Brain-based sex differences in schizophrenia: A systematic review of fMRI studies.

Schizophrenia is a chronic psychiatric disorder with characteristic symptoms of delusions, hallucinations, lack of motivation, and paucity of thought. Recent evidence suggests that the symptoms of schizophrenia, negative symptoms in particular, vary widely between the sexes and that symptom onset is earlier in males. A better understanding of sex-based differences in functional magnetic resonance imaging (fMRI) studies of schizophrenia may provide a key to understanding sex-based symptom differences. This study aimed to summarize sex-based functional magnetic resonance imaging (fMRI) differences in brain activity of patients with schizophrenia. We searched PubMed and Scopus to find fMRI studies that assessed sex-based differences in the brain activity of patients with schizophrenia. We excluded studies that did not evaluate brain activity using fMRI, did not evaluate sex differences, and were nonhuman or in vitro studies. We found 12 studies that met the inclusion criteria for the current systematic review. Compared to females with schizophrenia, males with schizophrenia showed more blood oxygen level-dependent (BOLD) activation in the cerebellum, the temporal gyrus, and the right precuneus cortex. Male patients also had greater occurrence of low-frequency fluctuations in cerebral blood flow in frontal and parietal lobes and the insular cortex, while female patients had greater occurrence of low-frequency fluctuations in the hippocampus, parahippocampus, and lentiform nucleus. The current study summarizes fMRI studies that evaluated sex-based fMRI brain differences in schizophrenia that may help to shed light on the underlying pathophysiology and further understanding of sex-based differences in the clinical presentation and course of the disorder.

Artificial intelligence in osteoarthritis detection: A systematic review and meta-analysis.

OBJECTIVES: As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI, and to compare them with clinicians' performance. MATERIALS AND METHODS: A search in PubMed and Scopus was performed to find studies published up to April 2022 that evaluated and/or validated an AI algorithm for the detection or classification of OA. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the involved joint and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Prediction Model Study Risk of Bias Assessment Tool reporting guidelines. RESULTS: Of the 61 studies included, 27 studies with 91 contingency tables provided sufficient data to enter the meta-analysis. The pooled sensitivities for AI algorithms and clinicians on internal validation test sets were 88% (95% confidence interval [CI]: 86,91) and 80% (95% CI: 68,88) and pooled specificities were 81% (95% CI: 75,85) and 79% (95% CI: 80,85), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 94% (95% CI: 90,97) and 91% (95% CI: 77,97), respectively. CONCLUSION: Although the results of this meta-analysis should be interpreted with caution due to the potential pitfalls in the included studies, the promising role of AI as a diagnostic adjunct to radiologists is indisputable.

Diagnostic Performance of Artificial Intelligence in Detection of Hepatocellular Carcinoma: A Meta-analysis.

Due to the increasing interest in the use of artificial intelligence (AI) algorithms in hepatocellular carcinoma detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI and to compare them with clinicians' performance. A search in PubMed and Scopus was performed in January 2024 to find studies that evaluated and/or validated an AI algorithm for the detection of HCC. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the modality of imaging and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST) reporting guidelines. Out of 3177 studies screened, 44 eligible studies were included. The pooled sensitivity and specificity for internally validated AI algorithms were 84% (95% CI: 81,87) and 92% (95% CI: 90,94), respectively. Externally validated AI algorithms had a pooled sensitivity of 85% (95% CI: 78,89) and specificity of 84% (95% CI: 72,91). When clinicians were internally validated, their pooled sensitivity was 70% (95% CI: 60,78), while their pooled specificity was 85% (95% CI: 77,90). This study implies that AI can perform as a diagnostic supplement for clinicians and radiologists by screening images and highlighting regions of interest, thus improving workflow.

Optical coherence tomography angiography measurements in systemic lupus erythematosus: A systematic review and meta-analysis.

Systemic lupus erythematosus (SLE) is an autoimmune disease affecting various organs. Ocular involvement, particularly retinopathy, is common, emphasizing the significance of early detection. Optical coherence tomography angiography (OCTA), a non-invasive imaging technique, reveals microvascular changes, aiding SLE diagnosis and monitoring. This study evaluates OCTA's effectiveness in detecting SLE-related retinal alterations. A systemic search was undertaken across PubMed, Embase, and Scopus databases to identify studies presenting OCTA measurements in SLE patients compared to healthy controls. The meta-analysis, employing either fixed-effects or random-effects models based on heterogeneity levels, was conducted. Additionally, subgroup and sensitivity analyses, meta-regression, and quality assessments were carried out. Thirteen studies of 565 eyes in the SLE group and 560 eyes in the control group were included. The meta-analyses revealed that SLE patients had a significantly lower retinal vessel density in the superficial and deep capillary plexus layers, choriocapillaris flow area, and foveal avascular zone (FAZ) circularity index compared to healthy controls, but that there were no significant differences in the FAZ area and perimeter. These findings highlight how OCTA can provide a noninvasive assessment of SLE effects on the retinal microvasculature, potentially presenting a reliable biomarker for more precise detection of SLE and disease activity monitoring.

Choroidal thickness in eyes of rheumatoid arthritis patients measured using optical coherence tomography: A systematic review and meta-analysis.

Rheumatoid arthritis (RA), an autoimmune disease, affects eyes in 25% of cases. Retinal alterations in RA can function as biomarkers as early risk indicators for developing sight-threatening conditions. Optical coherence tomography (OCT) provides high-resolution images of the retina and its component's thickness measures. The purpose of this review is to compare the choroidal thickness (CT) of RA patients and healthy controls. We examined the databases of PubMed, Scopus, and Embase. Depending on the heterogeneity, an appropriate model was used for the meta-analysis. Additionally, meta-regression, publication bias, subgroup analyses, and quality evaluation were carried out. We evaluated 8 studies involving 363 RA patients and 343 healthy controls. Our findings demonstrated that RA participants had significantly lower CT at 500 and 1500 µm nasal and temporal to the fovea compared to controls. The subfoveal, 1000 µm temporal and nasal to the fovea, and average CT, however, did not demonstrate statistical significance. The results of this study demonstrate that choroidal thickness is different in RA patients from healthy controls in several areas. OCT measurements may be related to both the visual acuity and the possibility of developing several rheumatic-ophthalmic problems. Future research is thus needed to get more firm findings.

Diagnostic Performance of Artificial Intelligence in Detection of Primary Malignant Bone Tumors: a Meta-Analysis.

We aim to conduct a meta-analysis on studies that evaluated the diagnostic performance of artificial intelligence (AI) algorithms in the detection of primary bone tumors, distinguishing them from other bone lesions, and comparing them with clinician assessment. A systematic search was conducted using a combination of keywords related to bone tumors and AI. After extracting contingency tables from all included studies, we performed a meta-analysis using random-effects model to determine the pooled sensitivity and specificity, accompanied by their respective 95% confidence intervals (CI). Quality assessment was evaluated using a modified version of Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) and Prediction Model Study Risk of Bias Assessment Tool (PROBAST). The pooled sensitivities for AI algorithms and clinicians on internal validation test sets for detecting bone neoplasms were 84% (95% CI: 79.88) and 76% (95% CI: 64.85), and pooled specificities were 86% (95% CI: 81.90) and 64% (95% CI: 55.72), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 84% (95% CI: 75.90) and 91% (95% CI: 83.96), respectively. The same numbers for clinicians were 85% (95% CI: 73.92) and 94% (95% CI: 89.97), respectively. The sensitivity and specificity for clinicians with AI assistance were 95% (95% CI: 86.98) and 57% (95% CI: 48.66). Caution is needed when interpreting findings due to potential limitations. Further research is needed to bridge this gap in scientific understanding and promote effective implementation for medical practice advancement.

Characteristics of HHV-7 meningitis: a systematic review.

There are limited data on HHV-7 meningitis and this systematic review used electronic search to gather pieces of evidence regarding its characteristics. Nine articles were included which three were case reports and the rest of the articles were retrospective studies. Altogether, 32 cases were described in the literature that 13 were females and 26 were aged less than 16 years old. The HHV-7 meningitis has been reported in any season, especially in winter. It affected both immunocompetent and immunocompromised individuals and mostly presented with fever and headache, however rash and seizure have also been documented. The CSF analysis in general showed an elevated range of cell count with lymphocytic predominance and normal to slightly elevated protein levels. Thirteen patients did not receive treatment for HHV-7 meningitis and full recovery was gained in the majority of cases after about 10 days. This review summarizes characteristics of HHV-7 meningitis in the literature, and yet epidemiological studies are needed to shed more light which eventually could be helpful for the diagnosis and management of this disease.