■对演变或共存的特发性(IIH)和自发性颅内低血压(SIH)的漏诊通常是Chiari畸形(CM)大孔减压后症状持续或恶化的原因。我们首次在文献中探讨了人工智能(AI)/卷积神经网络(CNN)在ChiariI畸形中的联合作用,探索上游和下游磁共振发现作为CM-1的初始筛查剖面。我们还对CM的所有现有亚型进行了综述,并讨论了直立(重力辅助)磁共振成像(MRI)在评估平躺MRI上模棱两可的扁桃体下降中的作用。我们使用上游和下游分析器制定了工作流算法MaChiP1.0(ManjilaChiariProtocol1.0),导致ChiariI畸形从头或恶化,我们计划使用AI实现。
■PRISMA指南用于PubMed数据库文章中的“CM和机器学习和CNN”,遇到了四篇针对该主题的文章。IIH和SIH的放射学标准来自神经外科文献,它们适用于原发性和继发性(获得性)ChiariI畸形。使用现有的文献来表征上游病因,例如IIH或SIH,以及脊柱中孤立的下游病因。我们建议对IIH和SIH分别使用四个选定的标准,大脑和脊柱的MRIT2图像,大脑上游病因中主要是矢状序列,脊柱病变中主要是多平面MRI。
■使用MaChiP1.0(专利/版权未决)概念,我们已经提出了与渐进性ChiariI畸形有关的上游和下游剖面。上游分析器包括大脑下垂的发现,第三心室底的斜率,桥脑间角,mamillopontinedistance,侧脑室角,大脑内静脉-Galen角静脉,和iter的位移,clivus长度,扁桃体下降,等。,暗示SIH。在上游病理中注意到的IIH特征是眼球后部变平,部分空的西拉,视神经鞘变形,和MRI中的视神经弯曲。下游病因涉及硬膜撕裂引起的脊髓脑脊液(CSF)渗漏,脑膜憩室,脑脊液静脉瘘,等。
■人工智能将有助于提供上游和下游病因谱的预测性分析,确保治疗继发性(获得性)ChiariI畸形的安全性和有效性,尤其是与IIH和SIH共存。MaChiP1.0算法可以帮助记录先前诊断的CM-1的恶化,并找到继发性CM-I的确切病因。然而,后颅窝形态测量和cine-flowMRI数据对颅内CSF血流动力学的作用,随着先进的脊髓CSF研究使用动态脊髓CT扫描在继发性CM-I的形成仍在评估中。
UNASSIGNED: Missed diagnosis of evolving or coexisting idiopathic (IIH) and spontaneous intracranial hypotension (SIH) is often the reason for persistent or worsening symptoms after foramen magnum decompression for Chiari malformation (CM) I. We explore the role of artificial intelligence (AI)/convolutional neural networks (CNN) in Chiari I malformation in a combinatorial role for the first time in literature, exploring both upstream and downstream magnetic resonance findings as initial screening profilers in CM-1. We have also put together a review of all existing subtypes of CM and discuss the role of upright (gravity-aided) magnetic resonance imaging (MRI) in evaluating equivocal tonsillar descent on a lying-down MRI. We have formulated a workflow algorithm MaChiP 1.0 (Manjila Chiari Protocol 1.0) using upstream and downstream profilers, that cause de novo or worsening Chiari I malformation, which we plan to implement using AI.
UNASSIGNED: The PRISMA guidelines were used for \"CM and machine learning and CNN\" on PubMed database articles, and four articles specific to the topic were encountered. The radiologic criteria for IIH and SIH were applied from neurosurgical literature, and they were applied between primary and secondary (acquired) Chiari I malformations. An upstream etiology such as IIH or SIH and an isolated downstream etiology in the spine were characterized using the existing body of literature. We propose the utility of using four selected criteria for IIH and SIH each, over MRI T2 images of the brain and spine, predominantly sagittal sequences in upstream etiology in the brain and multiplanar MRI in spinal lesions.
UNASSIGNED: Using MaChiP 1.0 (patent/ copyright pending) concepts, we have proposed the upstream and downstream profilers implicated in progressive Chiari I malformation. The upstream profilers included findings of brain sagging, slope of the third ventricular floor, pontomesencephalic angle, mamillopontine distance, lateral ventricular angle, internal cerebral vein-vein of Galen angle, and displacement of iter, clivus length, tonsillar descent, etc., suggestive of SIH. The IIH features noted in upstream pathologies were posterior flattening of globe of the eye, partial empty sella, optic nerve sheath distortion, and optic nerve tortuosity in MRI. The downstream etiologies involved spinal cerebrospinal fluid (CSF) leak from dural tear, meningeal diverticula, CSF-venous fistulae, etc.
UNASSIGNED: AI would help offer predictive analysis along the spectrum of upstream and downstream etiologies, ensuring safety and efficacy in treating secondary (acquired) Chiari I malformation, especially with coexisting IIH and SIH. The MaChiP 1.0 algorithm can help document worsening of a previously diagnosed CM-1 and find the exact etiology of a secondary CM-I. However, the role of posterior fossa morphometry and cine-flow MRI data for intracranial CSF flow dynamics, along with advanced spinal CSF studies using dynamic myelo-CT scanning in the formation of secondary CM-I is still being evaluated.