AIMC Topic: Middle Aged

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MR-based artificial intelligence model to assess response to therapy in locally advanced rectal cancer.

European journal of radiology Jun 22, 2019
PURPOSE: To develop and validate an Artificial Intelligence (AI) model based on texture analysis of high-resolution T2 weighted MR images able 1) to predict pathologic Complete Response (CR) and 2) to identify non-responders (NR) among patients with ...
Cohort Studies ROC Curve Treatment Outcome Neoadjuvant Therapy Magnetic Resonance Imaging Rectum Reproducibility of Results Aged Aged, 80 and over Humans Male Artificial Intelligence Middle Aged Rectal Neoplasms Prospective Studies Chemoradiotherapy, Adjuvant Retrospective Studies Female
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Prediction of early colorectal cancer metastasis by machine learning using digital slide images.

Computer methods and programs in biomedicine Jun 22, 2019
BACKGROUND AND OBJECTIVES: Prediction of lymph node metastasis (LNM) for early colorectal cancer (CRC) is critical for determining treatment strategies after endoscopic resection. Some histologic parameters for predicting LNM have been established, b...
Immunohistochemistry False Negative Reactions False Positive Reactions Keratins Male Middle Aged Lymph Nodes Lymphatic Metastasis Retrospective Studies Neoplasm Metastasis Female Humans Risk Factors Machine Learning Aged Algorithms Endoscopy Colorectal Neoplasms
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3D-CNN based discrimination of schizophrenia using resting-state fMRI.

Artificial intelligence in medicine Jun 22, 2019
MOTIVATION: This study reports a framework to discriminate patients with schizophrenia and normal healthy control subjects, based on magnetic resonance imaging (MRI) of the brain. Resting-state functional MRI data from a total of 144 subjects (72 pat...
Functional Neuroimaging Rest Humans Adult Magnetic Resonance Imaging Neural Networks, Computer Male Area Under Curve Brain Middle Aged Female Discriminant Analysis Case-Control Studies Schizophrenia Deep Learning Young Adult
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Performance of a Deep-Learning Neural Network to Detect Intracranial Aneurysms from 3D TOF-MRA Compared to Human Readers.

Clinical neuroradiology Jun 21, 2019
PURPOSE: To study the clinical potential of a deep learning neural network (convolutional neural networks [CNN]) as a supportive tool for detection of intracranial aneurysms from 3D time-of-flight magnetic resonance angiography (TOF-MRA) by comparing...
Imaging, Three-Dimensional Magnetic Resonance Angiography Intracranial Aneurysm Humans Retrospective Studies Female Sensitivity and Specificity Male Middle Aged Deep Learning Clinical Competence
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Estimation of the radiation dose in pregnancy: an automated patient-specific model using convolutional neural networks.

European radiology Jun 21, 2019
OBJECTIVES: The conceptus dose during diagnostic imaging procedures for pregnant patients raises health concerns owing to the high radiosensitivity of the developing embryo/fetus. The aim of this work is to develop a methodology for automated constru...
Phantoms, Imaging Radiometry Radiography, Abdominal Young Adult Adult Humans Middle Aged Radiation Dosage Monte Carlo Method Neural Networks, Computer Computer Simulation Tomography, X-Ray Computed Pregnancy Female
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Artificial intelligence and machine learning for predicting acute kidney injury in severely burned patients: A proof of concept.

Burns : journal of the International Society for Burn Injuries Jun 21, 2019
BACKGROUND: Burn critical care represents a high impact population that may benefit from artificial intelligence and machine learning (ML). Acute kidney injury (AKI) recognition in burn patients could be enhanced by ML. The goal of this study was to ...
Artificial Intelligence Machine Learning Acute Kidney Injury Female Young Adult Adult Male Middle Aged Peptide Fragments Lipocalin-2 Burns Humans Creatinine Natriuretic Peptide, Brain Urine Proof of Concept Study
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Artificial neural network models to predict nodal status in clinically node-negative breast cancer.

BMC cancer Jun 21, 2019
BACKGROUND: Sentinel lymph node biopsy (SLNB) is standard staging procedure for nodal status in breast cancer, but lacks therapeutic benefit for patients with benign sentinel nodes. For patients with positive sentinel nodes, individualized surgical s...
Retrospective Studies Female Aged Aged, 80 and over Young Adult Adult Humans Neural Networks, Computer Area Under Curve Tumor Burden Neovascularization, Pathologic Linear Models Receptors, Estrogen Sentinel Lymph Node Biopsy Breast Neoplasms Middle Aged Lymph Nodes Lymphatic Metastasis Axilla Carcinoma, Lobular
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A machine learning approach for predictive models of adverse events following spine surgery.

The spine journal : official journal of the North American Spine Society Jun 20, 2019
BACKGROUND: Rates of adverse events following spine surgery vary widely by patient-, diagnosis-, and procedure-related factors. It is critical to understand the expected rates of complications and to be able to implement targeted efforts at limiting ...
Middle Aged Risk Assessment Postoperative Complications Comorbidity Logistic Models United States Machine Learning Area Under Curve Humans Databases, Factual Spine Retrospective Studies Odds Ratio Female Orthopedic Procedures Aged Male
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Deep learning assisted mitotic counting for breast cancer.

Laboratory investigation; a journal of technical methods and pathology Jun 20, 2019
As part of routine histological grading, for every invasive breast cancer the mitotic count is assessed by counting mitoses in the (visually selected) region with the highest proliferative activity. Because this procedure is prone to subjectivity, th...
Diagnosis, Computer-Assisted Deep Learning Cohort Studies Female Mitotic Index Netherlands Humans Reproducibility of Results Observer Variation Neural Networks, Computer Aged Prospective Studies Adult Retrospective Studies Breast Neoplasms Middle Aged
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Machine Learning Prediction of Response to Cardiac Resynchronization Therapy: Improvement Versus Current Guidelines.

Circulation. Arrhythmia and electrophysiology Jun 20, 2019
BACKGROUND: Cardiac resynchronization therapy (CRT) has significant nonresponse rates. We assessed whether machine learning (ML) could predict CRT response beyond current guidelines.
Stroke Volume Echocardiography Middle Aged Risk Assessment Disease Progression Time Factors Progression-Free Survival Humans Female Practice Guidelines as Topic Machine Learning Risk Factors Heart-Assist Devices Recovery of Function Decision Support Techniques Aged Heart Transplantation Predictive Value of Tests Retrospective Studies Male Ohio Patient Selection Clinical Decision-Making Ventricular Function, Left Baltimore Heart Failure Cardiac Resynchronization Therapy
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