Background: Psychiatric disorders significantly impact pediatric populations globally, affecting individual development, education and societal integration. In Nigeria, where nearly half of the population is under the age of 15, there remains a substantial gap in our understanding of the burden of these disorders within this demographic. This systematic review was conducted to quantify the prevalence of psychiatric disorders among Nigerian children and adolescents in clinical and community settings. Methods: We identified and examined original research articles available in PUBMED/Medline, EMBASE, and AJOL from January 2010 to August 2024, and selected studies that evaluated the prevalence of psychiatric disorders among children and adolescents ( 75%). The most common psychiatric disorders across the different settings were major depression among children and adolescents attending primary care (prevalence = 7.6%; 95% CI: 1.0; 40.5; three studies; 1,278 individuals), separation anxiety disorder among primary school children (prevalence = 14%; 95% CI: 9.5, 19.6; one study; 200 pupils), and behavioral disorders among secondary school students (prevalence = 15.1%; 95% CI: 14.4, 15.8; one study; 9,450 adolescents). Conclusion: The prevalence of psychiatric disorders among Nigerian children and adolescents is substantial, underscoring the critical need for enhanced mental health services.

Background: Image-based artificial intelligence (AI) risk models can estimate short-term breast cancer risk directly from mammograms and may outperform traditional questionnaire-based tools. However, risk stratification remains particularly challenging in women with dense breasts who do not otherwise meet high-risk criteria. At our institutions, molecular breast imaging (MBI) is used as supplemental screening for this population. This study evaluated the performance and clinical utility of a mammography-based AI risk model (iCAD ProFound AI^® Risk) in predicting short-term breast cancer risk among women with dense breasts undergoing MBI. Methods: This retrospective IRB-approved study included 416 non-actionable (BI-RADS category 1 or 2) screening digital breast tomosynthesis mammograms (BI-RADS C–D density) obtained from 2018 to 2023, all followed by MBI within one year. The cohort comprised 70 cancer cases (16.8%) and 346 (83.2%) non-cancer controls. Mammograms were retrospectively processed using the ProFound AI^® Risk model to generate 1-year risk and density scores. Tyrer-Cuzick and Gail model scores were computed for comparison. Group differences were assessed using t-tests and effect sizes, and model discrimination was evaluated with ROC analysis using area under the curve (AUC), sensitivity, specificity, and 95% confidence intervals (CIs). Results: Across the full cohort, mean AI risk scores were higher in cancer cases than controls (0.41±0.35 vs. 0.37±0.21), although this difference was not statistically significant (P=0.239; Cohen’s d=0.23). Subgroup analyses demonstrated progressively stronger discriminatory performance with increasing breast density. The greatest separation was observed in women with extremely dense breasts (category D), where the AI model achieved an AUC of 0.75 (95% CI: 0.61–0.89; P=0.049), with 69.3% sensitivity and 61.1% specificity at a threshold of 0.14. Effect size in this group was the largest (d=0.41). In contrast, traditional models showed limited and non-significant discrimination across all density categories, with AUC values ranging from 0.54 to 0.63. When stratified by cancer subtype, the AI model produced significantly higher risk scores in invasive lobular carcinoma (ILC) compared with controls (0.69±0.46 vs. 0.41±0.32; P=0.048; d=0.56). Although differences in ductal carcinoma in situ (DCIS) and invasive ductal carcinoma (IDC) were not significant, risk scores trended higher for cancer cases. A similar pattern of increasing AI-estimated risk was observed with higher tumor grade, with the strongest separation seen in grade 2 cancers (P=0.089). Conclusions: Although overall differences between cancer and non-cancer groups were not statistically significant, the mammography-based AI risk model demonstrated meaningful and statistically significant discrimination in women with extremely dense breasts, outperforming both Tyrer-Cuzick and Gail models. The AI model also showed better separation in ILC and in higher-grade tumors. These findings support the role of image-based AI tools in refining risk assessment in women for whom mammography is least effective and in guiding more targeted use of supplemental MBI screening.

Objectives: Heritable connective tissue disorders (HCTDs), including hypermobile Ehlers-Danlos syndrome (hEDS) and hypermobile spectrum disorder (HSD), have complex clinical manifestations, but the prevalence of left common iliac vein (LCIV) compression in these groups is not well known. This study aimed to compare the prevalence of significant LCIV compression in patients with hEDS, HSD, and healthy controls. Methods: This retrospective, IRB-approved, single-center study analyzed 109 patients (25 hEDS, 64 HSD, 20 controls) who underwent contrast-enhanced abdominal and pelvic CT imaging from 2020 to 2023. LCIV compression was measured on axial CT, with >50% luminal narrowing defined as significant. Statistical analyses included chi-square tests, odds ratios, and t -tests. Interrater reliability was evaluated using intraclass correlation coefficients. Results: Significant LCIV compression occurred in 68.0% of hEDS, 65.6% of HSD, and 35.0% of controls ( p = 0.034). Both hEDS ( p = 0.027) and HSD ( p = 0.015) groups had significantly higher compression rates than controls, with no statistically significant difference between the two clinical groups ( p = 0.831). Odds of compression were nearly fourfold greater in hEDS (OR 3.95) and HSD (OR 3.54) compared to controls. Conclusion: LCIV compression is significantly more prevalent in patients with hEDS and HSD than in healthy controls, warranting further investigation of its clinical significance.

Background: Mental health disorders pose a major public health challenge worldwide, with one in eight individuals affected, particularly by anxiety and depression. In Nigeria, mental health issues are similarly prevalent, yet access to mental healthcare remains limited. Recent policy initiatives have increased governmental interest, but mental health research in Nigeria remains limited and fragmented. Methods: This protocol outlines a systematic review and meta-analysis methodology to quantify the prevalence of mental health disorders in Nigeria. The review follows the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) and Joanna Briggs Institute (JBI) guidelines and includes cross-sectional studies, surveys, and cohort studies providing prevalence data on mental disorders diagnosed by healthcare professionals or identified via validated screening tools in the country. Data sources include PubMed/Medline, EMBASE, and African Journals Online (AJOL), covering publications up to April 2024. Study selection, data extraction, and quality assessment will be conducted independently by multiple reviewers. A random-effects meta-analysis model will be used to synthesize data, with sensitivity and subgroup analyses to explore heterogeneity. Discussion: This systematic review and meta-analysis provides an estimate of the prevalence of mental health disorders in Nigeria, informing future public health policy and resource allocation. These findings will contribute significantly to understanding mental health in Nigeria and support the development of effective strategies and policies. Systematic review registration: This protocol is registered with PROSPERO (CRD42024559090).