Objectives
The aim of this research is to add to the current understanding of the latent factor structure of personality disorders by performing a review of the existing literature (Study 1) and a ...factor analytical study on the factor structure and the relationship between self‐reported Axis I and Axis II psychopathology (Study 2).
Design
The current research (Study 2) is cross‐sectional and multicenter.
Results
We found support for the assumption that the borderline personality disorder is a multidimensional construct. Second, we found evidence for a single‐factor structure of the narcissistic, dependent as well as the avoidant personality disorder. Third, we found support for the current Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM‐IV) distinction between Axis I and Axis II, Axis I psychopathology being explained by the factor neuroticism and Axis II disorders to be further subdivided into the higher order factors of internalizing and externalizing pathology.
Conclusions
An adaptation to the current DSM‐IV borderline personality criteria should be made, while various findings show that the borderline construct is multidimensional. Second, deletion of the dependent and narcissistic personality in the DSM‐V might be unjust. Third, Axis I psychopathology can be explained by the factor neuroticism, and Axis II disorders should be further subdivided into the higher order factors of internalizing and externalizing pathology.
With increasing life expectancy and improved preventive measures, teeth are retained longer, leading to a rise in prevalence of root caries lesions (RCL). However, little is known about how dentists ...manage this condition. The present survey aimed to evaluate the knowledge of Swiss dentists on decision making and management of RCL.
The survey evaluated dentists' knowledge, clinical routines, and demographics concerning RCL. Dentists were contacted via email and local newsletters, and 383 dentists from 25 (out of 26) cantons responded. Mann-Whitney U test, χ2 test, intraclass correlation coefficients, Spearman correlation and Chi Square were used.
The dentists had a mean(SD) working experience of 22.5(12) years. Most dentists correctly classified an inactive (67%) and an active (81%)RCL. Although the inactive lesion did not call for restorative treatments, 61% of the dentist declared they would restore it. From the active lesion,83% would restore it. The invasive treatments leaned toward complete caries excavation with composite resin as preferred restorative material. There were significant correlations between material choice and expected success rates. Among the non-invasive options, oral hygiene instructions and (highly-)fluoridated toothpaste were favored. Most dentists declared having a recall system for such patients, with biannual follow-ups preferred. The dentists’ place of education significantly influenced restorative decisions (p < 0.001), while participants’ age (≥60years) impacted activity status (p = 0.048) and restorative decisions (p = 0.02).
Material preferences for non-invasive or invasive management varied greatly and there were minimal differences in the management of inactive or an active RCL. Moreover, diagnosing active lesions appeared easier than diagnosing inactive ones.
Despite diverse material preferences for (non-)invasive treatments, a strong positive correlation existed between the chosen restorative material and its expected 2-year success rate. Moreover, diagnosing active lesions appeared easier than diagnosing inactive ones. The outcome emphasis the need to align guideline recommendations with their application in private dental practices.
The outbreak of novel corona virus 2019 (COVID-19) has been treated as a public health crisis of global concern by the World Health Organization (WHO). COVID-19 pandemic hugely affected countries ...worldwide raising the need to exploit novel, alternative and emerging technologies to respond to the emergency created by the weak health-care systems. In this context, Artificial Intelligence (AI) techniques can give a valid support to public health authorities, complementing traditional approaches with advanced tools. This study provides a comprehensive review of methods, algorithms, applications, and emerging AI technologies that can be utilized for forecasting and diagnosing COVID-19. The main objectives of this review are summarized as follows. (i) Understanding the importance of AI approaches such as machine learning and deep learning for COVID-19 pandemic; (ii) discussing the efficiency and impact of these methods for COVID-19 forecasting and diagnosing; (iii) providing an extensive background description of AI techniques to help non-expert to better catch the underlying concepts; (iv) for each work surveyed, give a detailed analysis of the rationale behind the approach, highlighting the method used, the type and size of data analyzed, the validation method, the target application and the results achieved; (v) focusing on some future challenges in COVID-19 forecasting and diagnosing.
•This is the first survey focusing on the very specific topic of COVID-19 forecasting through AI techniques.•Extensive background of AI methods is provided, which can help non-expert to better catch the underlying concepts.•For each work surveyed, are detailed the method used, the data analyzed, the validation approach the results achieved.•The main limitations of current approaches are reported, including interpretability and learning from limited labeled data.
Assessing students' understanding is central for teachers. While research has focused on factors affecting accuracy as a main performance measure of diagnosing, less is known about teachers' ...diagnostic process. This study investigated the diagnostic process of pre-service teachers in a simulation using a person-centered approach. We examined the frequency of the diagnostic processes describing, explaining, and decision-making as well as their relation to dispositions and diagnostic performance. Findings show that participants’ varying engagement in the diagnostic process is related to different levels of knowledge, task value, and accuracy. We discuss consequences for the adaptive support of learning to diagnose.
Purpose
To evaluate the diagnostic performance of both ultrasonography and MRI findings in finger lesions.
Methods
This study was carried out on seventy symptomatic patients (53 females and 17 ...males). Their ages ranged from 6 to 64 years. All patients were referred to the diagnostic radiology department from various outpatient clinics of general, orthopedic, cosmetic surgeries and rheumatology. All patients were subjected to history taking, clinical examination, laboratory investigations for rheumatoid arthritis patients and radiological investigations. Whenever we had a surgical and pathological final diagnosis, it was considered the gold standard of the results. When only ultrasound and MRI were correlated, MRI was considered the gold standard.
Results
In our study, we found that ultrasonography is useful for evaluating a variety of lesions of the finger. Its widespread availability, relatively low cost, and high spatial resolution make it an excellent tool for investigating finger disorders. MR is the best imaging modality for lesion characterization. By systematically using clinical history, lesion location, findings on radiographs and MR imaging features, the radiologist can differentiate between determinate and indeterminate lesions.
Conclusion
We concluded that ultrasonography and MRI are complementary to reach a correct diagnosis in different etiologies of finger lesions.
Approach to the patient with hair loss Workman, Kaelynn; Piliang, Melissa
Journal of the American Academy of Dermatology,
08/2023, Volume:
89, Issue:
2
Journal Article
(1) Background: Lung cancer's high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers ...promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI's performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI's role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI's potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI's accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI's potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.
Use of autism diagnosing standards in low‐income countries (LICs) are restricted due to the high price and unavailability of trained health professionals. Furthermore, these standards are heavily ...skewed towards developed countries and LICs are underrepresented. Due to such constraints, many LICs use their own ways of assessing autism. This is the first retrospective study to analyze such local practices in Sri Lanka. The study was conducted at Ward 19B of Lady Ridgeway Hospital (LRH) using the clinical forms filled for diagnosing ASD. In this study, 356 records were analyzed, from which 79.5% were boys and the median age was 33 months. For each child, the clinical form together with the Childhood Autism Rating Scale (CARS) value were recorded. In this study, a Clinically Derived Autism Score (CDAS) is obtained from the clinical forms. Scatter plot and Pearson product moment correlation coefficient were used to benchmark CDAS with CARS, and it was found CDAS to be positively and moderately correlated with CARS. In identifying the significant variables, a logistic regression model was built based on clinically observed data and it evidenced that “Eye Contact,” “Interaction with Others,” “Pointing,” “Flapping of Hands,” “Request for Needs,” “Rotate Wheels,” and “Line up Things” variables as the most significant variables in diagnosing autism. Based on these significant predictors, the classification tree was built. The pruned tree depicts a set of rules, which could be used in similar clinical environments to screen for autism.
Lay Summary
Screening and diagnosing autism in low‐income countries such as Sri Lanka has always been a challenge due to limited resources and not being able to afford global standards. Due to these challenges, locally developed clinical forms have been used. This study is the first to analyze a clinical record set for autism in Sri Lanka to benchmark the local clinic form with a global standard. Furthermore, this study identifies the most significant diagnostic symptoms for children and based on these significant features, a simple set of IF–THEN rules are derived which could be used for screening autism in a similar clinical environment by health officials in the absence of consultants.
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.
