Diagnosis of Hippocampal Sclerosis in Children: Comparison of Automated Brain MRI Volumetry and Readers of Varying Experience

Hippocampal sclerosis: A review on current research status and its mechanisms

Hippocampal sclerosis (HS) is a pathological condition characterized by significant loss of hippocampal neurons and gliosis. This condition represents the most common neuropathological change observed in patients with temporal lobe epilepsy (TLE) and is also found in aging individuals. TLE related to HS is the most prevalent type of drug-resistant epilepsy in adults, and its underlying mechanisms are not yet fully understood. Therefore, developing improved methods for predicting and treating drug-resistant patients with TLE-HS is crucial. Patients with TLE-HS often experience cognitive impairment and psychological comorbidities, significantly affecting their quality of life. Consequently, a thorough review of the current research status of TLE-HS is essential, focusing on its prediction, diagnosis, treatment, and underlying mechanisms. The hippocampus plays a pivotal role in memory and cognition. HS of aging (HS-Aging), a condition linked to dementia in the ultra-elderly, is marked by severe CA1 (cornu ammonis) neuronal loss and frequent transactive response DNA-binding protein of 43 kDa (TDP-43) proteinopathy, often misdiagnosed as Alzheimer's disease (AD). Nonetheless, clinical characteristics and patterns of hippocampal atrophy can help differentiate between the two disorders. This review aims to provide a comprehensive overview of the pathological features of HS, the relevant mechanisms underlying TLE-HS and HS-Aging, current imaging diagnostic techniques, including machine learning, and available treatment modalities. It also explores the prognosis and comorbidities related to these conditions. Future research directions include establishing animal models to clarify the poorly understood mechanisms underlying HS, particularly those related to emotional processing. Investigating post-HS behavioral and cognitive changes in these models will lay the foundation for further advancements in this field. This review is a cornerstone for future investigations and suggests additional research endeavors.

FDA-approved machine learning algorithms in neuroradiology: A systematic review of the current evidence for approval

Over the past decade, machine learning (ML) and artificial intelligence (AI) have become increasingly prevalent in the medical field. In the United States, the Food and Drug Administration (FDA) is responsible for regulating AI algorithms as "medical devices" to ensure patient safety. However, recent work has shown that the FDA approval process may be deficient. In this study, we evaluate the evidence supporting FDA-approved neuroalgorithms, the subset of machine learning algorithms with applications in the central nervous system (CNS), through a systematic review of the primary literature. Articles covering the 53 FDA-approved algorithms with applications in the CNS published in PubMed, EMBASE, Google Scholar and Scopus between database inception and January 25, 2022 were queried. Initial searches identified 1505 studies, of which 92 articles met the criteria for extraction and inclusion. Studies were identified for 26 of the 53 neuroalgorithms, of which 10 algorithms had only a single peer-reviewed publication. Performance metrics were available for 15 algorithms, external validation studies were available for 24 algorithms, and studies exploring the use of algorithms in clinical practice were available for 7 algorithms. Papers studying the clinical utility of these algorithms focused on three domains: workflow efficiency, cost savings, and clinical outcomes. Our analysis suggests that there is a meaningful gap between the FDA approval of machine learning algorithms and their clinical utilization. There appears to be room for process improvement by implementation of the following recommendations: the provision of compelling evidence that algorithms perform as intended, mandating minimum sample sizes, reporting of a predefined set of performance metrics for all algorithms and clinical application of algorithms prior to widespread use. This work will serve as a baseline for future research into the ideal regulatory framework for AI applications worldwide.