Artificial Intelligence for Ultrasound-Guided Peripheral Nerve and Plane Block Procedures: Assistive Tool for Medical Image Interpretation
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 40
- 试验地点
- 1
- 主要终点
- Validation of real-time identification of anatomical landmarks associated with selected peripheral nerve and plane blocks via AI supported ultrasound practice
研究概览
简要总结
The goal of this observational study is to test the accuracy of an artificial intelligence tool used for identifying ultrasound-guided block regions in healthy volunteer participants. The main question aims to answer is:
• Is the artificial intelligence tool effective for identifying selected ultrasound-guided nerve block regions and their anatomical landmarks?
Three anesthesiology trainees perform ultrasound scanning for 8 nerve block regions on participants. Peripheral nerve and plane block regions are;
- Adductor canal block region
- Axillary brachial plexus block region
- ESP (erector spinae plane) block region
- Femoral block region
- PECS (pectoral) block region
- Popliteal block region
- Rectus sheath block region
- Superficial cervical plexus block region
详细描述
Sonoanotomy knowledge is essential for ultrasound-guided regional anesthesia (UGRA) procedures. We aimed to assess the accuracy of artificial intelligence (AI) software used to assist sonoanatomy interpretation by highlighting anatomical structures in peripheral nerve and plane blocks in recognizing anatomical structures.
All scans were performed with an ultrasound device (GE Logiq, Wisconsin, USA) having AI software (Nerveblox, Smart Alfa Teknoloji San. Ve Tic. A.Ş., Ankara, Turkey). Using this setup, when a user performs an ultrasound scan, the AI software provides the user with real-time feedback about the identification of anatomical structures/landmarks.
The AI software is designed to provide three major feedback signals to the user in real-time;
- name tags for each anatomical structure
- color overlays for each anatomical structure
- scan success rate for the entire image
Color overlays and name tags are transparency-adjusted highlights and dots that provide the user with more general spatial feedback on the anatomical layout. The plane completeness rate is visualized with a "scan success" gauge, which guides the user in a way that shows how close the current image is to the ideal visualization of predefined landmarks.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Only
- 时间视角
- Prospective
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 是
入选标准
- •Volunteers over the age 18
排除标准
- •anatomical deformity in the selected regions
- •psychiatric or neurological diseases that would impair understanding of the consent form
- •inability to lie flat
结局指标
主要结局
Validation of real-time identification of anatomical landmarks associated with selected peripheral nerve and plane blocks via AI supported ultrasound practice
时间窗: After collecting and saving all scans/images performed by the anesthesiology trainees in one day, rating/scoring of all these saved raw and highlighted ultrasound scans/images by the experts in one day, single point
In 40 healthy volunteer participants, AI supported ultrasound was used to scan each peripheral nerve and plane block to highlight the block-specific anatomical landmarks (by the three anesthesiology trainees). Then, expert practitioners score/rate the accuracy of color overlays using a 6-point scale (between 0 to 5) for a total of 4,440 anatomical landmarks by assessing raw and highlighted ultrasound images.
次要结局
- Difference according to BMI and gender(After saving all ultrasound scans/images in one day, rating/scoring in one day, single point,)
研究者
Berrin Gunaydin
Prof (MD,PhD)
Gazi University