrasting with other proposed pre-clinical therapies, these results demonstrate that achieving nearly complete correction of GALC's metabolic deficiencies across the entire nervous system via gene therapy can have a significant improvement to behavioral deficits, pathophysiological changes, and survival. These results are an important consideration for determining the safest and most effective manner for adapting gene therapy to treat this leukodystrophy in the clinic.