RESUMEN
BACKGROUND AND PURPOSE: Early and reliable prediction of hemorrhagic transformation (HT) in patients with acute ischemic stroke (AIS) is crucial for treatment decisions and early intervention. The purpose of this study was to conduct a systematic review and meta-analysis on the performance of artificial intelligence (AI) and machine learning (ML) models that utilize neuroimaging to predict HT. METHODS: A systematic search of PubMed, EMBASE, and Web of Science was conducted until February 19, 2024. Inclusion criteria were as follows: patients with AIS who received reperfusion therapy; AI/ML algorithm using imaging to predict HT; or presence of sufficient data on the predictive performance. Exclusion criteria were as follows: articles with less than 20 patients; articles lacking algorithms that operate solely on images; or articles not detailing the algorithm used. The quality of eligible studies was assessed using the Quality Assessment of Diagnostic Accuracy Studies-2 and Checklist for Artificial Intelligence in Medical Imaging. Pooled sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model, and a summary receiver operating characteristic curve was constructed using the Reitsma method. RESULTS: We identified six eligible studies, which included 1640 patients. Aside from an unclear risk of bias regarding flow and timing identified in two of the studies, all studies showed low risk of bias and applicability concerns in all categories. Pooled sensitivity, specificity, and DOR were .849, .878, and 45.598, respectively. CONCLUSION: AI/ML models can reliably predict the occurrence of HT in AIS patients. More prospective studies are needed for subgroup analyses and higher clinical certainty and usefulness.
RESUMEN
The ability of neurons to identify correct synaptic partners is fundamental to the proper assembly and function of neural circuits. Relative to other steps in circuit formation such as axon guidance, our knowledge of how synaptic partner selection is regulated is severely limited. Drosophila Dpr and DIP immunoglobulin superfamily (IgSF) cell-surface proteins bind heterophilically and are expressed in a complementary manner between synaptic partners in the visual system. Here, we show that in the lamina, DIP mis-expression is sufficient to promote synapse formation with Dpr-expressing neurons and that disrupting DIP function results in ectopic synapse formation. These findings indicate that DIP proteins promote synapses to form between specific cell types and that in their absence, neurons synapse with alternative partners. We propose that neurons have the capacity to synapse with a broad range of cell types and that synaptic specificity is achieved by establishing a preference for specific partners.