Compagnone Christian, Borrini Giulia, Calabrese Alberto, Taddei Mario, Bellini Valentina, Bignami Elena
Anesthesiology, Critical Care, and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.
Ultrasound J. 2022 Aug 3;14(1):34. doi: 10.1186/s13089-022-00283-5.
Neuraxial anesthesia in obese parturients can be challenging due to anatomical and physiological modifications secondary to pregnancy; this led to growing popularity of spine ultrasound in this population for easing landmark identification and procedure execution. Integration of Artificial Intelligence with ultrasound (AI-US) for image enhancement and analysis has increased clinicians' ability to localize vertebral structures in patients with challenging anatomical conformation.
We present the case of a parturient with extremely severe obesity, with a Body Mass Index (BMI) = 64.5 kg/m, in which the AI-Enabled Image Recognition allowed a successful placing of an epidural catheter.
Benefits gained from AI-US implementation are multiple: immediate recognition of anatomical structures leads to increased first-attempt success rate, making easier the process of spinal anesthesia execution compared to traditional palpation methods, reducing needle placement time for spinal anesthesia and predicting best needle direction and target structure depth in peridural anesthesia.
由于妊娠引起的解剖和生理改变,肥胖产妇的椎管内麻醉具有挑战性;这使得脊柱超声在该人群中越来越受欢迎,有助于确定解剖标志和实施操作。人工智能与超声(AI-US)相结合用于图像增强和分析,提高了临床医生在解剖结构具有挑战性的患者中定位椎体结构的能力。
我们报告一例极度肥胖的产妇,体重指数(BMI)=64.5kg/m²,通过人工智能图像识别成功置入硬膜外导管。
实施AI-US带来的益处是多方面的:能够立即识别解剖结构可提高首次尝试成功率,与传统触诊方法相比,使脊髓麻醉的实施过程更容易,减少脊髓麻醉的穿刺时间,并预测硬膜外麻醉的最佳穿刺方向和目标结构深度。