AIMC Topic: Retrospective Studies

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Radiologic-Radiomic Machine Learning Models for Differentiation of Benign and Malignant Solid Renal Masses: Comparison With Expert-Level Radiologists.

AJR. American journal of roentgenology Sep 25, 2019
The objective of our study was to compare the performance of radiologicradiomic machine learning (ML) models and expert-level radiologists for differentiation of benign and malignant solid renal masses using contrast-enhanced CT examinations. This ...
Kidney Neoplasms Adult Male Middle Aged Clinical Competence Models, Theoretical Magnetic Resonance Imaging Kidney Diseases Diagnosis, Differential Radiology Tomography, X-Ray Computed Female Aged Aged, 80 and over Young Adult Humans Machine Learning Retrospective Studies
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Prediction of complication related death after radical cystectomy for bladder cancer with machine learning methodology.

Scandinavian journal of urology Sep 25, 2019
To create a pre-operatively usable tool to identify patients at high risk of early death (within 90 days post-operatively) after radical cystectomy and to assess potential risk factors for post-operative and surgery related mortality. Material consi...
Retrospective Studies Female Risk Factors Aged Aged, 80 and over Humans Machine Learning Prognosis Cystectomy Male Middle Aged Risk Assessment Postoperative Complications Urinary Bladder Neoplasms
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Diagnosis and classification of pediatric acute appendicitis by artificial intelligence methods: An investigator-independent approach.

PloS one Sep 25, 2019
Acute appendicitis is one of the major causes for emergency surgery in childhood and adolescence. Appendectomy is still the therapy of choice, but conservative strategies are increasingly being studied for uncomplicated inflammation. Diagnosis of acu...
Germany Infant Blood Cell Count Diagnosis, Computer-Assisted Appendectomy Humans Biomarkers Child Adolescent Appendix Artificial Intelligence Retrospective Studies Ultrasonography Child, Preschool Female Machine Learning ROC Curve Infant, Newborn Algorithms C-Reactive Protein Male Appendicitis
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Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.

Ophthalmology Sep 24, 2019
PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH) features from color fundus images, to determine the relative importance of these features in referr...
Aged Sensitivity and Specificity Male Middle Aged Deep Learning ROC Curve Area Under Curve Referral and Consultation Datasets as Topic Humans Retrospective Studies Female Ophthalmologists Glaucoma, Open-Angle Nerve Fibers Specialization Optic Disk Retinal Ganglion Cells Optic Nerve Diseases
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Using machine learning to understand neuromorphological change and image-based biomarker identification in Cavalier King Charles Spaniels with Chiari-like malformation-associated pain and syringomyelia.

Journal of veterinary internal medicine Sep 24, 2019
BACKGROUND: Chiari-like malformation (CM) is a complex malformation of the skull and cranial cervical vertebrae that potentially results in pain and secondary syringomyelia (SM). Chiari-like malformation-associated pain (CM-P) can be challenging to d...
Machine Learning Biomarkers Retrospective Studies Female Syringomyelia Sensitivity and Specificity Area Under Curve Animals Dogs Male Dog Diseases ROC Curve Pain Arnold-Chiari Malformation
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Machine learning based risk prediction model for asymptomatic individuals who underwent coronary artery calcium score: Comparison with traditional risk prediction approaches.

Journal of cardiovascular computed tomography Sep 23, 2019
BACKGROUND: Machine learning (ML) is a computer algorithm used to identify patterns for prediction in various tasks, and ML methods have been beneficial for developing prediction models when applied to heterogeneous and large datasets. We aim to exam...
Humans Vascular Calcification Machine Learning Asymptomatic Diseases Decision Support Techniques Male Predictive Value of Tests Databases, Factual Middle Aged Coronary Angiography Prognosis Risk Assessment Retrospective Studies Registries Time Factors Republic of Korea Coronary Artery Disease Health Status Female Risk Factors Adult
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Deep-learning model for predicting 30-day postoperative mortality.

British journal of anaesthesia Sep 23, 2019
BACKGROUND: Postoperative mortality occurs in 1-2% of patients undergoing major inpatient surgery. The currently available prediction tools using summaries of intraoperative data are limited by their inability to reflect shifting risk associated with...
Missouri Algorithms Retrospective Studies Risk Assessment Postoperative Complications Comorbidity Deep Learning Humans Support Vector Machine Neural Networks, Computer Predictive Value of Tests Surgical Procedures, Operative Postoperative Period
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Deep learning to convert unstructured CT pulmonary angiography reports into structured reports.

European radiology experimental Sep 23, 2019
BACKGROUND: Structured reports have been shown to improve communication between radiologists and providers. However, some radiologists are concerned about resultant decreased workflow efficiency. We tested a machine learning-based algorithm designed ...
Humans Algorithms Retrospective Studies Deep Learning Medical Records Computed Tomography Angiography Pulmonary Artery
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Identification of Vertebral Fractures by Convolutional Neural Networks to Predict Nonvertebral and Hip Fractures: A Registry-based Cohort Study of Dual X-ray Absorptiometry.

Radiology Sep 17, 2019
Background Detection of vertebral fractures (VFs) aids in management of osteoporosis and targeting of fracture prevention therapies. Purpose To determine whether convolutional neural networks (CNNs) can be trained to identify VFs at VF assessment (VF...
Male Hip Fractures Absorptiometry, Photon Spinal Fractures Osteoporotic Fractures Prognosis Retrospective Studies Female Aged Humans Neural Networks, Computer
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Automated Liver Fat Quantification at Nonenhanced Abdominal CT for Population-based Steatosis Assessment.

Radiology Sep 17, 2019
Background Nonalcoholic fatty liver disease and its consequences are a growing public health concern requiring cross-sectional imaging for noninvasive diagnosis and quantification of liver fat. Purpose To investigate a deep learning-based automated l...
Middle Aged Prevalence Deep Learning Non-alcoholic Fatty Liver Disease Radiography, Abdominal Humans Retrospective Studies Tomography, X-Ray Computed Female Male
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