Stroke is an emergency in which delays in treatment can lead to significant loss of neurological function and be fatal. Technologies that increase the speed and accuracy of stroke diagnosis or assist in post-stroke rehabilitation can improve patient outcomes. No resource exists that comprehensively assesses artificial intelligence/machine learning (AI/ML)-enabled technologies indicated for the management of ischemic and hemorrhagic stroke. We queried a United States Food and Drug Administration (FDA) database, along with PubMed and private company websites, to identify the recent literature assessing the clinical performance of FDA-approved AI/ML-enabled technologies. The FDA has approved 22 AI/ML-enabled technologies that triage brain imaging for more immediate diagnosis or promote post-stroke neurological/functional recovery. Technologies that assist with diagnosis predominantly use convolutional neural networks to identify abnormal brain images (e.g., CT perfusion). These technologies perform comparably to neuroradiologists, improve clinical workflows (e.g., time from scan acquisition to reading), and improve patient outcomes (e.g., days spent in the neurological ICU). Two devices are indicated for post-stroke rehabilitation by leveraging neuromodulation techniques. Multiple FDA-approved technologies exist that can help clinicians better diagnose and manage stroke. This review summarizes the most up-to-date literature regarding the functionality, performance, and utility of these technologies so clinicians can make informed decisions when using them in practice.

Purpose: There is great promise in use of machine learning (ML) for the diagnosis, prognosis, and treatment of various medical conditions in ophthalmology and beyond. Applications of ML for ocular neoplasms are in early development and this review synthesizes the current state of ML in ocular oncology. Methods: We queried PubMed and Web of Science and evaluated 804 publications, excluding nonhuman studies. Metrics on ML algorithm performance were collected and the Prediction model study Risk Of Bias ASsessment Tool was used to evaluate bias. We report the results of 63 unique studies. Results: Research regarding ML applications to intraocular cancers has leveraged multiple algorithms and data sources. Convolutional neural networks (CNNs) were one of the most commonly used ML algorithms and most work has focused on uveal melanoma and retinoblastoma. The majority of ML models discussed here were developed for diagnosis and prognosis. Algorithms for diagnosis primarily leveraged imaging (e.g., optical coherence tomography) as inputs, whereas those for prognosis leveraged combinations of gene expression, tumor characteristics, and patient demographics. Conclusions: ML has the potential to improve the management of intraocular cancers. Published ML models perform well, but were occasionally limited by small sample sizes owing to the low prevalence of intraocular cancers. This could be overcome with synthetic data enhancement and low-shot ML techniques. CNNs can be integrated into existing diagnostic workflows, while non-neural networks perform well in determining prognosis.

Purpose: To describe the clinical response and safety profile of the novel HIF-2ɑ inhibitor belzutifan in treating a giant retinal hemangioblastoma with extrascleral extension associated with von Hippel–Lindau syndrome. Methods: A 71-year-old woman with Von Hippel–Lindau syndrome presented with a giant retinal hemangioblastoma with extrascleral extension in her only remaining eye. She had no light perception in the right eye and intraocular pressure was 48. She requested enucleation because of chronic pain, but because of concern for significant bleeding given the size of the neoplasm, a trial of belzutifan was initiated. Results: Within 3 months of treatment initiation, the patient reported an 80% reduction in pain. Magnetic resonance imaging showed 30% reduction in longest tumor diameter. Dose adjustments were guided by serum hemoglobin levels, allowing the patient to remain on the medication for over a year with continued tumor regression on MRI and avoid enucleation. Conclusion: Retinal hemangioblastoma with extrascleral extension is exceedingly rare and its treatment is complex, often requiring enucleation or external beam radiotherapy. This report demonstrates the use of belzutifan to safely and successfully reduce ocular tumor burden of complicated retinal hemangioblastoma with extrascleral extension, ultimately decreasing the need for enucleation.

The fibroblast is a key mediator of wound healing in the heart and other organs, yet how it integrates multiple time-dependent paracrine signals to control extracellular matrix synthesis has been difficult to study in vivo . Here, we extended a computational model to simulate the dynamics of fibroblast signaling and fibrosis after myocardial infarction in response to time-dependent data for nine paracrine stimuli. This computational model was validated against dynamic collagen expression and collagen area fraction data from post-infarction rat hearts. The model predicted that while many features of the fibroblast phenotype at inflammatory or maturation phases of healing could be recapitulated by single static paracrine stimuli (interleukin-1 and angiotensin-II, respectively), mimicking of the proliferative phase required paired stimuli (e.g. TGFβ and angiotensin-II). Virtual overexpression screens with static cytokine pairs and after myocardial infarction predicted phase-specific regulators of collagen expression. Several regulators increased (Smad3) or decreased (Smad7, protein kinase G) collagen expression specifically in the proliferative phase. NADPH oxidase overexpression sustained collagen expression from proliferative to maturation phases, driven by TGFβ and endothelin positive feedback loops. Interleukin-1 overexpression suppressed collagen via NFκB and BAMBI (BMP and activin membrane-bound inhibitor) incoherent feedforward loops, but it then later sustained collagen expression due to the TGFβ positive feedback loop. These model-based predictions reveal network mechanisms by which the dynamics of paracrine stimuli and interacting signaling pathways drive the progression of fibroblast phenotypes and fibrosis after myocardial infarction.