Compression fractures detection on CT

Bar, Amir; Wolf, Lior; Amitai, Orna Bergman; Toledano, Eyal; Elnekave, Eldad

doi:10.1117/12.2249635

Cited by 52 publications

(67 citation statements)

References 26 publications

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“…Each point on this ROC curve represents one optimal classifier generated with one pair of hyperparameter values (probability threshold, noise threshold). Figure 4 shows this patient-level fracture detection ROC curve for the five-fold cross-validation experiment 3 [11] and the operating point (recall 0.905, specificity 0.938) on our patient-level fracture detection ROC is similar to the one reported by Bar et al (recall 0.839, specificity 0.938) [1]. We note that all these results have been reported using different test sets (due to the absence of a public test set for fracture detection).…”

Section: Resultssupporting

confidence: 75%

Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks

Nicolaes

Raeymaeckers²,

Robben

et al. 2020

Lecture Notes in Computer Science

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Osteoporosis induced fractures occur worldwide about every 3 seconds. Vertebral compression fractures are early signs of the disease and considered risk predictors for secondary osteoporotic fractures. We present a detection method to opportunistically screen spine-containing CT images for the presence of these vertebral fractures. Inspired by radiology practice, existing methods are based on 2D and 2.5D features but we present, to the best of our knowledge, the first method for detecting vertebral fractures in CT using automatically learned 3D feature maps. The presented method explicitly localizes these fractures allowing radiologists to interpret its results. We train a voxel-classification 3D Convolutional Neural Network (CNN) with a training database of 90 cases that has been semi-automatically generated using radiologist readings that are readily available in clinical practice. Our 3D method produces an Area Under the Curve (AUC) of 95% for patient-level fracture detection and an AUC of 93% for vertebra-level fracture detection in a five-fold cross-validation experiment.

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Section: Resultssupporting

confidence: 75%

Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks

Nicolaes

Raeymaeckers²,

Robben

et al. 2020

Lecture Notes in Computer Science

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“… 8 For example, Zebra Medical Vision Kashyap ( 2017 ) in 2017 began offering a pay-per-use service (SaaS) for compression fractures detection Bar et al ( 2017 ) and bone density evaluation to potentially all health care facilities that could connect and send medical images to its cloud. …”

mentioning

confidence: 99%

Machine Learning in Orthopedics: A Literature Review

Cabitza

Locoro

Banfi

2018

Front. Bioeng. Biotechnol.

180

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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“…Performance is graded according to its level of discrimination (probability of predicting outcomes accurately) and calibration (degree of over-or underestimating the predicted vs. true outcome) (17). Examples of ML applications encountered by spine surgeons include image classification [i.e., automated detection of vertebral body compression fractures on CT or MRI (18)(19)(20)], preoperative risk stratification models, clinical decision support tools (21)(22)(23)(24)(25), among others. The purpose of this review is to define basic ML terminology, discuss the difference between ML and classical statistics, detail common ML models, and introduce examples in spine research.…”

Section: Overview Of Machine Learningmentioning

confidence: 99%

“…as the grading of lumbar stenosis (18)(19)(20)54). The potential for successful translation for preoperative and intraoperative care is promising in spine surgery.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

The Role of Machine Learning in Spine Surgery: The Future Is Now

et al. 2020

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The recent influx of machine learning centered investigations in the spine surgery literature has led to increased enthusiasm as to the prospect of using artificial intelligence to create clinical decision support tools, optimize postoperative outcomes, and improve technologies used in the operating room. However, the methodology underlying machine learning in spine research is often overlooked as the subject matter is quite novel and may be foreign to practicing spine surgeons. Improper application of machine learning is a significant bioethics challenge, given the potential consequences of over-or underestimating the results of such studies for clinical decision-making processes. Proper peer review of these publications requires a baseline familiarity of the language associated with machine learning, and how it differs from classical statistical analyses. This narrative review first introduces the overall field of machine learning and its role in artificial intelligence, and defines basic terminology. In addition, common modalities for applying machine learning, including classification and regression decision trees, support vector machines, and artificial neural networks are examined in the context of examples gathered from the spine literature. Lastly, the ethical challenges associated with adapting machine learning for research related to patient care, as well as future perspectives on the potential use of machine learning in spine surgery, are discussed specifically.

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Compression fractures detection on CT

Cited by 52 publications

References 26 publications

Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks

Detection of Vertebral Fractures in CT Using 3D Convolutional Neural Networks

Machine Learning in Orthopedics: A Literature Review

The Role of Machine Learning in Spine Surgery: The Future Is Now

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