scholarly journals Discrimination of Low-Energy Acetabular Fractures from Controls Using Computed Tomography-Based Bone Characteristics

2020 ◽  
Vol 49 (1) ◽  
pp. 367-381
Author(s):  
Robel K. Gebre ◽  
Jukka Hirvasniemi ◽  
Iikka Lantto ◽  
Simo Saarakkala ◽  
Juhana Leppilahti ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Computed Tomography ◽  
Femoral Head ◽  
Acetabular Fracture ◽  
Ct Images ◽  
Low Energy ◽  
Acetabular Fractures ◽  
Operating Characteristics ◽  
Femoral Neck Axis

AbstractThe incidence of low-energy acetabular fractures has increased. However, the structural factors for these fractures remain unclear. The objective of this study was to extract trabecular bone architecture and proximal femur geometry (PFG) measures from clinical computed tomography (CT) images to (1) identify possible structural risk factors of acetabular fractures, and (2) to discriminate fracture cases from controls using machine learning methods. CT images of 107 acetabular fracture subjects (25 females, 82 males) and 107 age-gender matched controls were examined. Three volumes of interest, one at the acetabulum and two at the femoral head, were extracted to calculate bone volume fraction (BV/TV), gray-level co-occurrence matrix and histogram of the gray values (GV). The PFG was defined by neck shaft angle and femoral neck axis length. Relationships between the variables were assessed by statistical mean comparisons and correlation analyses. Bayesian logistic regression and Elastic net machine learning models were implemented for classification. We found lower BV/TV at the femoral head (0.51 vs. 0.55, p = 0.012) and lower mean GV at both the acetabulum (98.81 vs. 115.33, p < 0.001) and femoral head (150.63 vs. 163.47, p = 0.005) of fracture subjects when compared to their matched controls. The trabeculae within the femoral heads of the acetabular fracture sides differed in structure, density and texture from the corresponding control sides of the fracture subjects. Moreover, the PFG and trabecular architectural variables, alone and in combination, were able to discriminate fracture cases from controls (area under the receiver operating characteristics curve 0.70 to 0.79). In conclusion, lower density in the acetabulum and femoral head with abnormal trabecular structure and texture at the femoral head, appear to be risk factors for low-energy acetabular fractures.

scholarly journals Machine Learning Models for Sarcopenia Identification Based on Radiomic Features of Muscles in Computed Tomography

2021 ◽  
Vol 18 (16) ◽  
pp. 8710
Author(s):  
Young Jae Kim
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Ct Images ◽  
Erector Spinae ◽  
Support Vector ◽  
Gray Level ◽  
Learning Models ◽  
Extreme Gradient Boosting ◽  
Nsclc Patients ◽  
Machine Learning Models

The diagnosis of sarcopenia requires accurate muscle quantification. As an alternative to manual muscle mass measurement through computed tomography (CT), artificial intelligence can be leveraged for the automation of these measurements. Although generally difficult to identify with the naked eye, the radiomic features in CT images are informative. In this study, the radiomic features were extracted from L3 CT images of the entire muscle area and partial areas of the erector spinae collected from non-small cell lung carcinoma (NSCLC) patients. The first-order statistics and gray-level co-occurrence, gray-level size zone, gray-level run length, neighboring gray-tone difference, and gray-level dependence matrices were the radiomic features analyzed. The identification performances of the following machine learning models were evaluated: logistic regression, support vector machine (SVM), random forest, and extreme gradient boosting (XGB). Sex, coarseness, skewness, and cluster prominence were selected as the relevant features effectively identifying sarcopenia. The XGB model demonstrated the best performance for the entire muscle, whereas the SVM was the worst-performing model. Overall, the models demonstrated improved performance for the entire muscle compared to the erector spinae. Although further validation is required, the radiomic features presented here could become reliable indicators for quantifying the phenomena observed in the muscles of NSCLC patients, thus facilitating the diagnosis of sarcopenia.

Machine Learning with Data Science-Enabled Lung Cancer Diagnosis and Classification Using Computed Tomography Images

2021 ◽  
Author(s):  
S. Vishwa Kiran ◽  
Inderjeet Kaur ◽  
K. Thangaraj ◽  
V. Saveetha ◽  
R. Kingsy Grace ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Lung Cancer ◽  
Cancer Diagnosis ◽  
Data Science ◽  
Processing Technique ◽  
Ct Images ◽  
Lung Cancer Diagnosis ◽  
Computed Tomography Images ◽  
Diagnosis And Classification

In recent times, the healthcare industry has been generating a significant amount of data in distinct formats, such as electronic health records (EHR), clinical trials, genetic data, payments, scientific articles, wearables, and care management databases. Data science is useful for analysis (pattern recognition, hypothesis testing, risk valuation) and prediction. The major, primary usage of data science in the healthcare domain is in medical imaging. At the same time, lung cancer diagnosis has become a hot research topic, as automated disease detection poses numerous benefits. Although numerous approaches have existed in the literature for lung cancer diagnosis, the design of a novel model to automatically identify lung cancer is a challenging task. In this view, this paper designs an automated machine learning (ML) with data science-enabled lung cancer diagnosis and classification (MLDS-LCDC) using computed tomography (CT) images. The presented model initially employs Gaussian filtering (GF)-based pre-processing technique on the CT images collected from the lung cancer database. Besides, they are fed into the normalized cuts (Ncuts) technique where the nodule in the pre-processed image can be determined. Moreover, the oriented FAST and rotated BRIEF (ORB) technique is applied as a feature extractor. At last, sunflower optimization-based wavelet neural network (SFO-WNN) model is employed for the classification of lung cancer. In order to examine the diagnostic outcome of the MLDS-LCDC model, a set of experiments were carried out and the results are investigated in terms of different aspects. The resultant values demonstrated the effectiveness of the MLDS-LCDC model over the other state-of-the-art methods with the maximum sensitivity of 97.01%, specificity of 98.64%, and accuracy of 98.11%.

The Acetabular Sector Angle of the Adult Hip Determined by Computed Tomography

1986 ◽  
Vol 27 (4) ◽  
pp. 443-447 ◽  
Author(s):  
S. Anda ◽  
S. Svenningsen ◽  
L. G. Dale ◽  
P. Benum
Keyword(s):  
Computed Tomography ◽  
Femoral Head ◽  
Hip Joint ◽  
Posterior Part ◽  
Ct Images ◽  
Joint Function ◽  
Axial Ct ◽  
Sector Angle

A new set of angles measured on standard axial CT images of the hip joint is defined. The angles provide information on the support of the femoral head from the anterior and the posterior part of the acetabulum. These angles have been measured in 82 adult hips, and correlated to a set of established parameters commonly measured at conventional roentgenography and on CT images of the hip joint. The defined angles may prove to be valuable in the total appreciation of hip joint function and stability.

scholarly journals Structural risk factors for low-energy acetabular fractures

Bone ◽  
2019 ◽  
Vol 127 ◽  
pp. 334-342 ◽  
Author(s):  
Robel K. Gebre ◽  
Jukka Hirvasniemi ◽  
Iikka Lantto ◽  
Simo Saarakkala ◽  
Juhana Leppilahti ◽  
...  
Keyword(s):  
Risk Factors ◽  
Low Energy ◽  
Acetabular Fractures ◽  
Structural Risk

scholarly journals Fractures of the acetabulum - surgical treatment and complications

2020 ◽  
pp. 110-110
Author(s):  
Sasa Milenkovic ◽  
Milan Mitkovic ◽  
Milorad Mitkovic ◽  
Predrag Stojiljkovic
Keyword(s):  
Femoral Head ◽  
Acetabular Fracture ◽  
Fracture Dislocation ◽  
Acetabular Fractures ◽  
Male Population ◽  
Severe Injuries ◽  
Results Of Treatment ◽  
Car Accidents ◽  
Anatomic Reduction ◽  
Poor Outcomes

Acetabular fractures represent severe injuries that mostly occur in car accidents, or after falling from greater heights, most often in the working male population. Acetabular fractures are present in our clinical practice and require a good education and surgical training. Surgical experience is one of the prerequisites for achieving good treatment results, because these fractures are accompanied by numerous complications. In order to acquire knowledge and skills in this field of surgery, it is necessary to have a national center for education at one of the Medical Faculties in Serbia. All dislocated acetabular fractures (? 2mm), require early surgery, anatomical reduction and stable internal fixation of acetabular fracture. Acetabular fracture-dislocation requires urgent reduction of the dislocated femoral head. The anatomic reduction of the fracture is related to the time of definitive bone fixation of the fracture. After 14 days from the fracture, anatomic reduction is more difficult to achieve. In addition to these factors that positively affect the final results of treatment, there are negative factors as well, that result in poor outcomes. They are directly correlated to the initial trauma that occurs at the time of injury. Fracture comminution, large dislocation (> 20mm), injury of the femoral head, posterior dislocation of the hip, impaction, traumatic or iatrogenic sciatic nerve palsy, are factors that negatively affect the results and are responsible for complications, as opposed to positive factors.

scholarly journals The Retroacetabular Angle Determines the Safe Angle for Screw Placement in Posterior Acetabular Fracture Fixation

2013 ◽  
Vol 2013 ◽  
pp. 1-6
Author(s):  
Ayman M. A. Tadros ◽  
Thomas R. Oxland ◽  
Peter O’Brien
Keyword(s):  
Computed Tomography ◽  
Fracture Fixation ◽  
Acetabular Fracture ◽  
Articular Surface ◽  
Posterior Wall ◽  
Ct Scans ◽  
Acetabular Fractures ◽  
The Average Range ◽  
Acetabular Wall

Introduction. A method for the determination of safe angles for screws placed in the posterior acetabular wall based on preoperative computed tomography (CT) is described. It defines a retroacetabular angle and determines its variation in the population. Methods. The retroacetabular angle is the angle between the retroacetabular surface and the tangent to the posterior acetabular articular surface. Screws placed through the marginal posterior wall at an angle equal to the retroacetabular angle are extraarticular. Medial screws can be placed at larger angles whose difference from the retroacetabular angle is defined as the allowance angles. CT scans of all patients with acetabular fractures treated in our institute between September 2002 to July 2007 were used to measure the retroacetabular angle and tangent. Results. Two hundred thirty one patients were included. The average (range) age was 42 (15–74) years. The average (range) retroacetabular angle was 39 (30–47) degrees. The average (range) retroacetabular tangent was 36 (30–45) mm. Conclusions. Placing the screws at an average (range) angle of 39 (33–47) degrees of anterior inclination with the retroacetabular surface makes them extraarticular. Angles for medial screws are larger. Safe angles can be calculated preoperatively with a computer program.

scholarly journals Low energy fractures of the acetabulum

2014 ◽  
Vol 96 (4) ◽  
pp. 297-301 ◽  
Author(s):  
N Papadakos ◽  
R Pearce ◽  
MD Bircher
Keyword(s):  
Acetabular Fracture ◽  
High Energy ◽  
Fracture Pattern ◽  
Low Energy ◽  
Acetabular Fractures ◽  
Considerable Proportion ◽  
Female Ratio ◽  
Energy Mechanism ◽  
Pattern Distribution ◽  
Male To Female

Introduction Acetabular fractures due to high energy injuries are common and well documented; those secondary to low energy mechanisms are less well described. We undertook a retrospective study of the acetabular fracture referrals to our unit to evaluate the proportion of injuries resulting from a low energy mechanism. Methods A total of 573 acetabular fractures were evaluated from 1 January 2005 to 31 December 2008. The plain radiography and computed tomography of those sustaining a low energy fracture were assessed and the fracture patterns classified. Results Of the 573 acetabular fractures, 71 (12.4%) were recorded as being a result of a low energy mechanism. The male-to-female ratio was 2.4:1 and the mean patient age was 67.0 years (standard deviation: 19.1 years). There was a significantly higher number of fractures (p<0.001) involving the anterior column (with or without a posterior hemitransverse component) than in a number of previously conducted large acetabular fracture studies. Conclusions Our results demonstrate that low energy fractures make up a considerable proportion of acetabular fractures with a distinctly different fracture pattern distribution. With the continued predicted rise in the incidence of osteoporosis, life expectancy and an aging population, it is likely that this type of fracture will become increasingly more common, posing difficult management decisions and leading to procedures that are technically more challenging.

The Value of “Liver Windows” Settings in the Detection of Small Renal Cell Carcinomas on Unenhanced Computed Tomography

2014 ◽  
Vol 65 (1) ◽  
pp. 71-76 ◽  
Author(s):  
Kamal Sahi ◽  
Stuart Jackson ◽  
Edward Wiebe ◽  
Gavin Armstrong ◽  
Sean Winters ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Soft Tissue ◽  
Renal Cell ◽  
Roc Analysis ◽  
Ct Images ◽  
Operating Characteristics ◽  
Renal Cell Carcinomas ◽  
Unenhanced Ct ◽  
Single Slice ◽  
Unenhanced Computed Tomography

Objective To assess if “liver window” settings improve the conspicuity of small renal cell carcinomas (RCC). Methods Patients were analysed from our institution's pathology-confirmed RCC database that included the following: (1) stage T1a RCCs, (2) an unenhanced computed tomography (CT) abdomen performed ≤ 6 months before histologic diagnosis, and (3) age ≥ 17 years. Patients with multiple tumours, prior nephrectomy, von Hippel-Lindau disease, and polycystic kidney disease were excluded. The unenhanced CT was analysed, and the tumour locations were confirmed by using corresponding contrast-enhanced CT or magnetic resonance imaging studies. Representative single-slice axial, coronal, and sagittal unenhanced CT images were acquired in “soft tissue windows” (width, 400 Hounsfield unit (HU); level, 40 HU) and liver windows (width, 150 HU; level, 88 HU). In addition, single-slice axial, coronal, and sagittal unenhanced CT images of nontumourous renal tissue (obtained from the same cases) were acquired in soft tissue windows and liver windows. These data sets were randomized, unpaired, and were presented independently to 3 blinded radiologists for analysis. The presence or absence of suspicious findings for tumour was scored on a 5-point confidence scale. Results Eighty-three of 415 patients met the study criteria. Receiver operating characteristics (ROC) analysis, t test analysis, and kappa analysis were used. ROC analysis showed statistically superior diagnostic performance for liver windows compared with soft tissue windows (area under the curve of 0.923 vs 0.879; P = .0002). Kappa statistics showed “good” vs “moderate” agreement between readers for liver windows compared with soft tissue windows. Conclusion Use of liver windows settings improves the detection of small RCCs on the unenhanced CT.

scholarly journals Utilizing Deep Machine Learning for Prognostication of Oral Squamous Cell Carcinoma—A Systematic Review

2021 ◽  
Vol 2 ◽  
Author(s):  
Rasheed Omobolaji Alabi ◽  
Ibrahim O. Bello ◽  
Omar Youssef ◽  
Mohammed Elmusrati ◽  
Antti A. Mäkitie ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Squamous Cell Carcinoma ◽  
Oral Squamous Cell Carcinoma ◽  
Cell Carcinoma ◽  
Squamous Cell ◽  
Operating Characteristics ◽  
Clinical Practices ◽  
Area Of Interest ◽  
Computed Tomography Images

The application of deep machine learning, a subfield of artificial intelligence, has become a growing area of interest in predictive medicine in recent years. The deep machine learning approach has been used to analyze imaging and radiomics and to develop models that have the potential to assist the clinicians to make an informed and guided decision that can assist to improve patient outcomes. Improved prognostication of oral squamous cell carcinoma (OSCC) will greatly benefit the clinical management of oral cancer patients. This review examines the recent development in the field of deep learning for OSCC prognostication. The search was carried out using five different databases—PubMed, Scopus, OvidMedline, Web of Science, and Institute of Electrical and Electronic Engineers (IEEE). The search was carried time from inception until 15 May 2021. There were 34 studies that have used deep machine learning for the prognostication of OSCC. The majority of these studies used a convolutional neural network (CNN). This review showed that a range of novel imaging modalities such as computed tomography (or enhanced computed tomography) images and spectra data have shown significant applicability to improve OSCC outcomes. The average specificity, sensitivity, area under receiving operating characteristics curve [AUC]), and accuracy for studies that used spectra data were 0.97, 0.99, 0.96, and 96.6%, respectively. Conversely, the corresponding average values for these parameters for computed tomography images were 0.84, 0.81, 0.967, and 81.8%, respectively. Ethical concerns such as privacy and confidentiality, data and model bias, peer disagreement, responsibility gap, patient-clinician relationship, and patient autonomy have limited the widespread adoption of these models in daily clinical practices. The accumulated evidence indicates that deep machine learning models have great potential in the prognostication of OSCC. This approach offers a more generic model that requires less data engineering with improved accuracy.

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