AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis.

(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.

A Pulmonary Telerehabilitation Program Improves Exercise Capacity and Quality of Life in Young Females Post-COVID-19 Patients.

OBJECTIVE: To examine the impact of telerehabilitation training on exercise capacity, lung function, and health-related quality of life (HRQOL) in comparison to no rehabilitation for post-COVID-19 symptoms in adult females. METHODS: A randomized controlled trial of 48 females after mild to moderate COVID-19 survival were equally and randomly assigned to one of two groups: intervention group or control group. Three sessions per week for 6 weeks of a telerehabilitation program provided via a smartphone to the intervention group. Spirometry was used to quantify lung function, a 6-minute walk test (6MWT) measured in meters to measure exercise capacity, and the Short Form Health Survey-36 was used to assess HRQOL. RESULTS: After treatment, there was no statistically significant difference in forced vital capacity (FVC) or forced expiratory volume in 1 second (FEV1) between groups (p>0.05), but the 6MWT of the intervention group increased significantly more than that of the control group (p=0.001). The percent of change in 6MWT for the intervention group and control group was 14.22% and 4.21%, respectively. After therapy, the intervention group's HRQOL significantly improved when compared to the control group's (p=0.001). CONCLUSION: This study showed that a telerehabilitation programs improved exercise capacity and HRQOL in young females post-COVID-19 compared to no rehabilitation.

Advancements in the Clinical Outcomes of Functional Neurosurgery With Deep Brain Stimulation for Movement Disorders: A Literature Review.

This literature review explores recent advancements in deep brain stimulation (DBS) surgery for movement disorders. It highlights notable improvements, including closed-loop stimulation techniques, optogenetics, and improved surgical targeting. Positive clinical outcomes with low complication rates and improved motor symptoms are consistently reported. The review emphasizes the importance of minimizing risks through meticulous surgical practices and discusses potential complications associated with DBS surgery. Future prospects focus on enhancing technology, refining surgical techniques, and conducting further research. Closed-loop stimulation optimizes DBS efficacy by tailoring stimulation parameters to individual patient needs. Optogenetics offers precise modulation of neural activity with light-sensitive proteins, enabling more targeted treatments. Cybersecurity measures are essential due to the integration of wireless and digital technologies in DBS systems. DBS surgery has significantly improved the management of movement disorders with its safety and effectiveness. Ongoing research in closed-loop stimulation, optogenetics, and cybersecurity is expected to further enhance DBS technology and outcomes, benefiting patients with treatment-resistant movement disorders.

Association between sleep duration and dental caries in a nationally representative U.S. population.

BACKGROUND: Dental caries is considered one of the most prevalent chronic diseases worldwide despite all dental public health efforts. Short sleep duration has been established as a risk factor for several medical conditions. In this study, we aimed to examine the relationship between sleep duration and dental caries. METHODS: Data were collected from the 2017-2018 cycle of the National Health and Nutrition Examination Survey, a nationally representative health survey conducted in the United States. Participants who completed sleep questionnaires were examined by dentists using standardized clinical criteria. Analysis was limited to Individuals aged ≥ 16 years with complete clinical oral examination data and who completed the sleep questionnaire (N = 5,205). The data were weighted to provide a national estimate, and multiple potential covariates were included in the analysis to account for the complex sample design. The main outcomes of the study were untreated dental caries and dental caries experience. The main predictor variables were average sleep hours/night and a binary variable with 7 h/night as a cut off. Multiple weighted Poisson and logistic regression analyses were conducted to test the hypothesis that people with short sleep duration are more likely to exhibit dental caries. RESULTS: This study showed a statistically significant negative relationship between sleep duration and dental caries amongst all weighted adjusted analyses conducted. For a one hour increase in average sleep hours, the Adjusted Odds Ratio (AOR) of having a dental caries experience might decrease by 0.86 (AOR = 0.86, 95% CI = 0.75-0.98, P < 0.05). Individuals who reported an average sleep of ≥ 7 h were less likely to have a dental caries experience compared to individuals who reported an average sleep of < 7 h (AOR = 0.52, 95% CI = 0.33-0.82, P < 0.05). For a one hour increase in average sleep hours, the Adjusted Mean Ratio (AMR) of having a dental caries experience might decrease by 0.97 (AMR = 0.97, 95% CI = 0.96-0.99, P < 0.05), and was lower for those who reported sleeping ≥ 7 h/night than individuals who reported sleeping < 7 h/night (AMR = 0.92, 95% CI = 0.87-0.99, P < 0.05). CONCLUSION: Findings of this cross-sectional representative study of the U.S. population revealed a statistically significant negative association between sleep duration and dental caries. In this study, individuals who slept < 7 h/night were more likely to exhibit dental caries.

Web-based expert system with quick response code for beta-thalassemia management.

ß-thalassemia is an inherited blood disorder in which the body cannot produce hemoglobin normally. Since patients with this condition receive blood transfusions regularly, iron builds up primarily in organs such as the heart, liver and endocrine glands. Accumulation of iron in the organs necessitates chelation therapy. These patients must visit the hospital frequently to assess and follow up on their health condition. Physician intervention is required after each regular assessment to adjust the treatment. Lifelong healthcare support using a web-based expert system with a quick response code is designed for ß-thalassemia management in order to deliver benefits to patients, physicians, and other healthcare providers. The aim of this study is to implement a web-based expert system for ß-thalassemia management in order to provide treatment recommendations and support the lifelong healthcare of patients. The system provides patient-related details, such as medical history, medicines, and appointments, in real-time. It has been also tested in real-life cases and shown to enhance ß-thalassemia management.