SLIDE: automatic spine level identification system using a deep convolutional neural network

Hetherington, Jorden; Lessoway, Victoria A.; Gunka, V.; Abolmaesumi, Purang; Rohling, Robert

doi:10.1007/s11548-017-1575-8

Cited by 76 publications

(45 citation statements)

References 28 publications

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“…Two recent studies, namely Pesteie et al ( 2015 ) and Hetherington et al ( 2017 ), exploited artificial neural networks trained with ultrasound images to automatically detect the optimal vertebra level and injection plane for percutaneous spinal needle injections. They used different ML techniques on the same type of medical images.…”

Section: Literature Review: Resultsmentioning

confidence: 99%

“…The second study reported a real-time scanner system (SLIDE) implementing a pre-trained CNN and a finite-state transducer that automatically detected the patient's vertebral level for the optimal lumbar puncture point (Hetherington et al, 2017 ). The CNN was trained using a transfer learning approach, where an inter-domain knowledge transfer was used as a prerequisite for accurate prediction.…”

Section: Literature Review: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Orthopedics: A Literature Review

Cabitza

Locoro

Banfi

2018

Front. Bioeng. Biotechnol.

190

125

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In this paper we present the findings of a systematic literature review covering the articles published in the last two decades in which the authors described the application of a machine learning technique and method to an orthopedic problem or purpose. By searching both in the Scopus and Medline databases, we retrieved, screened and analyzed the content of 70 journal articles, and coded these resources following an iterative method within a Grounded Theory approach. We report the survey findings by outlining the articles' content in terms of the main machine learning techniques mentioned therein, the orthopedic application domains, the source data and the quality of their predictive performance.

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Section: Literature Review: Resultsmentioning

confidence: 99%

Section: Literature Review: Resultsmentioning

confidence: 99%

Machine Learning in Orthopedics: A Literature Review

Cabitza

Locoro

Banfi

2018

Front. Bioeng. Biotechnol.

190

125

View full text Add to dashboard Cite

show abstract

“…The accuracy of CNN for normal versus affected tissues (DM, PM, and IBM) reached 76.2% which is 3.9% higher than this value of random forests. Hetherington et al designed a spine level identification system employing CNN [ 68 ]. The system can accurately detect the vertebral level so that the anesthesiologist can find the right site to inject the anaesthetic.…”

Section: Ultrasound Cad System With Deep Learning Technologymentioning

confidence: 99%

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

Huang

Zhang

2018

BioMed Research International

164

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The ultrasound imaging is one of the most common schemes to detect diseases in the clinical practice. There are many advantages of ultrasound imaging such as safety, convenience, and low cost. However, reading ultrasound imaging is not easy. To support the diagnosis of clinicians and reduce the load of doctors, many ultrasound computer-aided diagnosis (CAD) systems are proposed. In recent years, the success of deep learning in the image classification and segmentation led to more and more scholars realizing the potential of performance improvement brought by utilizing the deep learning in the ultrasound CAD system. This paper summarized the research which focuses on the ultrasound CAD system utilizing machine learning technology in recent years. This study divided the ultrasound CAD system into two categories. One is the traditional ultrasound CAD system which employed the manmade feature and the other is the deep learning ultrasound CAD system. The major feature and the classifier employed by the traditional ultrasound CAD system are introduced. As for the deep learning ultrasound CAD, newest applications are summarized. This paper will be useful for researchers who focus on the ultrasound CAD system.

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“…Minimally invasive surgery is another promising strategy for spine deformities; therefore, accurate percutaneous spinal needle insertion procedures are necessary. Hetherington et al (45) developed a real-time system based on deep CNN to classify transverse images of the lower spine, and this method might contribute to the development of a minimally invasive treatment for ADS. To satisfy the goal of verification, screw insertion was performed intraoperatively.…”

Section: Intraoperative Manipulationmentioning

confidence: 99%

A narrative review of machine learning as promising revolution in clinical practice of scoliosis

Chen¹,

Zhai²,

Sun³

et al. 2021

Ann Transl Med

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Machine learning (ML), as an advanced domain of artificial intelligence (AI), is progressively changing our view of the world. By implementing its algorithms, our ability to detect previously undiscoverable patterns in data has the potential to revolutionize predictive analytics. Scoliosis, as a relatively specialized branch in the spine field, mainly covers the pediatric, adult and the elderly populations, and its diagnosis and treatment remain difficult. With recent efforts and interdisciplinary cooperation, ML has been widely applied to investigate issues related to scoliosis, and surprisingly augment a surgeon's ability in clinical practice related to scoliosis. Meanwhile, ML models penetrate in every stage of the clinical practice procedure of scoliosis. In this review, we first present a brief description of the application of ML in the clinical practice procedures regarding scoliosis, including screening, diagnosis and classification, surgical decision making, intraoperative manipulation, complication prediction, prognosis prediction and rehabilitation. Meanwhile, the ML models and specific applications adopted are presented. Additionally, current limitations and future directions are briefly discussed regarding its use in the field of scoliosis. We believe that the implementation of ML is a promising revolution to assist surgeons in all aspects of clinical practice related to scoliosis in the near future.

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SLIDE: automatic spine level identification system using a deep convolutional neural network

Cited by 76 publications

References 28 publications

Machine Learning in Orthopedics: A Literature Review

Machine Learning in Orthopedics: A Literature Review

Machine Learning in Ultrasound Computer-Aided Diagnostic Systems: A Survey

A narrative review of machine learning as promising revolution in clinical practice of scoliosis

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