AIMC Topic: Adult

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A supervised machine learning approach for predicting the need for postsurgical intervention in acromegaly.

Neurosurgical focus
OBJECTIVE: Patients with growth hormone (GH)-secreting pituitary adenomas (PAs) experience various symptoms and comorbidities, which can ultimately lead to increased mortality. This study aimed to develop and validate a machine learning (ML) model fo...

A novel deep learning system for automated diagnosis and grading of lumbar spinal stenosis based on spine MRI: model development and validation.

Neurosurgical focus
OBJECTIVE: The study aimed to develop a single-stage deep learning (DL) screening system for automated binary and multiclass grading of lumbar central stenosis (LCS), lateral recess stenosis (LRS), and lumbar foraminal stenosis (LFS).

A multiregional multimodal machine learning model for predicting outcome of surgery for symptomatic hemorrhagic brainstem cavernous malformations.

Neurosurgical focus
OBJECTIVE: Given that resection of brainstem cavernous malformations (BSCMs) ends hemorrhaging but carries a high risk of neurological deficits, it is necessary to develop and validate a model predicting surgical outcomes. This study aimed to constru...

Generation of synthetic CT-like imaging of the spine from biplanar radiographs: comparison of different deep learning architectures.

Neurosurgical focus
OBJECTIVE: This study compared two deep learning architectures-generative adversarial networks (GANs) and convolutional neural networks combined with implicit neural representations (CNN-INRs)-for generating synthetic CT (sCT) images of the spine fro...

Phenotype-driven risk stratification of cerebral aneurysms using Shapley Additive Explanations-based supervised clustering: a novel approach to rupture prediction.

Neurosurgical focus
OBJECTIVE: The aim of this study was to address the limitations of traditional aneurysm risk scoring systems and computational fluid dynamics (CFD) analyses by applying a supervised clustering framework to identify distinct aneurysm phenotypes and im...

Open-source AI-assisted rapid 3D color multimodal image fusion and preoperative augmented reality planning of extracerebral tumors.

Neurosurgical focus
OBJECTIVE: This study aimed to develop an advanced method for preoperative planning and surgical guidance using open-source artificial intelligence (AI)-assisted rapid 3D color multimodal image fusion (MIF) and augmented reality (AR) in extracerebral...

Does alignment alone predict mechanical complications after adult spinal deformity surgery? A machine learning comparison of alignment, bone quality, and soft tissue.

Neurosurgical focus
OBJECTIVE: Mechanical complications are a vexing occurrence after adult spinal deformity (ASD) surgery. While achieving ideal spinal alignment in ASD surgery is critical, alignment alone may not fully explain all mechanical complications. The authors...

Fully automatic anatomical landmark localization and trajectory planning for navigated external ventricular drain placement.

Neurosurgical focus
OBJECTIVE: The aim of this study was to develop and validate a fully automatic anatomical landmark localization and trajectory planning method for external ventricular drain (EVD) placement using CT or MRI.

Image-based detection of the internal carotid arteries and sella turcica in endoscopic endonasal transsphenoidal surgery.

Neurosurgical focus
OBJECTIVE: Endoscopic endonasal transsphenoidal surgery (EETS) is a minimally invasive procedure that accesses the sellar and parasellar regions. Various anatomical structures must be identified during the operation, particularly the sella turcica an...

Fundus Refraction Offset as a Personalized Biomarker for 12-Year Risk of Retinal Detachment.

Investigative ophthalmology & visual science
PURPOSE: The purpose of this study was to investigate the potential of a novel anatomical metric of ametropia-fundus refraction offset (FRO)-in stratifying the risk of retinal detachment (RD) or breaks, beyond the influence of risk factors including ...