Chih-Ying Wu

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. For excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthened the accuracy of results. Indicators such as area and Intersection over Union (IoU) are also provided for assessment. Among them, the mean Average Precision (mAP) for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

Artificial intelligence has garnered significant attention in recent years as a rapidly advancing field of computer technology. With the continual advancement of computer hardware, deep learning has made breakthrough developments within the realm of artificial intelligence. Over the past few years, applying deep learning architecture in medicine and Industrial anomaly inspection has significantly contributed to solving numerous challenges related to efficiency and accuracy. Despite excellent results in radiological, pathological, endoscopic, ultrasonic, and biochemical examinations, this paper utilizes deep learning combined with image processing to identify spinal canal and vertebral foramen dimensions. In existing research, technologies such as corrosion and expansion in magnetic resonance image (MRI) processing have also strengthening the accuracy of results. Indicators such as area and Intersection over Union (IoU)are also provided for assessment. Among them, the mean average precision (mAP), for identifying intervertebral foramen (IVF) and intervertebral disc (IVD) through YOLOv4 is 95.6%. Resnet50 mixing U-Net was employed to identify the spinal canal and intervertebral foramen and achieved IoU scores of 79.11% and 80.89%.

BACKGROUND: Percutaneous endoscopic lumbar decompression is gaining attention as a minimally invasive surgery. Here, the authors report a rare complication of pneumocephalus caused by vacuum drain after biportal endoscopic spinal surgery (BESS) for lumbar stenosis. OBSERVATIONS: A 79-year-old woman with spinal stenosis over the L4–5 level received BESS. No visible dural tear was encountered during surgery, and a vacuum drain was placed after surgery. Approximately 150 mL of cerebrospinal fluid was drained on postoperative day 1. Simultaneously, symptoms of intracranial hypotension were noted. Brain computed tomography (CT) revealed pneumocephalus. The patient was advised to have bed rest and hydration, and her symptoms improved subsequently. Follow-up brain CT indicated the resolution of pneumocephalus. LESSONS: Pneumocephalus after endoscopic lumbar surgery is rare. Dural tear, high rate of normal saline irrigation, and vacuum drain placement are the associated risk factors.

The low-density lipoprotein receptor (LDLR) gene has been reported to be associated with cerebral infarction. This study aimed to explore 2 genetic LDLR variants, rs688 and rs5925, for their potential roles in cerebral infarction. This genetic association study was conducted within an isolated Taiwanese population; 815 ischemic stroke patients (431 with atherothrombotic stroke and 384 with lacunar infarction) and 430 normal controls were enrolled. There was no significant difference in the genetic frequency of rs688 and rs5925 between the control group and overall ischemic stroke, atherothrombotic stroke, or lacunar infarct groups. However, when analyzing the association between the haplotypes related to rs688 and rs5925 and cerebral ischemic stroke, the most common haplotype allele CT was used as the reference allele, and the haplotype TC associated with a 65% increased risk of overall ischemic stroke, 72% increased risk of atherothrombotic stroke, and 70% increased risk of lacunar infarction; this indicated a synergistic effect between these 2 single-nucleotide polymorphisms. The LDLR analysis based on the haplotypes rs688 and rs5925 was conducted in a Taiwanese population and provided preliminary evidence suggesting that genetic polymorphisms of LDLR are associated with cerebral infarction.