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In recent years, the development of implantable bioelectronics has garnered significant attention. With the continuous advancement of IoT and information technology, implantable bioelectronics can be utilized more effectively for health monitoring to enhance treatment outcomes, reduce healthcare costs, and improve quality of life. Implantable energy storage devices have been widely studied as critical components for energy supply. Conventional power sources are bulky, inflexible, and potentially contain materials that are dangerous to the body. Meanwhile, human tissues are soft, flexible, dynamic, and closed, which puts new requirements on energy storage devices to improve the safety, stability, and matching of implantable batteries or supercapacitors. Herein, recent advances in state‐of‐the‐art nonconventional power options for implantable electronics, specifically biocompatible, miniaturized, stretchable/deformable, biodegradable/bioresorbable, edible, and injectable energy storage devices, are reviewed in this paper. The material strategy and architectural design of the next‐generation implantable energy storage device are discussed, including the selection principle of electrolytes, the all‐in‐one structure design strategy, and the way to realize self‐charging. Finally, the challenges and prospects of emerging design strategies toward developing next‐generation implantable batteries and supercapacitors for the future are put forward.

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Background. Intracranial solitary fibrous tumor(SFT)/hemangiopericytoma (HPC) is an aggressive malignant tumor originating from the intracranial vasculature. Angiomatous meningioma (AM) is a benign tumor with a good prognosis. The imaging manifestations of the two are very similar. Thus, novel noninvasive diagnostic method is urgently needed in clinical practice. Texture analysis and model building through machine learning may have good prospects. Aim. To evaluate whether a 3D-MRI texture feature model could be used to differentiate malignant intracranial SFT/HPC from AM. Method. A total of 97 patients with SFT/HPC and 95 with AM were included in this study. Patients from each group were randomly divided into the train (70%) and test (30%) sets. ROIs were drawn along the edge of the tumor on each section of T1WI, T2WI, and contrasted T1WI using ITK-SNAP software. The segmented image was imported into the AK software for texture feature extraction, and the 3D ROI signal intensity histograms of T1WI, T2WI, and contrasted T1WI were automatically obtained along with all the parameters. Modeling was performed using the language R. Confusion matrix was used to analyze the accuracy of the model. ROC curve was constructed to assess the grading ability of the logistic regression model. Results. After Lasso dimension reduction, 5, 9, and 7 texture features were extracted from T1WI, T2WI, and contrasted T1WI, respectively; additional 8 texture features were extracted from the combined sequence for modeling. The ROC analyses on four models resulted in an area under the curve (AUC) of 0.885 (sensitivity 76.1%, specificity 87.9%) for T1WI model, 0.918 (73.1%, 95.5%) for T2WI model, 0.815 (55.2%, 93.9%) for contrasted T1WI model, and 0.959 (92.5%, 84.8%) for the combined sequence model and were enough to correctly distinguish the two groups in 71.2%, 81.4%, 69.5%, and 83.1% of cases in test set, respectively. Conclusions. The radiological model based on texture features could be used to differentiate SFT/HPC from AM.