Covid‐19 is an acute respiratory infection and presents various clinical features ranging from no symptoms to severe pneumonia and death. Medical expert systems, especially in diagnosis and ...monitoring stages, can give positive consequences in the struggle against Covid‐19. In this study, a rule‐based expert system is designed as a predictive tool in self‐pre‐diagnosis of Covid‐19. The potential users are smartphone users, healthcare experts and government health authorities. The system does not only share the data gathered from the users with experts, but also analyzes the symptom data as a diagnostic assistant to predict possible Covid‐19 risk. To do this, a user needs to fill out a patient examination card that conducts an online Covid‐19 diagnostic test, to receive an unconfirmed online test prediction result and a set of precautionary and supportive action suggestions. The system was tested for 169 positive cases. The results produced by the system were compared with the real PCR test results for the same cases. For patients with certain symptomatic findings, there was no significant difference found between the results of the system and the confirmed test results with PCR test. Furthermore, a set of suitable suggestions produced by the system were compared with the written suggestions of a collaborated health expert. The suggestions deduced and the written suggestions of the health expert were similar and the system suggestions in line with suggestions of the expert. The system can be suitable for diagnosing and monitoring of positive cases in the areas other than clinics and hospitals during the Covid‐19 pandemic. The results of the case studies are promising, and it demonstrates the applicability, effectiveness, and efficiency of the proposed approach in all communities.
Evaluation of Premature Ejaculation Jannini, Emmanuele A.; Maggi, Mario; Lenzi, Andrea
Journal of sexual medicine,
October 2011, Volume:
8, Issue:
s4
Journal Article
Peer reviewed
Premature ejaculation (PE) is a prevalent, yet often underdiagnosed, sexual disorder that affects men of all ages. Identification of PE is hampered by stigma and embarrassment associated with the ...condition, and limited awareness that it is treatable. Because diagnosis informs treatment decisions that have an impact on clinical outcomes, the ability to diagnose PE accurately is vital to the successful management of this condition.
Provide an overview of how to evaluate and diagnose PE.
Review of the literature.
The taxonomy of PE based on onset, time, type, and comorbidities.
Diagnosis of PE encompasses seven key steps: (i) Obtaining the patient's general medical and sexual history; (ii) Classifying the symptom on the basis of onset (e.g., lifelong or acquired PE), timing (e.g., prior to or during intercourse), and type (e.g., absolute/generalized or relative/situational); (iii) Involving the partner to determine their view of the situation and the impact of PE on the couple as a whole; (iv) Identifying sexual comorbidities (e.g., erectile dysfunction) to define whether PE is simple (occurring in the absence of other sexual dysfunctions) or complicated (occurring in the presence of other sexual dysfunctions); (v) Performing physical examination to check the man's sexual organs and reflexes; (vi) Identifying underlying etiologies and risk factors (e.g., endocrine-, urological-, or psychorelational-/psychosexual-related) to determine the primary cause of PE and any associated comorbidities; (vii) Discussing treatment options to find the most suitable intervention, according to the needs of the man and his partner.
A greater understanding of how to diagnose PE correctly, and a more widespread use of a structured diagnostic approach, could lead to better treatment outcomes in the future. Jannini EA, Maggi M, and Lenzi A. Evaluation of premature ejaculation.
Anterior shoulder dislocation (ASD) is a frequently observed musculoskeletal injury that is often encountered in the context of sports activities or as a result of trauma. Several magnetic resonance ...imaging (MRI) parameters have been previously investigated for the purpose of characterizing the anatomical features, which could potentially be responsible for the episodes of instability. These measurements have the potential to identify patients who are susceptible to dislocation. Consequently, ensuring the reliability and consistency of these measurements is crucial in the diagnosis and the management of athletic or traumatic shoulder injuries.
A group of four students, who had no previous experience in reading MRI series, were selected to perform radiographic measurements on specific parameters of MRI scans. These parameters were glenoid version, glenoid depth, glenoid width, humeral head diameter, humeral containing angle, and the ratio of humeral head diameter to glenoid diameter. The four participants conducted two distinct readings on a total of 28 sets of shoulder MRI scans. Simultaneously, the aforementioned measures were assessed by a consultant shoulder surgeon.
A total of 1512 measurements were categorized into nine sets: eight from students’ measurements (two per student) and one from the consultant. Intra-rater reliability assessed by the intra-class correlation (ICC) test indicated excellent or good reliability for all parameters (p < 0.05), with glenoid depth showing the highest (0.925) and humeral-containing angles the lowest (0.675) ICC value. Inter-rater correlation, also evaluated using ICC, demonstrated strong correlation (p < 0.05), with glenoid diameter having the highest ICC score (0.935) and glenoid depth the lowest (0.849). Agreement analysis, expressed by Cohen's Kappa test, revealed substantial agreement (p < 0.05) for all parameters, with humeral head diameter having the highest agreement (0.90) and humeral-containing angle the lowest (0.73).
In this study, intra- and inter-rater MRI parameters are substantially concordant. Credibility comes from these reliability and agreement analyses' statistical significance. Glenoid diameter and depth are the most reliable intrarater and interrater, respectively. Best agreement was with the humeral-containing angle. These data demonstrate repeatability and clinical relevance.
Level IV
The increasing spread of monkeypox recently requires more study, investigation, and analysis. Not only to understand the different factors that contribute to the spread and transmission of the ...disease, but also to devise rules by which the disease can be diagnosed more accurately as the clinical presentation of monkeypox is very similar to that of smallpox. This paper presents a simple but effective strategy for diagnosing monkeypox, which is called Accurate Monkeypox Diagnosing Strategy (AMDS). The proposed AMDS consists of two successive phases, which are; (i) Feature Selection Phase (FSP) and (ii) Classification Phase (CP). During the FSP, the most beneficial features for diagnosing monkeypox are selected; afterwards the CP is applied during which the actual diagnosis takes place. The main contribution of AMDS relies on a proposed improvement of the Traditional Gray Wolf Optimization (TGWO), which is identified as Dynamic Recursive Gray Wolf Optimization (DRGW) and is applied in the FSP to select the most influential features. DRGW solves two of the most effective drawbacks of TGWO, which are; (i) inability to share good positions among wolves’ pack and (ii) the lack of an accurate mechanism to locate the potential prey. AMDS has been experimentally evaluated compared to other competitors of recent feature selection techniques, in which CP has been employed using different well-known classifiers such as; Support Vector Machines (SVM), Naïve Bayes (NB), K-Nearest Neighbors (KNN), and Deep Neural Network (DNN). Experimental results conclude that AMDS promotes the diagnosing performance in terms of recall, accuracy, error, precision, and F score. This ensures that the proposed AMDS can operate efficiently in the disease diagnosis, which in turn ensures the effectiveness of the proposed DRGW.
Traffic light detection is crucial for environment perception and decision-making in autonomous driving. State-of-the-art detectors are built upon deep Convolutional Neural Networks (CNNs) and have ...exhibited promising performance. However, one looming concern with CNN based detectors is how to thoroughly evaluate the performance of accuracy and robustness before they can be deployed to autonomous vehicles. In this work, we propose a visual analytics system, VATLD, equipped with a disentangled representation learning and semantic adversarial learning, to assess, understand, and improve the accuracy and robustness of traffic light detectors in autonomous driving applications. The disentangled representation learning extracts data semantics to augment human cognition with human-friendly visual summarization, and the semantic adversarial learning efficiently exposes interpretable robustness risks and enables minimal human interaction for actionable insights. We also demonstrate the effectiveness of various performance improvement strategies derived from actionable insights with our visual analytics system, VATLD, and illustrate some practical implications for safety-critical applications in autonomous driving.
In the field of Artificial Intelligence (AI) and Medicine, chest X-ray images are crucial for diagnosing various diseases. However, training AI models presents challenges, particularly with limited ...data or cases involving significant pathology or minor anomalies. To address the constraint of limited data, data augmentation has emerged as a popular technique in medical imaging. One promising approach to augmenting chest X-ray images involves leveraging text-to-image generation, which transforms textual disease descriptions into synthetic images. This technique effectively rectifies class imbalances and enhances the accuracy and reliability of AI models used in medical imaging applications. This study introduces a text-to-image generation architecture based on DF-GAN to augment chest X-ray images. The study aims to assess the impact of augmented data on the performance of two AI models, namely VGG16 and ResNet50, in a classification task. The experimentation is conducted on two challenging datasets, namely Chest X-rays from Indiana University and NIH Chest X-rays. The findings reveal that integrating text-to-image generated data enhances sensitivity by 2.1%, specificity by 1.9%, and AUC by 1.4%, while also mitigating overfitting during training across both datasets. These results underscore the potential of text-to-image generation in bolstering the accuracy and robustness of AI models employed in medical imaging tasks.
•Proposed text-to-image generation using DF-GAN to augment chest X-ray images.•Demonstrated impact of augmented data on VGG16 and ResNet50 in classifying X-rays.•Evaluated technique on challenging datasets: Chest X-rays (IU) and NIH X-rays.•Augmented data improved sensitivity by 2.1% and specificity by 1.9% across datasets.•Text-to-image generation mitigated overfitting and enhanced accuracy in medical imaging.
When sapphire crystal is prepared with Kyropoulos method, the necking-down growth process is a key stage. Sapphire growth defect is a big problem in this stage. However, diagnosing growth defects is ...subject to the interference of workers subjectivity and accuracy always goes down. To address the problem, a novel defect diagnosis method is proposed for necking-down growth process in this paper. Industrial CCD sensors replace eyes of skilled workers to observe in this method. A new Defect-Diagnosing Siamese network (DDSN) is used in this method. We use Siamese architecture to learn similarity through pairs of images. We use the deep separable convolution (DSC) into the DDSN to optimize running speed and model size. In experiment, dataset is acquired by industrial CCD sensors in the necking-down growth process. The accuracy of defect diagnosis can reach up to 94.5%. The method significantly improves the traditional way.
The ability to accurately anticipate heart failure risks in a timely manner is essential because heart failure has been identified as one of the leading causes of death. In this paper, we propose a ...novel method for identifying cardiovascular heart disease by utilizing a K-means clustering and Random Forest classifier combination. Based on their clinical and demographic traits, patients were classified into either healthy or diseased groups using the Random Forest classifier after being clustered using the K-means method. The performance of the proposed hybrid approach was evaluated using a dataset of patient records and compared with traditional diagnostic methods, namely support vector machine (SVM), logistic regression, and Naive Bayes classifiers. The outcomes indicated that the proposed hybrid method attained a high accuracy in diagnosing heart disease, with an overall accuracy of 96.8%. Additionally, the method showed a good performance in classifying patients at high risk of heart disease: the sensitivity reached 96.3% and the specificity reached 97.2%. In conclusion, the proposed method of combining K-means clustering and a Random Forest classifier is a promising approach for the accurate and efficient identification of heart disease. Further studies are needed to validate the proposed method in larger and more diverse patient populations.
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