Prediction of pathologic femoral fractures in patients with lung cancer using machine learning algorithms: Comparison of computed tomography-based radiological features with clinical features versus without clinical features

Oh, Eunsun; Seo, Sung Wook; Yoon, Young Cheol; Kim, Dong Wook; Kwon, Song; Yoon, Sungroh

doi:10.1177/2309499017716243

Cited by 23 publications

(15 citation statements)

References 11 publications

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“…13 Machine learning algorithms combine many features to construct an optimized highly accurate prediction equation. 14 Supervised machine learning models are suited to solve classification problems where the classification of each patient is known a priori; samples are used to train the model to classify other samples. 15 Therefore, the aim of this study was to develop and test a predictive machine learning model to identify factors associated with intellectual disability in teenagers with cerebral palsy and evaluate potential association between severe intellectual disability and physical impairments.…”

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Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model

et al. 2019

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Background: Intellectual disability and impaired adaptive functioning are common in children with cerebral palsy, but there is a lack of studies assessing these issues in teenagers with cerebral palsy. Therefore, the aim of this study was to develop and test a predictive machine learning model to identify factors associated with intellectual disability in teenagers with cerebral palsy. Methods: This was a multicenter controlled cohort study of 91 teenagers with cerebral palsy (53 males, 38 females; mean age ± SD = 17 ± 1 y; range: 12-18 y). Data on etiology, diagnosis, spasticity, epilepsy, clinical history, communication abilities, behaviors, motor skills, eating, and drinking abilities were collected between 2005 and 2015. Intellectual disability was classified as “mild,” “moderate,” “severe,” or “profound” based on adaptive functioning, and according to the DSM-5 after 2013 and DSM-IV before 2013, the Wechsler Intelligence Scale for Children for patients up to ages 16 years, 11 months, and the Wechsler Adult Intelligence Scale for patients ages 17-18. Statistical analysis included Fisher’s exact test and multiple logistic regressions to identify factors associated with intellectual disability. A predictive machine learning model was developed to identify factors associated with having profound intellectual disability. The guidelines of the “Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis Statement” were followed. Results: Poor manual abilities (P ≤ .001), gross motor function (P ≤ .001), and type of epilepsy (intractable: P = .04; well controlled: P = .01) were significantly associated with profound intellectual disability. The average model accuracy, specificity, and sensitivity was 78%. Conclusion: Poor motor skills and epilepsy were associated with profound intellectual disability. The machine learning prediction model was able to adequately identify high likelihood of severe intellectual disability in teenagers with cerebral palsy.

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Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model

et al. 2019

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“…Some scholars proposed that combining pixel information of different scales can extract the best size information [19]. Some researchers believed that reducing the size of the convolution kernel can improve the running speed of the neural network [20]. X-DRR image is a highly nonlinear function of spatial transformation parameters.…”

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Dual X-Ray Image under Neural Network Adopted in Comparison of Efficacy and Safety of Deep Hydrolyzed Protein Milk Powder and Parenteral Nutrition in Intervention of Neonatal Noninfectious Abdominal Distension

Zong¹,

Wu³

et al. 2021

Scientific Programming

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Objective. The aim of this study was to explore the application value of double X-ray image based on neural network in comparison of the efficacy and safety of deep-water proteolytic milk powder and parenteral nutrition in the intervention of neonatal noninfectious abdominal distention. Methods. Clinical data of 58 neonates diagnosed with noninfectious abdominal distention were retrospectively analyzed. 2D-3D registration was simplified into two steps by decomposing spatial rigid-body transformation parameters into two planes, including 2D-2D approximate rigid-body registration and single-parameter 2D-3D rigid-body registration. Then, the convolution neural network was used to fit the nonlinear mapping relationship between the residual of X-ray images and the corresponding attitude differences of children, and the residual regression spatial rigid-body transformation parameters of the X-DRR image pairs were obtained. Noninfectious abdominal distention was diagnosed in all neonates, of which 28 neonates were treated with deep hydrolyzed protein milk powder. Another 30 neonates who received parenteral nutrition support were set as control group. All newborns received two weeks of treatment. The total effective rate, birth weight recovery, weight growth rate, intestinal feeding recovery time, and incidence of feeding intolerance were compared between the two groups. Results. Spatial coordinate decomposition using double X-ray can simplify the mapping relationship between spatial coordinate transformation and X-DRR residual image. Compared with the gray level iterative optimization registration algorithm, the registration accuracy and speed were significantly improved. The total effective rate in the treatment group (92.86%) was significantly higher than that in the control group (9%). The recovery time of birth weight, intestinal feeding recovery time, and meconium excretion time were significantly shorter than those in the control group, and the body weight in the treatment group increased faster than that in the control group ( P < 0.05 ). In addition, the incidence of feeding intolerance was 3.57% (1/28) in the treatment group and 36.36% (8/22) in the control group, which was significantly lower than that in the treatment group ( P < 0.05 ). Conclusion. After data training, the network can complete accurate double ray registration in 0.04 s. Deep hydrolyzed protein milk powder had remarkable therapeutic effect on neonates, with no infective abdominal distention, fast recovery, and low incidence of feeding intolerance, which was safe and reliable in clinical application.

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“…In orthopaedic oncology, management of metastatic bone disease is a major focus, especially with respect to fracture and impending fracture care[ 77 ]. Oh et al [ 78 ] used ML on CT imaging and clinical features to extract radiologic features and derive predictions for pathological femoral fractures in metastatic lung cancer and compared the ML model with one that used CT features alone. The ML model, which included clinical features, showed superior predictive accuracy compared to the model that used CT features alone.…”

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Machine learning in orthopaedic surgery

Lalehzarian¹,

Gowd²,

Liu³

2021

WJO

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Artificial intelligence and machine learning in orthopaedic surgery has gained mass interest over the last decade or so. In prior studies, researchers have demonstrated that machine learning in orthopaedics can be used for different applications such as fracture detection, bone tumor diagnosis, detecting hip implant mechanical loosening, and grading osteoarthritis. As time goes on, the utility of artificial intelligence and machine learning algorithms, such as deep learning, continues to grow and expand in orthopaedic surgery. The purpose of this review is to provide an understanding of the concepts of machine learning and a background of current and future orthopaedic applications of machine learning in risk assessment, outcomes assessment, imaging, and basic science fields. In most cases, machine learning has proven to be just as effective, if not more effective, than prior methods such as logistic regression in assessment and prediction. With the help of deep learning algorithms, such as artificial neural networks and convolutional neural networks, artificial intelligence in orthopaedics has been able to improve diagnostic accuracy and speed, flag the most critical and urgent patients for immediate attention, reduce the amount of human error, reduce the strain on medical professionals, and improve care. Because machine learning has shown diagnostic and prognostic uses in orthopaedic surgery, physicians should continue to research these techniques and be trained to use these methods effectively in order to improve orthopaedic treatment.

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Prediction of pathologic femoral fractures in patients with lung cancer using machine learning algorithms: Comparison of computed tomography-based radiological features with clinical features versus without clinical features

Abstract: We believe that machine learning algorithms may be useful in the prediction of pathologic femoral fracture, which are multifactorial problem.

Cited by 23 publications

References 11 publications

Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model

Identifying Factors Associated With Severe Intellectual Disabilities in Teenagers With Cerebral Palsy Using a Predictive Learning Model

Dual X-Ray Image under Neural Network Adopted in Comparison of Efficacy and Safety of Deep Hydrolyzed Protein Milk Powder and Parenteral Nutrition in Intervention of Neonatal Noninfectious Abdominal Distension

Machine learning in orthopaedic surgery

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