Artificial intelligence enhanced ultrasound (AI-US) in a severe obese parturient: a case report

Abstract

Background 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. Case presentation We present the case of a parturient with extremely severe obesity, with a Body Mass Index (BMI) = 64.5 kg/m2, in which the AI-Enabled Image Recognition allowed a successful placing of an epidural catheter. Conclusions 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.

Artificial intelligence for ultrasound scanning in regional anaesthesia: a scoping review of the evidence from multiple disciplines

“… 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 Around this point, there has been an increase in published data where the dominant technique was deep learning for both PNB 9 , 11 , 12 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 and CNB, 7 , 74 , 75 , 76 , 77 though there are some exceptions. 78 , 79 , 80 A number of sources do not explicitly state the techniques used; these are typically clinical case reports or (external) validation studies describing accuracy or efficacy 81 , 82 , 83 , 84 , 85 , 86 , 87 ,…”

Evolving the Era of 5D Ultrasound? A Systematic Literature Review on the Applications for Artificial Intelligence Ultrasound Imaging in Obstetrics and Gynecology

“…The study of Cho et al proposed a model for the automated estimation of amniotic fluid, as it is known to be a particular observer-dependent factor and, therefore, benefits from automation [179]. Compagnone et al presented a clinical case report of a successful AI-image-guided placement of an epidural catheter in an extremely obese patient for delivery [180]. The research group Maraci et al developed an AI model to detect the fetal position and heart beat from predefined US sweeps.…”

“…Another example of direct patient benefit is the finding that in high-risk patients with ectopic pregnancies, reduced timing of diagnosis may result in an improved outcome [224]. AI models can also help to increase diagnostic accuracy, for example when US image quality is impeded by a thickened abdominal wall in obese patients [180]. Moreover, the advantages in pre-operative risk stratification or intraoperative assistance are described in both subspecialties of OB/GYN, e.g., in pre-operative endometrial cancer staging [209] and for fetoscopic surgical interventions [69,127].…”

Recent advances in system management, decision support systems, artificial intelligence and computing in anaesthesia

“…A review of the efficacy of ScanNav in modern practice revealed that it enhances training for non-specialists and instruction for specialists in the field of ultrasound-guided regional anaesthesia (USGRA). [ 15 – 17 ] Artificial intelligence enhanced ultrasound (AI-US) has been found to be more effective than conventional ultrasound in performing a central neuraxial block by identifying the anatomical structures, automatically estimating the depth of the epidural space, calculating the point of needle entry and angle of insertion and shortening the duration of block. Thus, there are many applications of AI in clinical anaesthesia practice [ Table 2 ].…”