Intracranial aneurysm location: potential influencing factor in ruptures with internal carotid artery occlusion.

Background And Purpose: Intracranial aneurysms combined with spontaneous internal carotid artery occlusion (ICAO) are a rare and serious vascular disorder. Currently, there is only limited information available on the clinical characteristics of these patients and the risk factors for aneurysm rupture. Our objective is to describe the clinical features of these patients and predict the risk factors for the rupture of unruptured intracranial aneurysms (UIAs) combined with ICAO.

Ruptured intracranial aneurysm with spontaneous occlusion of internal carotid artery: clinical characteristics, prognostic risk factors, and survival analysis.

Background: Ruptured intracranial aneurysm (RIA) combined with internal carotid artery occlusion (ICAO) is a rare and serious vascular condition. We aimed to describe the clinical characteristics and outcomes of these patients.

Methods: We retrospectively analyzed cases of RIA with concurrent spontaneous ICAO from the Chinese Multicenter Aneurysm Database (CMAD).

Swarm learning network for privacy-preserving and collaborative deep learning assisted diagnosis of fracture: a multi-center diagnostic study.

Background: While artificial intelligence (AI) has revolutionized medical diagnostics, conventional centralized AI models for medical image analysis raise critical concerns regarding data privacy and security. Swarm learning (SL), a decentralized machine learning framework, addresses these limitations by enabling collaborative model training through secure parameter aggregation while preserving data locality. However, no prior studies have specifically developed distributed learning models for fracture recognition due to challenges in multi-institutional data harmonization.

Revolutionizing brain‒computer interfaces: overcoming biocompatibility challenges in implantable neural interfaces.

Brain‒computer interfaces (BCIs) exhibit significant potential for various applications, including neurofeedback training, neurological injury management, and language, sensory and motor rehabilitation. Neural interfacing electrodes are positioned between external electronic devices and the nervous system to capture complex neuronal activity data and promote the repair of damaged neural tissues. Implantable neural electrodes can record and modulate neural activities with both high spatial and high temporal resolution, offering a wide window for neuroscience research.

Natural course of unruptured intracranial aneurysms: a case surveillance study in China.

Background: The natural course of unruptured intracranial aneurysms (UIAs) has been well described in developed countries, but there is a lack of large studies on UIAs in China. This article aims to fill this gap by detailing the current status and natural course of UIAs in China and identifying the major risk factors for their rupture, providing a basis for clinical decision-making.

Methods: We included all patients with UIAs consecutively admitted to 12 tertiary care centers in 4 provinces in northern China between January 2017 and December 2020.

Construction of a Multimodal 3D Atlas for a Micrometer-Scale Brain-Computer Interface Based on Mixed Reality.

Objective: To develop a multimodal imaging atlas of a rat brain-computer interface (BCI) that incorporates brain, arterial, bone tissue and a BCI device using mixed reality (MR) for three-dimensional (3D) visualization.

Methods: An invasive BCI was implanted in the left visual cortex of 4-week-old Sprague-Dawley rats. Multimodal imaging techniques, including micro-CT and 9.

Can some algorithms of machine learning identify osteoporosis patients after training and testing some clinical information about patients?

Objective: This study was designed to establish a diagnostic model for osteoporosis by collecting clinical information from patients with and without osteoporosis. Various machine learning algorithms were employed for training and testing the model, evaluating its performance, and conducting validations to explore the most suitable machine learning algorithm.

Methods: Clinical information, including demographic data, examination results, medical history, and laboratory test results, was collected from inpatients with and without osteoporosis.

Dual-Stream Attention-Based Classification Network for Tibial Plateau Fractures via Diffusion Model Augmentation and Segmentation Map Integration.

Objective: This study aimed to explore a novel method that integrates the segmentation guidance classification and the diffusion model augmentation to realize the automatic classification for tibial plateau fractures (TPFs).

Methods: YOLOv8n-cls was used to construct a baseline model on the data of 3781 patients from the Orthopedic Trauma Center of Wuhan Union Hospital. Additionally, a segmentation-guided classification approach was proposed.

Integrating blockchain technology with artificial intelligence for the diagnosis of tibial plateau fractures.

Purpose: The application of artificial intelligence (AI) in healthcare has seen widespread implementation, with numerous studies highlighting the development of robust algorithms. However, limited attention has been given to the secure utilization of raw data for medical model training, and its subsequent impact on clinical decision-making and real-world applications. This study aims to assess the feasibility and effectiveness of an advanced diagnostic model that integrates blockchain technology and AI for the identification of tibial plateau fractures (TPFs) in emergency settings.

Artificial Intelligence in Medical Metaverse: Applications, Challenges, and Future Prospects.

The medical metaverse is a combination of medicine, computer science, information technology and other cutting-edge technologies. It redefines the method of information interaction about doctor-patient communication, medical education and research through the integration of medical data, knowledge and services in a virtual environment. Artificial intelligence (AI) is a discipline that uses computer technology to study and develop human intelligence.

Application and Prospects of Deep Learning Technology in Fracture Diagnosis.

Artificial intelligence (AI) is an interdisciplinary field that combines computer technology, mathematics, and several other fields. Recently, with the rapid development of machine learning (ML) and deep learning (DL), significant progress has been made in the field of AI. As one of the fastest-growing branches, DL can effectively extract features from big data and optimize the performance of various tasks.

Performance Assessment of GPT 4.0 on the Japanese Medical Licensing Examination.

Objective: To evaluate the accuracy and parsing ability of GPT 4.0 for Japanese medical practitioner qualification examinations in a multidimensional way to investigate its response accuracy and comprehensiveness to medical knowledge.

Methods: We evaluated the performance of the GPT 4.

Does a Video-Based and 3D Animation Hybrid Learning System Improve Teaching Outcomes in Orthopedic Surgery? A Randomized Controlled Trial.

Objective: This study aims to evaluate the instructional efficacy of a 3D Surgical Training System (3DSTS), which combines real surgical footage with high-definition 3D animations, against conventional surgical videos and textbooks in the context of orthopedic proximal humerus fracture surgeries.

Design: Before the experiment, 89 participants completed a pre-educational knowledge assessment. They were then randomized into 3 groups: the 3DSTS group (n = 30), the surgical video (SV) group (n = 29), and the textbook group (n = 30).

Is there an alternative to the Delbet-Colonna classification? Introduction and reliability assessment of a new classification system for paediatric femoral neck fractures: preliminary results.

Purpose: The purpose of this study was to introduce a new classification system for paediatric femoral neck fractures (PFNFs) and to evaluate its reliability.

Methods: Two hundred and eight unilateral PFNFs (mean patient age: 9.0 ± 4.

Deep learning system assisted detection and localization of lumbar spondylolisthesis.

Explore a new deep learning (DL) object detection algorithm for clinical auxiliary diagnosis of lumbar spondylolisthesis and compare it with doctors' evaluation to verify the effectiveness and feasibility of the DL algorithm in the diagnosis of lumbar spondylolisthesis. Lumbar lateral radiographs of 1,596 patients with lumbar spondylolisthesis from three medical institutions were collected, and senior orthopedic surgeons and radiologists jointly diagnosed and marked them to establish a database. These radiographs were randomly divided into a training set ( = 1,117), a validation set ( = 240), and a test set ( = 239) in a ratio of 0.

Deep learning-based algorithm improves radiologists' performance in lung cancer bone metastases detection on computed tomography.

Purpose: To develop and assess a deep convolutional neural network (DCNN) model for the automatic detection of bone metastases from lung cancer on computed tomography (CT).

Methods: In this retrospective study, CT scans acquired from a single institution from June 2012 to May 2022 were included. In total, 126 patients were assigned to a training cohort (n = 76), a validation cohort (n = 12), and a testing cohort (n = 38).

Laminin α1 as a target for the treatment of epidural fibrosis by regulating fibrotic mechanisms.

Excessive proliferation and migration of fibroblasts in the lumbar laminectomy area can lead to epidural fibrosis, eventually resulting in failed back surgery syndrome. It has been reported that laminin α1, a significant biofunctional glycoprotein in the extracellular matrix, is involved in several fibrosis‑related diseases, such as pulmonary, liver and keloid fibrosis. However, the underlying mechanism of laminin α1 in epidural fibrosis remains unknown.

HSA-templated self-generation of gold nanoparticles for tumor vaccine delivery and combinational therapy.

Drug delivery systems (DDS) play a vital role in the construction of tumor vaccines and can promote their therapeutic effect. Taking advantage of the versatile binding sites and bioreduction ability of human serum albumin (HSA), Au ions could be absorbed, reduced and nucleated to generate gold nanoparticles (AuNPs) on HSA without complicated intermediates, forming a DDS that can transform light to heat. Here, we designed self-generated AuNPs templated by HSA (HSA@AuNP).

Artificial intelligence to detect the femoral intertrochanteric fracture: The arrival of the intelligent-medicine era.

To explore a new artificial intelligence (AI)-aided method to assist the clinical diagnosis of femoral intertrochanteric fracture (FIF), and further compare the performance with human level to confirm the effect and feasibility of the AI algorithm. 700 X-rays of FIF were collected and labeled by two senior orthopedic physicians to set up the database, 643 for the training database and 57 for the test database. A Faster-RCNN algorithm was applied to be trained and detect the FIF on X-rays.

Applications of Mixed Reality Technology in Orthopedics Surgery: A Pilot Study.

The aim of this study is to explore the potential of mixed reality (MR) technology in the visualization of orthopedic surgery. The visualization system with MR technology is widely used in orthopedic surgery. The system is composed of a 3D imaging workstation, a cloud platform, and an MR space station.

Mesenchymal Stem Cell-Derived Exosomes as a Novel Strategy for the Treatment of Intervertebral Disc Degeneration.

Intervertebral disc degeneration (IVDD) has been reported to be the most prevalent contributor to low back pain, posing a significant strain on the healthcare systems on a global scale. Currently, there are no approved therapies available for the prevention of the progressive degeneration of intervertebral disc (IVD); however, emerging regenerative strategies that aim to restore the normal structure of the disc have been fundamentally promising. In the last decade, mesenchymal stem cells (MSCs) have received a significant deal of interest for the treatment of IVDD due to their differentiation potential, immunoregulatory capabilities, and capability to be cultured and regulated in a favorable environment.