Compression fracture diagnosis in lumbar: a clinical CAD system

Al-Helo, Samah; Alomari, Raja’ S.; Ghosh, S. K.; Chaudhary, Vipin; Dhillon, Gurmeet; Al-Zoubi, Moh'd Belal; Hiary, Hazem; Hamtini, Thair

doi:10.1007/s11548-012-0796-0

Cited by 41 publications

(23 citation statements)

References 13 publications

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“…In a similar study, Al-Helo et al ( 2013 ), the authors attempted to learn lumbar wedge fracture diagnoses from CT image labeling, for segmentation and prediction, by applying either a neural network and a k-means approach. The neural net was assessed to attain an accuracy of 93.2% on average for lumbar fractures detection, while the clustering method attained an accuracy of 98% on average, thereby showing a sensitivity of over 99% and a specificity of 87.5%.…”

Section: Literature Review: Resultsmentioning

confidence: 99%

“…Examining the best performing model for each class of orthopedic application, on the basis of the highest score of the main evaluation metric considered in the study (discarding the scores below the 85%), we see a varied situation with some results that could seem surprising:indeed, for most application domains, the reported performance is very good (if not almost perfect) like in spine pathology detection (Jamaludin et al, 2017 ), bone age assessment (Spampinato et al, 2017 ), prosthesis control (Lemoyne et al, 2015 ), gait classification' (Pogorelc and Gams, 2010 ), ostheoartritis detection (Phinyomark et al, 2016 ) and fracture detection (Atkinson et al, 2012 ; Al-Helo et al, 2013 ). In a few cases, like shoulder strength assessment (Silver et al, 2006 ) and image segmentation Prasoon et al ( 2013 ), there is still room for improvement in the performance.…”

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.

183

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.

183

125

View full text Add to dashboard Cite

show abstract

“…On CT images, osteolytic metastases show lower attenuation than the surrounding bone, while osteoblastic ones show higher attenuation than the surrounding bone. However, the detection of vertebral metastases by computer-aided diagnosis (CAD) on CT is challenging [4, 12]. Temporal subtraction of chest radiograph is a CAD technique that initial digital image subtract from follow-up image on workstation, and it has already been commercialized [13, 14].…”

Section: Introductionmentioning

confidence: 99%

Thoracic Temporal Subtraction Three Dimensional Computed Tomography (3D-CT): Screening for Vertebral Metastases of Primary Lung Cancers

Iwano¹,

Ito²,

Umakoshi³

et al. 2017

PLoS ONE

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PurposeWe developed an original, computer-aided diagnosis (CAD) software that subtracts the initial thoracic vertebral three-dimensional computed tomography (3D-CT) image from the follow-up 3D-CT image. The aim of this study was to investigate the efficacy of this CAD software during screening for vertebral metastases on follow-up CT images of primary lung cancer patients.Materials and MethodsThe interpretation experiment included 30 sets of follow-up CT scans in primary lung cancer patients and was performed by two readers (readers A and B), who each had 2.5 years’ experience reading CT images. In 395 vertebrae from C6 to L3, 46 vertebral metastases were identified as follows: osteolytic metastases (n = 17), osteoblastic metastases (n = 14), combined osteolytic and osteoblastic metastases (n = 6), and pathological fractures (n = 9). Thirty-six lesions were in the anterior component (vertebral body), and 10 lesions were in the posterior component (vertebral arch, transverse process, and spinous process). The area under the curve (AUC) by receiver operating characteristic (ROC) curve analysis and the sensitivity and specificity for detecting vertebral metastases were compared with and without CAD for each observer.ResultsReader A detected 47 abnormalities on CT images without CAD, and 33 of them were true-positive metastatic lesions. Using CAD, reader A detected 57 abnormalities, and 38 were true positives. The sensitivity increased from 0.717 to 0.826, and on ROC curve analysis, AUC with CAD was significantly higher than that without CAD (0.849 vs. 0.902, p = 0.021). Reader B detected 40 abnormalities on CT images without CAD, and 36 of them were true-positive metastatic lesions. Using CAD, reader B detected 44 abnormalities, and 39 were true positives. The sensitivity increased from 0.783 to 0.848, and AUC with CAD was nonsignificantly higher than that without CAD (0.889 vs. 0.910, p = 0.341). Both readers detected more osteolytic and osteoblastic metastases with CAD than without CAD.ConclusionOur temporal 3D-CT subtraction CAD software easily detected vertebral metastases on the follow-up CT images of lung cancer patients regardless of the osteolytic or osteoblastic nature of the lesions.

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“…Al-Helo et al [8] developed a CAD system to detect vertebral body fractures in computed tomographic (CT) images and obtained a specificity of 87.5% and sensitivity over 99% using K-Means. They performed vertebrae localization and segmentation, and then classified each vertebra as fractured or non-fractured, based on an Active Shape Model and a Gradient Vector Flow Active Contours.…”

Section: Introductionmentioning

confidence: 99%

Semiautomatic Classification of Benign Versus Malignant Vertebral Compression Fractures Using Texture and Gray-Level Features in Magnetic Resonance Images

Frighetto-Pereira

Menezes-Reis

Metzner

et al. 2015

2015 IEEE 28th International Symposium on Computer-Based Medical Systems

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Our study aimed to develop a system for computer-aided diagnosis of vertebral compression fractures (VCFs) using magnetic resonance imaging (MRI), to help in the differentiation between malignant and benign VCFs. Lumbar spine MRI was used to acquire T1-weighted images in the sagittal plane. Images from 63 consecutive patients (38 women, 25 men, mean age 62.25 ± 14.13 years) with at least one VCF diagnosis were studied. Contrast and texture features were extracted from manually segmented images of 103 vertebral bodies with VCFs. The classification of malignant vs. benign VCFs was performed using the k-nearest neighbor (KNN) classifier with the Euclidean distance. Using a KNN classifier with k=3, feature selection, and 10-fold cross-validation, we obtained a value of the area under the receiver operating characteristic curve of 0.913.

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Compression fracture diagnosis in lumbar: a clinical CAD system

Cited by 41 publications

References 13 publications

Machine Learning in Orthopedics: A Literature Review

Machine Learning in Orthopedics: A Literature Review

Thoracic Temporal Subtraction Three Dimensional Computed Tomography (3D-CT): Screening for Vertebral Metastases of Primary Lung Cancers

Semiautomatic Classification of Benign Versus Malignant Vertebral Compression Fractures Using Texture and Gray-Level Features in Magnetic Resonance Images

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