Continuous intracranial pressure (ICP) monitoring is a cornerstone of neurocritical care after severe brain injuries such as traumatic brain injury and acts as a biomarker of secondary brain injury. With the rapid development of artificial intelligent (AI) approaches to data analysis, the acquisition, storage, real-time analysis, and interpretation of physiological signal data can bring insights to the field of neurocritical care bioinformatics. We review the existing literature on the quantification and analysis of the ICP waveform and present an integrated framework to incorporate signal processing tools, advanced statistical methods, and machine learning techniques in order to comprehensively understand the ICP signal and its clinical importance. Our goals were to identify the strengths and pitfalls of existing methods for data cleaning, information extraction, and application. In particular, we describe the use of ICP signal analytics to detect intracranial hypertension and to predict both short-term intracranial hypertension and long-term clinical outcome. We provide a well-organized roadmap for future researchers based on existing literature and a computational approach to clinically-relevant biomedical signal data.

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Automatic and accurate instance segmentation of teeth can provide important support for computer-aided orthodontic work. Traditional methods for tooth segmentation studies often ignore the rich structural features of teeth. Capturing the complete and accurate geometry as well as morphological details of a single tooth remains a challenge for current tooth segmentation studies. In this article, a new tooth segmentation deeplearning network based on capturing dependencies and receptive field adjustment in cone beam computed tomography (CBCT) is proposed to achieve automatic and accurate instance segmentation of dental CBCT data. The method acquires coarse-level features of tooth and accurate tooth centroids in the first stage, and acquires the instance information and spatial position localization of the tooth. The encoding process in the second stage of the network introduces a guidance module for obtaining tooth geometry information based on a 3D self-attention mechanism to capture dependencies in CBCT. The proposed tooth feature integration module is based on multiscale fusion of dilated convolutions to capture tooth detailed information at multiple scales, and the network receptive field was adjusted. Extensive evaluation, ablation, and comparison experiments demonstrate that our method exhibits state-of-the-art segmentation performance and accurate instance segmentation results, reflecting their potential applicability in clinical medicine.

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