Urinary tract infections (UTIs) are among the most common bacterial infections worldwide, requiring timely and accurate diagnosis to guide appropriate therapy and reduce antimicrobial resistance. Although urine culture remains the diagnostic gold standard, its prolonged turnaround time and susceptibility to pre-analytical variability limit its clinical efficiency. Recent advances in artificial intelligence (AI) have positioned urinalysis as a promising alternative diagnostic approach, utilizing machine learning and deep learning algorithms for automated analysis and prediction. This review synthesizes current evidence on AI applications in urinalysis for UTI diagnosis, examining computational techniques, diagnostic performance, clinical integration, limitations, and future directions. The literature demonstrates that AI-powered urinalysis can achieve diagnostic accuracy comparable to urine culture, with high sensitivity and specificity while reducing diagnostic time. Integration of AI into clinical workflows has the potential to enhance decision-making, streamline laboratory processes, and support antimicrobial stewardship. However, challenges related to data heterogeneity, algorithm interpretability, validation, and regulatory requirements remain significant barriers to widespread adoption. Overall, AI-driven urinalysis represents a transformative opportunity to complement the existing diagnostic standard and advance more rapid, efficient, and personalized approaches to UTI management.

Human ; Artificial Intelligence ; Urinalysis ; Urinary Tract Infections ; Machine Learning ; Deep Learning

Abstract Intestinal ultrasound (IUS) is increasingly recognized as a vital, non-invasive tool for managing inflammatory bowel disease (IBD) due to its real-time assessment capabilities and patient comfort. However, the lack of standardized training pathways remains a significant barrier to its widespread adoption, particularly in low- and middle-income settings. This editorial evaluates the implementation of a mastery learning-based workshop in Indonesia designed to enhance IUS skills among physicians. The mastery learning framework—incorporating flipped learning, deliberate practice, and real-time feedback led to significant improvements in technical performance with large effect sizes. Despite these gains, only about two-thirds of participants achieved competency in sigmoid colon scanning, and only half met the standard for terminal ileum assessment. This suggests that while short-term intensive workshops are effective for early skill acquisition, they may be insufficient for ensuring consistent proficiency in technically demanding tasks. Mastery learning offers a structured and reproducible approach to gastroenterology procedural training. To translate these initial educational gains into durable clinical expertise, future programs should consider longitudinal curricula that include sustained practice, mentorship, and ongoing assessment.
Intestinal ultrasound ; mastery learning ; Inflammatory bowel disease ; medical education ; competency-based training ; gastroenterology

BACKGROUND

The COVID-19 pandemic disrupted education worldwide, prompting a rapid shift to emergency remote teaching that challenged students’ learning and mental health. Nursing students, in particular, faced heightened pressures due to the suspension or online adaptation of essential clinical experiences, alongside the need to master theoretical and practical competencies. Emerging evidence indicates that such stressors adversely affect students’ emotional and psychological well-being, potentially influencing academic outcomes. Understanding the relationship between mental health and academic performance among nursing students is crucial for developing targeted interventions that support their well-being and professional readiness.

OBJECTIVE

This study analyzed the mental health status and academic performance of graduating nursing students during the challenging period of remote learning amid the pandemic in a government school in Leyte.

METHODS

The study utilized a descriptive correlational design to explore the relationships between mental health status and academic performance among nursing students. A modified self-administered questionnaire was utilized to gather data. Ethical approval from Eastern Visayas Health Research and Development ConsortiumEthics Review with ERC number 2023-024 was secured, and data collection occurred through various methods. Data analysis used SPSS version 24, emphasizing the importance of understanding these relationships in educational settings.

RESULTS

The study assessed the demographic profile, online learning attributes, mental health status, and academic performance of 20 nursing students during the pandemic. All students passed their courses, despite reporting moderate emotional loneliness and irritability, but minimal fear of COVID-19. Significant correlations were found between demographic factors and mental health indicators. The null hypothesis, suggesting no relationship between demographic factors and mental health, is void, as significant associations were identified. Recommendations include enhancing mental health support in nursing education to address these challenges.

CONCLUSION

This study highlights the experiences of 20 nursing students from a government college in Leyte during the COVID-19 pandemic. Predominantly young women from rural, low-income backgrounds, these students faced challenges like poor internet access but successfully completed their academic requirements, showcasing resilience. While they reported low fear of COVID-19, moderate emotional loneliness and irritability indicated underlying mental health issues. The findings stress the need for educational institutions to provide mental health support and address the digital divide to enhance student well-being and success.

Human ; Male ; Female ; Young Adult: 19-24 Yrs Old ; Adult: 25-44 Yrs Old ; Statistics As Topic ; Psychological Well-being ; Indicators And Reagents ; Students, Nursing ; Suspensions ; Academic Performance ; Learning ; Pandemics ; Nursing ; Education, Nursing ; Covid-19 ; Mental Health

Breast cancer (BC) is the most frequently diagnosed malignancy worldwide and a leading cause of death in women. Machine learning (ML) enables data-driven risk stratification and outcome prediction, potentially improving screening, treatment choices, and overall care. This scoping review cataloged ML models used to predict BC risk and post-treatment outcomes. A PubMed search using MeSH terms (“breast neoplasms,” “breast cancer,” “machine learning,” and “predictive value of tests”) retrieved 44 records, with 9 added by manual searching; 24 studies were included. We excluded 29 reports limited to imaging/radiomics or biomolecular/omics, plus other ineligible items. Model usage among included studies: 13 random forest (RF); 13 support vector machine; 7 decision tree; 5 k-nearest neighbors; 4 deep learning; 3 naïve Bayes; 2 artificial neural networks. Average RF accuracy surpassed 90% in most reports, indicating RF is both common and consistently strong for predictive modeling, supporting targeted clinical translation.
breast neoplasm ; machine learning ; predictive value of tests

BACKGROUND AND OBJECTIVES

The Philippines faces challenges in the screening of tuberculosis (TB), one of them being the shortage in the health workforce who are skilled and allowed to screen TB. Deep learning neural networks (DLNNs) have shown potential in the TB screening process utilizing chest radiographs (CXRs). However, local studies on AIbased TB screening are limited. This study evaluated qXR3.0 technology's diagnostic performance for TB screening in Filipino adults aged 15 and older. Specifically, we evaluated the specificity and sensitivity of qXR3.0 compared to radiologists' impressions and determined whether it meets the World Health Organization (WHO) standards.

METHODS

A prospective cohort design was used to perform a study on comparing screening and diagnostic accuracies of qXR3.0 and two radiologist gradings in accordance with the Standards for Reporting Diagnostic Accuracy (STARD). Subjects from two clinics in Metro Manila which had qXR 3.0 seeking consultation at the time of study were invited to participate to have CXRs and sputum collected. Radiologists' and qXR3.0 readings and impressions were compared with respect to the reference standard Xpert MTB/RiF assay. Diagnostic accuracy measures were calculated.

RESULTS

With 82 participants, qXR3.0 demonstrated 100% sensitivity and 72.7% specificity with respect to the reference standard. There was a strong agreement between qXR3.0 and radiologists' readings as exhibited by the 0.7895 (between qXR 3.0 and CXRs read by at least one radiologist), 0.9362 (qXR 3.0 and CXRs read by both radiologists), and 0.9403 (qXR 3.0 and CXRs read as not suggestive of TB by at least one radiologist) concordance indices.

CONCLUSIONS

qXR3.0 demonstrated high sensitivity to identify presence of TB among patients, and meets the WHO standard of at least 70% specificity for detecting true TB infection. This shows an immense potential for the tool to supplement the shortage of radiologists for TB screening in the country. Future research directions may consider larger sample sizes to confirm these findings and explore the economic value of mainstream adoption of qXR 3.0 for TB screening.

Human ; Tuberculosis ; Diagnostic Imaging ; Deep Learning

Objective Based on 25 indicators including immune factors, cell count classification, and smear results of the knee joint fluid, machine learning models were established to distinguish between osteoarthritis (OA) and rheumatoid arthritis (RA). Methods 100 OA and 40 RA patients scheduled for total knee arthroplasty were enrolled respectively. Each patient's knee joint fluid was collected preoperatively. Nucleated cells were counted and classified. The expression levels of immune factors, including tumor necrosis factor alpha (TNF-α), interleukin-1 beta (IL-1β), IL-6, IL-8, IL-15, matrix metalloproteinase 3 (MMP3), MMP9, MMP13, rheumatoid factor (RF), serum amyloid A (SAA), C-reactive protein (CRP), and others were measured. Smears and microscopic classification of all the immune factors were performed. Independent influencing factors for OA or RA were identified using univariate binary logistic regression, Lasso regression, and multivariate binary logistic regression. Based on the independent influencing factors, three machine learning models were constructed which are logistic regression, random forest, and support vector machine. Receiver operating characteristic curve (ROC), calibration curve and decision curve analysis (DCA) were used to evaluate and compare the models. Results A total of 5 indicators in the knee joint fluid were screened out to distinguish OA and RA, which were IL-1β(odds ratio(OR)=10.512, 95× confidence interval (95×CI) was 1.048-105.42, P=0.045), IL-6 (OR=1.007, 95×CI was 1.001-1.014, P=0.022), MMP9 (OR=3.202, 95×CI was 1.235-8.305, P=0.017), MMP13 (OR=1.002, 95× CI was 1-1.004, P=0.049), and RF (OR=1.091, 95×CI was 1.01-1.179, P=0.026). According to the results of ROC, calibration curve and DCA, the accuracy (0.979), sensitivity (0.98) and area under the curve (AUC, 0.996, 95×CI was 0.991-1) of the random forest model were the highest. It has good validity and feasibility, and its distinguishing ability is better than the other two models. Conclusion The machine learning model based on immune factors in the knee joint fluid holds significant value in distinguishing OA and RA. It provides an important reference for the clinical early differential diagnosis, prevention and treatment of OA and RA.
Humans ; Arthritis, Rheumatoid/metabolism* ; Machine Learning ; Male ; Female ; Middle Aged ; Aged ; Synovial Fluid/immunology* ; Osteoarthritis, Knee/metabolism* ; Knee Joint/metabolism* ; ROC Curve ; Diagnosis, Differential

Objective To mine and analyze the routine blood test data of children with allergic rhinitis (AR), identify routine blood parameters related to childhood allergic rhinitis, establish an effective diagnostic model, and evaluate the performance of the model. Methods This study was a retrospective study of clinical cases. The experimental group comprised a total of 1110 children diagnosed with AR at the First Affiliated Hospital of Air Force Medical University during the period from December 12, 2020 to December 12, 2021, while the control group included 1109 children without a history of allergic rhinitis or other allergic diseases who underwent routine physical examinations during the same period. Information such as age, sex and routine blood test results was collected for all subjects. The levels of routine blood test indicators were compared between AR children and healthy children using comprehensive intelligent baseline analysis, with indicators of P≥0.05 excluded; variables were screened by Lasso regression. Binary Logistic regression was used to further evaluate the influence of multiple routine blood indexes on the results. Five kinds of machine model algorithms were used, namely extreme value gradient lift (XGBoost), logistic regression (LR), gradient lift decision tree (LGBMC), Random forest (RF) and adaptive lift algorithm (AdaBoost), to establish the diagnostic models. The receiver operating characteristic (ROC) curve was used to screen the optimal model. The best LightGBM algorithm was used to build an online patient risk assessment tool for clinical application. Results Statistically significant differences were observed between the AR group and the control group in the following routine blood test indicators: mean cellular hemoglobin concentration (MCHC), hemoglobin (HGB), absolute value of basophils (BASO), absolute value of eosinophils (EOS), large platelet ratio (P-LCR), mean platelet volume (MPV), platelet distribution width (PDW), platelet count (PLT), absolute values of leukocyte neutrophil (W-LCC), leukocyte monocyte (W-MCC), leukocyte lymphocyte (W-SCC), and age. Lasso regression identified these variables as important predictors, and binary Logistic regression further analyzed the significant influence of these variables on the results. The optimal machine learning algorithm LightGBM was used to establish a multi-index joint detection model. The model showed robust prediction performance in the training set, with AUC values of 0.8512 and 0.8103 in the internal validation set. Conclusion The identified routine blood parameters can be used as potential biomarkers for early diagnosis and risk assessment of AR, which can improve the accuracy and efficiency of diagnosis. The established model provides scientific basis for more accurate diagnostic tools and personalized prevention strategies. Future studies should prospectively validate these findings and explore their applicability in other related diseases.
Humans ; Male ; Female ; Rhinitis, Allergic/blood* ; Child ; Biomarkers/blood* ; Retrospective Studies ; Early Diagnosis ; Child, Preschool ; ROC Curve ; Logistic Models ; Hematologic Tests ; Algorithms ; Adolescent ; Machine Learning

PURPOSE: Septic shock is associated with high mortality and poor outcomes among sepsis patients with coagulopathy. Although traditional statistical methods or machine learning (ML) algorithms have been proposed to predict septic shock, these potential approaches have never been systematically compared. The present work aimed to develop and compare models to predict septic shock among patients with sepsis. METHODS: It is a retrospective cohort study based on 484 patients with sepsis who were admitted to our intensive care units between May 2018 and November 2022. Patients from the 908th Hospital of Chinese PLA Logistical Support Force and Nanchang Hongdu Hospital of Traditional Chinese Medicine were respectively allocated to training (n=311) and validation (n=173) sets. All clinical and laboratory data of sepsis patients characterized by comprehensive coagulation indexes were collected. We developed 5 models based on ML algorithms and 1 model based on a traditional statistical method to predict septic shock in the training cohort. The performance of all models was assessed using the area under the receiver operating characteristic curve and calibration plots. Decision curve analysis was used to evaluate the net benefit of the models. The validation set was applied to verify the predictive accuracy of the models. This study also used Shapley additive explanations method to assess variable importance and explain the prediction made by a ML algorithm. RESULTS: Among all patients, 37.2% experienced septic shock. The characteristic curves of the 6 models ranged from 0.833 to 0.962 and 0.630 to 0.744 in the training and validation sets, respectively. The model with the best prediction performance was based on the support vector machine (SVM) algorithm, which was constructed by age, tissue plasminogen activator-inhibitor complex, prothrombin time, international normalized ratio, white blood cells, and platelet counts. The SVM model showed good calibration and discrimination and a greater net benefit in decision curve analysis. CONCLUSION The SVM algorithm may be superior to other ML and traditional statistical algorithms for predicting septic shock. Physicians can better understand the reliability of the predictive model by Shapley additive explanations value analysis.
Humans ; Shock, Septic/blood* ; Machine Learning ; Male ; Female ; Retrospective Studies ; Middle Aged ; Aged ; Sepsis/complications* ; ROC Curve ; Cohort Studies ; Adult ; Intensive Care Units ; Algorithms ; Blood Coagulation ; Critical Illness

PURPOSE: Early mortality in major trauma has decreased, but MODS remains a leading cause of poor outcomes, driven by trauma-induced cytokine storms that exacerbate injuries and organ damage. METHODS: This prospective cohort study included 79 major trauma patients (ISS >15) treated in the National Center for Trauma Medicine, Peking University People's Hospital, from September 1, 2021, to July 31, 2023. Patients (1) with ISS >15 (according to AIS 2015), (2) aged 15-80 years, (3) admitted within 6 h of injury, (4) having no prior treatment before admission, were included. Exclusion criteria were (1) GCS score <9 or AIS score ≥3 for TBI, (2) confirmed infection, infectious disease, or high infection risk, (3) pregnancy, (4) severe primary diseases affecting survival, (5) recent use of immunosuppressive or cytotoxic drugs within the past 6 months, (6) psychiatric patients, (7) participation in other clinical trials within the past 30 days, (8) patients with incomplete data or missing blood samples. Admission serum inflammatory cytokines and pathophysiological data were analyzed to develop machine learning models predicting MODS within 7 days. LR, DR, RF, SVM, NB, and XGBoost were evaluated based on the area under the AUROC. The SHAP method was used to interpret results. RESULTS: This study enrolled 79 patients with major trauma, and the median (Q₁, Q₃) age was 51 (35, 59) years (52 males, 65.8%). The inflammatory cytokine data were collected for all participants. Among these patients, 35 (44.3%) developed MODS, and 44 (55.7%) did not. Additionally, 2 patients (2.5%) from the MODS group succumbed. The logistic regression model showed strong performance in predicting MODS. Ten key cytokines, IL-18, Eotaxin, MCP-4, IP-10, CXCL12, MIP-3α, MCP-1, IL-1RA, Cystatin C, and MRP8/14 were identified as critical to the trauma-induced cytokine storm and MODS development. Early elevation of these cytokines achieved high predictive accuracy, with an AUROC of 0.887 (95% CI 0.813-0.976). CONCLUSION Trauma-induced cytokine storms are strongly associated with MODS. Early identification of inflammatory cytokine changes enables better prediction and timely interventions to improve outcomes.
Humans ; Prospective Studies ; Middle Aged ; Male ; Female ; Adult ; Aged ; Cytokine Release Syndrome/etiology* ; Adolescent ; Young Adult ; Aged, 80 and over ; Wounds and Injuries/complications* ; Cytokines/blood* ; Multiple Organ Failure/diagnosis* ; Machine Learning

Varicocele is a prevalent condition in the infertile male population. However, to date, which patients may benefit most from varicocele repair is still a matter of debate. The purpose of this study was to evaluate whether certain preintervention sperm parameters are predictive of successful varicocele repair, defined as an improvement in total motile sperm count (TMSC). We performed a retrospective study on 111 patients with varicocele who had undergone varicocele repair, collected from the Department of Endocrinology, Metabolic Diseases and Nutrition, University of Catania (Catania, Italy), and the Unit of Urology at the Selcuk University School of Medicine (Konya, Türkiye). The predictive analysis was conducted through the use of the Brain Project, an innovative tool that allows a complete and totally unbiased search of mathematical expressions that relate the object of study to the various parameters available. Varicocele repair was considered successful when TMSC increased by at least 50% of the preintervention value. For patients with preintervention TMSC below 5 × 10 6 , improvement was considered clinically relevant when the increase exceeded 50% and the absolute TMSC value was >5 × 10 6 . From the preintervention TMSC alone, we found a model that predicts patients who appear to benefit little from varicocele repair with a sensitivity of 50.0% and a specificity of 81.8%. Varicocele grade and serum follicle-stimulating hormone (FSH) levels did not play a predictive role, but it should be noted that all patients enrolled in this study were selected with intermediate- or high-grade varicocele and normal FSH levels. In conclusion, preintervention TMSC is predictive of the success of varicocele repair in terms of TMSC improvement in patients with intermediate- or high-grade varicoceles and normal FSH levels.
Humans ; Varicocele/complications* ; Male ; Retrospective Studies ; Machine Learning ; Adult ; Treatment Outcome ; Sperm Count ; Infertility, Male/etiology* ; Sperm Motility ; Follicle Stimulating Hormone/blood* ; Young Adult