Leveraging the smarts in your phone: An artificial intelligence-driven iOS application for neurosurgical navigation of external ventricular drains

Abstract

External ventricular drain (EVD) placement is a critical neurosurgical procedure traditionally performed freehand, with inherent risks of malposition, infection, and hemorrhage. Recent advances in artificial intelligence (AI), particularly in medical imaging and real-time computer vision, have enabled the development of portable navigation tools that may enhance accuracy, safety, and bedside accessibility. This study evaluated whether iOS devices equipped with a TrueDepth camera could perform real-time object and facial recognition, tracking, and semantic segmentation of computed tomography (CT) scans for non-immobilized heads to guide EVD placement via a custom AI-driven application. A custom iOS application was developed to provide a complete, real-time surgical navigation experience on an iPhone or iPad Pro. Three AI models were trained, tuned, and validated: a semantic segmentation model for brain anatomy, a semantic segmentation model for facial features, and an object detection model for a custom EVD stylet attachment. GPU programming accelerated on-device real-time, continuous registration while optimizing power consumption. A UNet convolutional neural network trained on eight 1 mm head CTs achieved 98.3% testing and 98.2% validation accuracy using a 50/50 test–validation split, segmenting a thin-cut CT in 3 s on an iPhone 12 Pro. Point cloud merging of patient anatomy took 4 seconds with an initial depth scan of 30,000 points, updating in real time with a cumulative error of 1 × 10-8 cm. Transfer learning-powered EVD tracking, trained for 1,000 epochs, achieved an intersection over union of 1.0 and 0.98 for the detection model, with inference times of 800 μs on Apple’s Neural Engine. This feasibility study demonstrates that iOS devices with TrueDepth cameras can enable real-time, continuous surgical navigation for EVD stylets.

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