AIMC Topic: Predictive Value of Tests

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Learning physical properties in complex visual scenes: An intelligent machine for perceiving blood flow dynamics from static CT angiography imaging.

Neural networks : the official journal of the International Neural Network Society Nov 30, 2019
Humans perceive physical properties such as motion and elastic force by observing objects in visual scenes. Recent research has proven that computers are capable of inferring physical properties from camera images like humans. However, few studies pe...
Humans Coronary Stenosis Machine Learning Aged Fractional Flow Reserve, Myocardial Male Retrospective Studies Middle Aged Physical Phenomena Female Hemodynamics Predictive Value of Tests Coronary Angiography Pattern Recognition, Automated Blood Flow Velocity Computed Tomography Angiography
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Clinical calculator predictive of chemotherapy benefit in stage 1A uterine papillary serous cancers.

Gynecologic oncology Nov 30, 2019
OBJECTIVE: Determine the utility of a clinical calculator to predict the benefit of chemotherapy in stage IA uterine papillary serous cancer (UPSC).
Cystadenocarcinoma, Serous Cystadenocarcinoma, Papillary Nomograms Humans Predictive Value of Tests Algorithms Machine Learning Neoplasm Staging Prognosis Uterine Neoplasms Female Proportional Hazards Models Reproducibility of Results Aged
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An overview of deep learning algorithms and water exchange in colonoscopy in improving adenoma detection.

Expert review of gastroenterology & hepatology Nov 30, 2019
: Among the Gastrointestinal (GI) Endoscopy Editorial Board top 10 topics in advances in endoscopy in 2018, water exchange colonoscopy and artificial intelligence were both considered important advances. Artificial intelligence holds the potential to...
Image Interpretation, Computer-Assisted Adenomatous Polyps Humans Reproducibility of Results Deep Learning Colonoscopy Diagnosis, Computer-Assisted Water Predictive Value of Tests Colonic Neoplasms Colonic Polyps
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[Predicting atrial fibrillation through a sinus-rhythm electrocardiogram; useful or not?].

Nederlands tijdschrift voor geneeskunde Nov 28, 2019
In patients with cryptogenic stroke, the detection of atrial fibrillation (AF) is important, since it is an indication for the prescription of oral anticoagulation, instead of anti-platelet therapy, to decrease the chance of a recurrent ischaemic cer...
Atrial Fibrillation Reproducibility of Results Humans Electrocardiography Artificial Intelligence Predictive Value of Tests Algorithms Decision Support Techniques Anticoagulants Stroke
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The exploration of feature extraction and machine learning for predicting bone density from simple spine X-ray images in a Korean population.

Skeletal radiology Nov 23, 2019
OBJECTIVE: Osteoporosis is hard to detect before it manifests symptoms and complications. In this study, we evaluated machine learning models for identifying individuals with abnormal bone mineral density (BMD) through an analysis of spine X-ray feat...
Humans Absorptiometry, Photon Machine Learning Bone Density Retrospective Studies Republic of Korea Osteoporosis Female Sensitivity and Specificity Male Middle Aged Predictive Value of Tests Radiography Radiographic Image Interpretation, Computer-Assisted Spine
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Machine learning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy.

Breast (Edinburgh, Scotland) Nov 23, 2019
In patients with locally advanced breast cancer undergoing neoadjuvant chemotherapy (NAC), some patients achieve a complete pathologic response (pCR), some achieve a partial response, and some do not respond at all or even progress. Accurate predicti...
Neoadjuvant Therapy Predictive Value of Tests Breast Chemotherapy, Adjuvant Multiparametric Magnetic Resonance Imaging Female Humans Adult Machine Learning Breast Neoplasms Middle Aged Treatment Outcome
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Deep segmentation networks predict survival of non-small cell lung cancer.

Scientific reports Nov 21, 2019
Non-small-cell lung cancer (NSCLC) represents approximately 80-85% of lung cancer diagnoses and is the leading cause of cancer-related death worldwide. Recent studies indicate that image-based radiomics features from positron emission tomography/comp...
Neoplasm Staging Radiopharmaceuticals Datasets as Topic Radiosurgery Male Middle Aged Lung Deep Learning Image Processing, Computer-Assisted Lung Neoplasms Treatment Outcome Carcinoma, Non-Small-Cell Lung Retrospective Studies Predictive Value of Tests Female Positron Emission Tomography Computed Tomography Humans Aged Prognosis Aged, 80 and over Survival Rate
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Prediction of visual outcomes by an artificial neural network following intravitreal injection and laser therapy for retinopathy of prematurity.

The British journal of ophthalmology Nov 19, 2019
AIMS: To construct a program to predict the visual acuity (VA), best corrected VA (BCVA) and spherical equivalent (SE) of patients with retinopathy of prematurity (ROP) from 3 to 12 years old after intravitreal injection (IVI) of anti-vascular endoth...
Retinopathy of Prematurity Bevacizumab Laser Coagulation Male Humans Refraction, Ocular Neural Networks, Computer Child, Preschool Visual Acuity Retrospective Studies Child Predictive Value of Tests Female Angiogenesis Inhibitors Vascular Endothelial Growth Factor A Intravitreal Injections Ranibizumab
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Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning.

Radiology Nov 19, 2019
Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve incre...
Humans Neural Networks, Computer Algorithms Retrospective Studies Female Breast Neoplasms Middle Aged Deep Learning Cohort Studies Lymph Nodes Aged Aged, 80 and over Young Adult Adult Sensitivity and Specificity Image Interpretation, Computer-Assisted Lymphatic Metastasis Predictive Value of Tests Ultrasonography, Mammary Feasibility Studies
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Machine learning-based quantitative texture analysis of conventional MRI combined with ADC maps for assessment of IDH1 mutation in high-grade gliomas.

Japanese journal of radiology Nov 18, 2019
PURPOSE: To assess the performance of texture analysis of conventional magnetic resonance imaging (MRI) and apparent diffusion coefficient (ADC) maps in predicting IDH1 status in high-grade gliomas (HGG).
Machine Learning Brain Retrospective Studies Female Reproducibility of Results Humans Magnetic Resonance Imaging Predictive Value of Tests Mutation Image Interpretation, Computer-Assisted Isocitrate Dehydrogenase Adult Brain Neoplasms Male Middle Aged Glioma
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