Artificial intelligence using deep neural network learning for automatic location of the interscalene brachial plexus in ultrasound images

Abstract

Background: Identifying the interscalene brachial plexus can be challenging during ultrasound-guided interscalene block.

Objective: We hypothesised that an algorithm based on deep learning could locate the interscalene brachial plexus in ultrasound images better than a nonexpert anaesthesiologist, thus possessing the potential to aid anaesthesiologists.

Design: Observational study.

Setting: A tertiary hospital in Shanghai, China.

Patients: Patients undergoing elective surgery.

Interventions: Ultrasound images at the interscalene level were collected from patients. Two independent image datasets were prepared to train and evaluate the deep learning model. Three senior anaesthesiologists who were experts in regional anaesthesia annotated the images. A deep convolutional neural network was developed, trained and optimised to locate the interscalene brachial plexus in the ultrasound images. Expert annotations on the datasets were regarded as an accurate baseline (ground truth). The test dataset was also annotated by five nonexpert anaesthesiologists.

Main outcome measures: The primary outcome of the research was the distance between the lateral midpoints of the nerve sheath contours of the model predictions and ground truth.

Results: The data set was obtained from 1126 patients. The training dataset comprised 11 392 images from 1076 patients. The test dataset constituted 100 images from 50 patients. In the test dataset, the median [IQR] distance between the lateral midpoints of the nerve sheath contours of the model predictions and ground truth was 0.8 [0.4 to 2.9] mm: this was significantly shorter than that between nonexpert predictions and ground truth (3.4 mm [2.1 to 4.5] mm; P < 0.001).

Conclusion: The proposed model was able to locate the interscalene brachial plexus in ultrasound images more accurately than nonexperts.

Trial registration: ClinicalTrials.gov (https://clinicaltrials.gov) identifier: NCT04183972.