Machine Learning-Based Continuous Intracranial Pressure Prediction for Traumatic Injury Patients.

Structured Abstract-Objective: Abnormal elevation of intracranial pressure (ICP) can cause dangerous or even fatal outcomes. The early detection of high intracranial pressure events can be crucial in saving lives in an intensive care unit (ICU). Despite many applications of machine learning (ML) techniques related to clinical diagnosis, ML applications for continuous ICP detection or short-term predictions have been rarely reported. This study proposes an efficient method of applying an artificial recurrent neural network on the early prediction of ICP evaluation continuously for TBI patients. Methods: After ICP data preprocessing, the learning model is generated for thirteen patients to continuously predict the ICP signal occurrence and classify events for the upcoming 10 minutes by inputting the previous 20-minutes of the ICP signal. Results: As the overall model performance, the average accuracy is 94.62%, the average sensitivity is 74.91%, the average specificity is 94.83%, and the average root mean square error is approximately 2.18 mmHg. Conclusion: This research addresses a significant clinical problem with the management of traumatic brain injury patients. The machine learning model data enables early prediction of ICP continuously in a real-time fashion, which is crucial for appropriate clinical interventions. The results show that our machine learning-based model has high adaptive performance, accuracy, and efficiency.

Automated Cell Foreground-Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data.

Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated cell segmentation method involving morphological operations to automatically achieve cell segmentation for phase-contrast microscopes. Manual/visual counting of cell segmentation serves as the control group (156 images as ground truth) to evaluate the proposed method's performance. The proposed technology's adaptive performance is assessed at varying conditions, including artificial blurriness, illumination, and image size. Compared to the Trainable Weka Segmentation method, the Empirical Gradient Threshold method, and the ilastik segmentation software, the proposed method achieved better segmentation accuracy (dice coefficient: 90.07, IoU: 82.16%, and 6.51% as the average relative error on measuring cell area). The proposed method also has good reliability, even under unfavored imaging conditions at which manual labeling or human intervention is inefficient. Additionally, similar degrees of segmentation accuracy were confirmed when the ground truth data and the generated data from the proposed method were applied individually to train modified U-Net models (16848 images). These results demonstrated good accuracy and high practicality of the proposed cell segmentation method with phase-contrast microscopy image data.

Simulation-Driven Machine Learning for Predicting Stent Expansion in Calcified Coronary Artery.

In this work, we integrated finite element (FE) method and machine learning (ML) method to predict the stent expansion in a calcified coronary artery. The stenting procedure was captured in a patient-specific artery model, reconstructed based on optical coherence tomography images. Following FE simulation, eight geometrical features in each of 120 cross sections in the pre-stenting artery model, as well as the corresponding post-stenting lumen area, were extracted for training and testing the ML models. A linear regression model and a support vector regression (SVR) model with three different kernels (linear, polynomial, and radial basis function kernels) were adopted in this work. Two subgroups of the eight features, i.e., stretch features and calcification features, were further assessed for the prediction capacity. The influence of the neighboring cross sections on the prediction accuracy was also investigated by averaging each feature over eight neighboring cross sections. Results showed that the SVR models provided better predictions than the linear regression model in terms of bias. In addition, the inclusion of stretch features based on mechanistic understanding could provide a better prediction, compared with the calcification features only. However, there were no statistically significant differences between neighboring cross sections and individual ones in terms of the prediction bias and range of error. The simulation-driven machine learning framework in this work could enhance the mechanistic understanding of stenting in calcified coronary artery lesions, and also pave the way toward precise prediction of stent expansion.