首页|Beihang University Reports Findings in Artificial Intelligence (Automatic Segmen tation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intell igence)
Beihang University Reports Findings in Artificial Intelligence (Automatic Segmen tation of Ultrasound-Guided Quadratus Lumborum Blocks Based on Artificial Intell igence)
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2024 OCT 03 (NewsRx)-By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Artificial Intelligenc e is the subject of a report. According to news reporting from Beijing, People's Republic of China, by NewsRx journalists, research stated, "Ultrasoundguided q uadratus lumborum block (QLB) technology has become a widely used perioperative analgesia method during abdominal and pelvic surgeries. Due to the anatomical co mplexity and individual variability of the quadratus lumborum muscle (QLM) on ul trasound images, nerve blocks heavily rely on anesthesiologist experience." The news correspondents obtained a quote from the research from Beihang Universi ty, "Therefore, using artificial intelligence (AI) to identify different tissue regions in ultrasound images is crucial. In our study, we retrospectively collec ted 112 patients (3162 images) and developed a deep learning model named Q-VUM, which is a U-shaped network based on the Visual Geometry Group 16 (VGG16) networ k. Q-VUM precisely segments various tissues, including the QLM, the external obl ique muscle, the internal oblique muscle, the transversus abdominis muscle (coll ectively referred to as the EIT), and the bones. Furthermore, we evaluated Q-VUM . Our model demonstrated robust performance, achieving mean intersection over un ion (mIoU), mean pixel accuracy, dice coefficient, and accuracy values of 0.734, 0.829, 0.841, and 0.944, respectively. The IoU, recall, precision, and dice coe fficient achieved for the QLM were 0.711, 0.813, 0.850, and 0.831, respectively. Additionally, the Q-VUM predictions showed that 85% of the pixels in the blocked area fell within the actual blocked area. Finally, our model exh ibited stronger segmentation performance than did the common deep learning segme ntation networks (0.734 vs. 0.720 and 0.720, respectively). In summary, we propo sed a model named Q-VUM that can accurately identify the anatomical structure of the quadratus lumborum in real time."
BeijingPeople's Republic of ChinaAsi aArtificial IntelligenceEmerging TechnologiesMachine Learning
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