Retinal and choroidal changes after anti-VEGF therapy in neovascular-AMD patients: A systematic review and meta-analysis of SD-OCT studies

BACKGROUND

In recent years, the progress made in the field of optical coherence tomography has helped to understand the changes in eye layers in patients with exudative age-related macular degeneration (nAMD). Early diagnosis of nAMD, a leading cause of irreversible vision impairment, is helpful. Therefore, we performed a meta-analysis on OCT measurement alterations before and after anti-VEGF therapy in patients with nAMD and controls.

METHOD

We systematically searched Scopus, PubMed, Cochrane, and Web of Science to find articles that measured choroidal and retinal layer changes after anti-VEGF therapy in nAMD Patients. We chose either a fixed-effects or random-effects model based on the assessed heterogeneity level to perform a meta-analysis. In addition, we conducted meta-regression, subgroup analyses, publication bias, and quality assessment for included studies.

RESULTS

Thirteen studies were included in the meta-analysis, with 733 total participants. Foveal thickness and subfoveal choroidal thickness (CT) decreased significantly in the first 3 years after injections, except for subfoveal CT in the third year after injection. It also showed that CT at 1500 µm temporal and nasal to the fovea did not significantly change.

CONCLUSION

Our results showed anti-VEGF treatment for nAMD patients was associated with a significant reduction in foveal thickness and subfoveal CT in the first 2 years after treatment. Our analysis did not reveal any correlation between changes in foveal thickness and subfoveal CT with best-corrected visual acuity or other factors.

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.

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.

Brain-based Sex Differences in Depression: A Systematic Review of Neuroimaging Studies

Major depressive disorder (MDD) is a common psychiatric illness with a wide range of symptoms such as mood decline, loss of interest, and feelings of guilt and worthlessness. Women develop depression more often than men, and the diagnostic criteria for depression mainly rely on female patients' symptoms. By contrast, male depression usually manifests as anger attacks, aggression, substance use, and risk-taking behaviors. Various studies have focused on the neuroimaging findings in psychiatric disorders for a better understanding of their underlying mechanisms. With this review, we aimed to summarize the existing literature on the neuroimaging findings in depression, separated by male and female subjects. A search was conducted on PubMed and Scopus for magnetic resonance imaging (MRI), functional MRI (fMRI), and diffusion tensor imaging (DTI) studies of depression. After screening the search results, 15 MRI, 12 fMRI, and 4 DTI studies were included. Sex differences were mainly reflected in the following regions: 1) total brain, hippocampus, amygdala, habenula, anterior cingulate cortex, and corpus callosum volumes, 2) frontal and temporal gyri functions, along with functions of the caudate nucleus and prefrontal cortex, and 3) frontal fasciculi and frontal projections of corpus callosum microstructural alterations. Our review faces limitations such as small sample sizes and heterogeneity in populations and modalities. But in conclusion, it reflects the possible roles of sex-based hormonal and social factors in the depression pathophysiology.

Optical coherence tomography angiography measurements in Parkinson's disease: A systematic review and meta-analysis

Optical coherence tomography angiography (OCT-A) is an ocular imaging technology that has emerged as a non-invasive tool to evaluate retinal microvascular changes in neurodegenerative diseases including Parkinson's disease (PD) and Alzheimer's disease. While several studies have reported on the presence of pathologic retinal microvascular alterations in PD, the utility of OCT-A as a biomarker for PD evaluation is still unclear. A systematic review and meta-analysis were performed to explore the current evidence for the role of OCT-A in PD published up until June 2022. PubMed, Scopus, and Web of Science databases were used to systematically identify relevant papers and a meta-analysis was conducted using Stata16 software according to the level of heterogeneity applying a random- or fixed-effect model. Thirteen studies of 925 eyes in the PD group and 1501 eyes in the control group assessing OCT-A findings in PD patients were included. The meta-analyses revealed that the foveal region of PD patients had a significantly lower vessel density in the superficial capillary plexus (SCP) compared to healthy controls but that there were no significant differences in the foveal avascular zone, the SCP in whole, parafoveal, and perifoveal regions, and deep capillary plexus. OCT-A metrics may act as a potential biomarker for a more accurate and early PD diagnosis. Still, the OCT-A algorithms and interchangeability between OCT-A devices require further standardization to draw clinical conclusions regarding their utility.