Applying deep learning in recognizing the femoral nerve block region on ultrasound images

Huang, Chanyan; Zhou, Ying; Tan, Wulin; Qiu, Zeting; Zhou, Huaqiang; Song, Yiyan; Zhao, Yue; S, Gao

doi:10.21037/atm.2019.08.61

Cited by 36 publications

(43 citation statements)

References 12 publications

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“…Our results show an overall high detection performance and overall high values of the IoU and DICE score, indicating that the classification of US images of pressure injuries by deep learning may be applicable in clinical practice. Conversely, the values of the IoU of the cobblestone-and cloud-like patterns were slightly lower compared to those in previous studies [29,30]. One of the reasons for this is that these two patterns are similar, and as shown in Figure 4, they are often misjudged.…”

Section: Discussionmentioning

confidence: 55%

Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning

et al. 2021

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The classification of ultrasound (US) findings of pressure injury is important to select the appropriate treatment and care based on the state of the deep tissue, but it depends on the operator’s skill in image interpretation. Therefore, US for pressure injury is a procedure that can only be performed by a limited number of highly trained medical professionals. This study aimed to develop an automatic US image classification system for pressure injury based on deep learning that can be used by non-specialists who do not have a high skill in image interpretation. A total 787 training data were collected at two hospitals in Japan. The US images of pressure injuries were assessed using the deep learning-based classification tool according to the following visual evidence: unclear layer structure, cobblestone-like pattern, cloud-like pattern, and anechoic pattern. Thereafter, accuracy was assessed using two parameters: detection performance, and the value of the intersection over union (IoU) and DICE score. A total of 73 images were analyzed as test data. Of all 73 images with an unclear layer structure, 7 showed a cobblestone-like pattern, 14 showed a cloud-like pattern, and 15 showed an anechoic area. All four US findings showed a detection performance of 71.4–100%, with a mean value of 0.38–0.80 for IoU and 0.51–0.89 for the DICE score. The results show that US findings and deep learning-based classification can be used to detect deep tissue pressure injuries.

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Section: Discussionmentioning

confidence: 55%

Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning

et al. 2021

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“…Based on this information, we presented the case for the use of assistive artificial intelligence (AI) technology to facilitate the recognition of anatomical structures in UGRA (Bowness et al, 2020). This concept has also been proposed by other groups, both for UGRA (Alkhatib et al, 2019; Huang et al, 2019) and central neuraxial blockade (spinal and epidural) (Oh et al, 2019; Smistad et al, 2018; Tran & Rohling, 2010).…”

Section: Introductionmentioning

confidence: 67%

“…This clinically orientated evaluation of AI anatomy identification is a novel approach to assessing such technology, as it is taken from the point of the end‐user. Statistical techniques to provide a quantitative assessment of system performance have been used in prior publications, such as Intersection over Union (Huang et al, 2019) and the Dice co‐efficient (Smistad et al, 2018). However, as there has been little work done to determine the clinical utility of any given threshold in these metrics, the approach in this investigation emphasizes the ultimate need for the clinician to recognize the salient anatomical structures (which the system is designed to aid).…”

Section: Discussionmentioning

confidence: 99%

Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound‐guided regional anesthesia

2021

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Ultrasound‐guided regional anesthesia involves visualizing sono‐anatomy to guide needle insertion and the perineural injection of local anesthetic. Anatomical knowledge and recognition of anatomical structures on ultrasound are known to be imperfect amongst anesthesiologists. This investigation evaluates the performance of an assistive artificial intelligence (AI) system in aiding the identification of anatomical structures on ultrasound. Three independent experts in regional anesthesia reviewed 40 ultrasound scans of seven body regions. Unmodified ultrasound videos were presented side‐by‐side with AI‐highlighted ultrasound videos. Experts rated the overall system performance, ascertained whether highlighting helped identify specific anatomical structures, and provided opinion on whether it would help confirm the correct ultrasound view to a less experienced practitioner. Two hundred and seventy‐five assessments were performed (five videos contained inadequate views); mean highlighting scores ranged from 7.87 to 8.69 (out of 10). The Kruskal–Wallis H‐test showed a statistically significant difference in the overall performance rating (χ2[6] = 36.719, asymptotic p < 0.001); regions containing a prominent vascular landmark ranked most highly. AI‐highlighting was helpful in identifying specific anatomical structures in 1330/1334 cases (99.7%) and for confirming the correct ultrasound view in 273/275 scans (99.3%). These data demonstrate the clinical utility of an assistive AI system in aiding the identification of anatomical structures on ultrasound during ultrasound‐guided regional anesthesia. Whilst further evaluation must follow, such technology may present an opportunity to enhance clinical practice and energize the important field of clinical anatomy amongst clinicians.

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“…To alleviate the time inefficiency of human interventions and to bypass the intermediate stages in conventional ML-based pipeline designs, DL-based segmentation (U-Net architecture) has been applied to identify musculocutaneous, median, ulnar, and radial nerves [ 57 ], as well as femoral nerve blocks in US images [ 58 ]. DCNN-based nerve segmentation with variants inspired by the original U-Net architecture was applied [ 59 , 60 ] to NERVE datasets (brachial plexus segmentation in US images, available at https://www.kaggle.com/c/ultrasound-nerve-segmentation ).…”

Section: Automated Us Image Segmentation Techniquesmentioning

confidence: 99%

Artificial intelligence in musculoskeletal ultrasound imaging

et al. 2021

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Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.

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Applying deep learning in recognizing the femoral nerve block region on ultrasound images

Cited by 36 publications

References 12 publications

Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning

Development of an Automatic Ultrasound Image Classification System for Pressure Injury Based on Deep Learning

Identifying anatomical structures on ultrasound: assistive artificial intelligence in ultrasound‐guided regional anesthesia

Artificial intelligence in musculoskeletal ultrasound imaging

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