Comparison of ultrasound scanning for scoliosis assessment: Robotic versus manual

Victorova, Maria; Lau, Heidi Hin Ting; Lee, Timothy Tin-Yan; Navarro-Alarcon, David; Zheng, Yongping

doi:10.1002/rcs.2468

Cited by 4 publications

(2 citation statements)

References 40 publications

(86 reference statements)

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“…25 With robotic scanning, the system performance has better reliability and reputability. 28 The proposed robotic system also provides a stable force control around 5 N, keeping regular contact with the skin surface compared to manual scanning. 15 The robot-assisted system achieves smooth scanning with constant speed over the complex back contour.…”

Section: Discussionmentioning

confidence: 99%

Development and evaluation of robot‐assisted ultrasound navigation system for pedicle screw placement: An ex‐vivo animal validation

Li,

Davoodi,

Cai

et al. 2023

Robotics Computer Surgery

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PurposeSpinal instrumentation with pedicle screw placement (PSP) is an important surgical technique for spinal diseases. Accurate screw trajectory is a prerequisite for PSP. Ultrasound (US) imaging with robot‐assisted system forms a non‐radiative alternative to provide precise screw trajectory. This study reports on the development and assessment of US navigation for this application.MethodsA robot‐assisted US reconstruction was proposed and an automatic CT‐to‐US registration algorithm was investigated, allowing the registration of screw trajectories. Experiments were conducted on ex‐vivo lamb spines to evaluate system performance.ResultsIn total, 72 screw trajectories are measured, displaying an average position accuracy of 2.80 ± 1.14 mm and orientation accuracy of 1.38 ± 0.61°.ConclusionThe experimental results demonstrate the feasibility of proposed US system. This work, although restricted to laboratory settings, encourages further exploration of the potential of this technology in clinical practice.

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Section: Discussionmentioning

confidence: 99%

Development and evaluation of robot‐assisted ultrasound navigation system for pedicle screw placement: An ex‐vivo animal validation

Li,

Davoodi,

Cai

et al. 2023

Robotics Computer Surgery

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show abstract

“…Accurately determining the extent of curvature through mechanical measurements can be challenging for physicians due to individual anatomical differences. In addition, traditional methods are subject to human error, leading to result inconsistencies [5]. Low-quality images and variations in patient's posture and positioning during scans influence scoliosis assessments.…”

Section: No Angle In Degreesmentioning

confidence: 99%

SCOLIONET: An Automated Scoliosis Cobb Angle Quantification Using Enhanced X-ray Images and Deep Learning Models

Maaliw

2023

J. Imaging

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The advancement of medical prognoses hinges on the delivery of timely and reliable assessments. Conventional methods of assessments and diagnosis, often reliant on human expertise, lead to inconsistencies due to professionals’ subjectivity, knowledge, and experience. To address these problems head-on, we harnessed artificial intelligence’s power to introduce a transformative solution. We leveraged convolutional neural networks to engineer our SCOLIONET architecture, which can accurately identify Cobb angle measurements. Empirical testing on our pipeline demonstrated a mean segmentation accuracy of 97.50% (Sorensen–Dice coefficient) and 96.30% (Intersection over Union), indicating the model’s proficiency in outlining vertebrae. The level of quantification accuracy was attributed to the state-of-the-art design of the atrous spatial pyramid pooling to better segment images. We also compared physician’s manual evaluations against our machine driven measurements to validate our approach’s practicality and reliability further. The results were remarkable, with a p-value (t-test) of 0.1713 and an average acceptable deviation of 2.86 degrees, suggesting insignificant difference between the two methods. Our work holds the premise of enabling medical practitioners to expedite scoliosis examination swiftly and consistently in improving and advancing the quality of patient care.

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Quantitative Skeletal Imaging and Image-Based Modeling in Pediatric Orthopaedics

Requist,

Mills,

Carroll

et al. 2024

Curr Osteoporos Rep

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Comparison of ultrasound scanning for scoliosis assessment: Robotic versus manual

Cited by 4 publications

References 40 publications

Development and evaluation of robot‐assisted ultrasound navigation system for pedicle screw placement: An ex‐vivo animal validation

Development and evaluation of robot‐assisted ultrasound navigation system for pedicle screw placement: An ex‐vivo animal validation

SCOLIONET: An Automated Scoliosis Cobb Angle Quantification Using Enhanced X-ray Images and Deep Learning Models

Quantitative Skeletal Imaging and Image-Based Modeling in Pediatric Orthopaedics

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