Bilateral Vitiligo-Like Depigmentation of Choroid and Retinal Pigment Epithelium Associated with Ipilimumab-Nivolumab Therapy for Metastatic Cutaneous Melanoma

Introduction: Ipilimumab and nivolumab are checkpoint inhibitors that are known to cause a multitude of inflammatory ocular adverse events. Here we report a patient with poliosis and symptomatic depigmentation of the choroid and retinal pigment epithelium (RPE) associated with checkpoint inhibitor therapy for cutaneous melanoma. Case Presentation: The patient presented with floaters in both eyes and concerns for intraocular metastases of metastatic cutaneous melanoma after 1 month of therapy with ipilimumab and nivolumab. External examination revealed poliosis of her eyebrows and eyelashes. Fundus photography demonstrated multiple 1–3 disc-diameter hypopigmented placoid flat areas in the RPE/choroid exposing underlying choroidal vessels in both eyes. At subsequent evaluation 7 months later (after an additional 6 months of checkpoint inhibitor therapy), the lesions appeared more blanched. Evaluation nearly 20 months after the initial presentation showed no significant changes from her prior visit despite cessation of checkpoint inhibitor therapy for 13 months. Conclusion: Checkpoint inhibitor therapy for cutaneous melanoma metastases can cause depigmentation of the choroid and RPE that must be differentiated from progression of intraocular melanoma

Artificial Intelligence and Machine Learning in the Diagnosis and Management of Stroke: A Narrative Review of United States Food and Drug Administration-Approved Technologies

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

Landscape and future directions of machine learning applications in closed-loop brain stimulation

Abstract Brain stimulation (BStim) encompasses multiple modalities (e.g., deep brain stimulation, responsive neurostimulation) that utilize electrodes implanted in deep brain structures to treat neurological disorders. Currently, BStim is primarily used to treat movement disorders such as Parkinson’s, though indications are expanding to include neuropsychiatric disorders like depression and schizophrenia. Traditional BStim systems are “open-loop” and deliver constant electrical stimulation based on manually-determined parameters. Advancements in BStim have enabled development of “closed-loop” systems that analyze neural biomarkers (e.g., local field potentials in the sub-thalamic nucleus) and adjust electrical modulation in a dynamic, patient-specific, and energy efficient manner. These closed-loop systems enable real-time, context-specific stimulation adjustment to reduce symptom burden. Machine learning (ML) has emerged as a vital component in designing these closed-loop systems as ML models can predict / identify presence of disease symptoms based on neural activity and adaptively learn to modulate stimulation. We queried the US National Library of Medicine PubMed database to understand the role of ML in developing closed-loop BStim systems to treat epilepsy, movement disorders, and neuropsychiatric disorders. Both neural and non-neural network ML algorithms have successfully been leveraged to create closed-loop systems that perform comparably to open-loop systems. For disorders in which the underlying neural pathophysiology is relatively well understood (e.g., Parkinson’s, essential tremor), most work has involved refining ML models that can classify neural signals as aberrant or normal. The same is seen for epilepsy, where most current research has focused on identifying optimal ML model design and integrating closed-loop systems into existing devices. For neuropsychiatric disorders, where the underlying pathologic neural circuitry is still being investigated, research is focused on identifying biomarkers (e.g., local field potentials from brain nuclei) that ML models can use to identify onset of symptoms and stratify severity of disease