Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia

Bowness, James; El‐Boghdadly, Kariem; Woodworth, Glenn E.; Noble, J. Alison; Higham, Helen; Laurent, David Burckett-St

doi:10.1136/rapm-2021-103368

Cited by 38 publications

(37 citation statements)

References 16 publications

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“…Six experts in UGRA (AJRM, DBSL, CH, DP, TR, SW) participated in the study, either providing teaching or undertaking assessments of the participants. In accordance with our recent study in this area, 14 all experts had completed advanced training in UGRA and/or held a dedicated qualification in UGRA, regularly delivered direct clinical care using UGRA (including for ‘awake’ surgery), and regularly delivered teaching on UGRA (including advanced techniques).…”

Section: Methodsmentioning

confidence: 63%

Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia

Bowness¹,

Macfarlane²,

Laurent³

et al. 2023

British Journal of Anaesthesia

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Section: Methodsmentioning

confidence: 63%

Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia

Bowness¹,

Macfarlane²,

Laurent³

et al. 2023

British Journal of Anaesthesia

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“…Deep learning-based CADx systems have been utilized to determine the optimal views of ultrasound images of various anatomic structures, including fetal, 28 cardiac, 29 and breast 30 structures. Studies have evaluated the use of ultrasound videos to determine BP at the interscalene level 9,31 and to identify anatomic structures in several regions of the body. 32 however, to our knowledge, no studies to date have described methods for optimal view determination for SCB among patients undergoing ultrasound-guided regional anesthesia.…”

Section: Discussionmentioning

confidence: 99%

Optimal View Detection for Ultrasound-guided Supraclavicular Block using Deep Learning Approaches

Jo,

Lee,

Baek

et al. 2023

Preprint

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Background Successful ultrasound-guided supraclavicular block (SCB) requires the understanding of sonoanatomy and identification of the optimal view. Segmentation using a convolutional neural network (CNN) is limited in clearly determining the optimal view. The present study describes the development of a computer-aided diagnosis (CADx) system using a CNN that can determine the optimal view for complete SCB in real time.Objective The aim of this study was the development of computer-aided diagnosis system that aid non-expert to determine the optimal view for complete supraclavicular block in real time.Methods Ultrasound videos were retrospectively collected from 881 patients to develop the CADx system (600 to the training and validation set and 281 to the test set). The CADx system included classification and segmentation approaches, with Residual neural network (ResNet) and U-Net, respectively, applied as backbone networks. In the classification approach, an ablation study was performed to determine the optimal architecture and improve the performance of the model. In the segmentation approach, a cascade structure, in which U-Net is connected to ResNet, was implemented. The performance of the two approaches was evaluated based on a confusion matrix.Results Using the classification approach, ResNet34 and gated recurrent units with augmentation showed the highest performance, with average accuracy 0.901, precision 0.613, recall 0.757, f1-score 0.677 and AUROC 0.936. Using the segmentation approach, U-Net combined with ResNet34 and augmentation showed poorer performance than the classification approach.Conclusions The CADx system described in this study showed high performance in determining the optimal view for SCB. This system could be expanded to include many anatomical regions and may have potential to aid clinicians in real-time settings.Trial registration The protocol was registered with the Clinical Trial Registry of Korea (KCT0005822, https://cris.nih.go.kr)

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“…Nonexperts have reported the system useful during both clinical practice and training. 50,51 Other MV systems under evaluation include NerveBlox 52,53 (SmartAlpha), and NerveTrack 54 (Samsung Medison). Randomized clinical trials are required to determine the benefit of the use of any MV-based system for regional anesthesia, and as of publication are yet to report.…”

Section: Applications Of Machine Vision In Anesthesiamentioning

confidence: 99%

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

Lonsdale,

Gray,

Ahumada

et al. 2023

Anesthesia &Amp; Analgesia

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Machine vision describes the use of artificial intelligence to interpret, analyze, and derive predictions from image or video data. Machine vision–based techniques are already in clinical use in radiology, ophthalmology, and dermatology, where some applications currently equal or exceed the performance of specialty physicians in areas of image interpretation. While machine vision in anesthesia has many potential applications, its development remains in its infancy in our specialty. Early research for machine vision in anesthesia has focused on automated recognition of anatomical structures during ultrasound-guided regional anesthesia or line insertion; recognition of the glottic opening and vocal cords during video laryngoscopy; prediction of the difficult airway using facial images; and clinical alerts for endobronchial intubation detected on chest radiograph. Current machine vision applications measuring the distance between endotracheal tube tip and carina have demonstrated noninferior performance compared to board-certified physicians. The performance and potential uses of machine vision for anesthesia will only grow with the advancement of underlying machine vision algorithm technical performance developed outside of medicine, such as convolutional neural networks and transfer learning. This article summarizes recently published works of interest, provides a brief overview of techniques used to create machine vision applications, explains frequently used terms, and discusses challenges the specialty will encounter as we embrace the advantages that this technology may bring to future clinical practice and patient care. As machine vision emerges onto the clinical stage, it is critically important that anesthesiologists are prepared to confidently assess which of these devices are safe, appropriate, and bring added value to patient care.

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Exploring the utility of assistive artificial intelligence for ultrasound scanning in regional anesthesia

Cited by 38 publications

References 16 publications

Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia

Evaluation of the impact of assistive artificial intelligence on ultrasound scanning for regional anaesthesia

Optimal View Detection for Ultrasound-guided Supraclavicular Block using Deep Learning Approaches

Machine Vision and Image Analysis in Anesthesia: Narrative Review and Future Prospects

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