AIMC Topic: Sensitivity and Specificity

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Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

European radiology Mar 11, 2020
OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the performance of a deep learning algorithm for detection of post-biopsy pneumothorax in chest radiographs ...
Retrospective Studies Female Aged Adult Sensitivity and Specificity Radiography, Thoracic Male Middle Aged Lung Biopsy, Needle Deep Learning Pneumothorax Humans Cohort Studies Algorithms ROC Curve Area Under Curve Radiologists
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AIBx, Artificial Intelligence Model to Risk Stratify Thyroid Nodules.

Thyroid : official journal of the American Thyroid Association Mar 11, 2020
Current classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivity in medical image interpretation. One out of 2 women over the age of 50 years may have a thyroid n...
Sensitivity and Specificity Carcinoma, Papillary, Follicular Humans Ultrasonography Risk Assessment Thyroid Nodule Artificial Intelligence Thyroid Gland Image Interpretation, Computer-Assisted Algorithms Thyroid Neoplasms Adenocarcinoma, Follicular Thyroid Cancer, Papillary
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Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders.

Sensors (Basel, Switzerland) Mar 11, 2020
In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks having a small number of ground-truth images. We evaluate the proposed deep network architecture for the ...
Image Interpretation, Computer-Assisted Nevus Melanocytes Sensitivity and Specificity Deep Learning Skin Supervised Machine Learning Humans Algorithms Melanoma
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Monitoring Glaucomatous Functional Loss Using an Artificial Intelligence-Enabled Dashboard.

Ophthalmology Mar 10, 2020
PURPOSE: To develop an artificial intelligence (AI) dashboard for monitoring glaucomatous functional loss.
Humans Glaucoma Artificial Intelligence Retrospective Studies Cross-Sectional Studies Male Middle Aged Longitudinal Studies Monitoring, Physiologic Female Aged Adult Sensitivity and Specificity False Negative Reactions Optic Nerve Diseases Visual Acuity Vision Disorders Predictive Value of Tests Visual Fields
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A pilot trial of Convolution Neural Network for automatic retention-monitoring of capsule endoscopes in the stomach and duodenal bulb.

Scientific reports Mar 5, 2020
The retention of a capsule endoscope (CE) in the stomach and the duodenal bulb during the examination is a troublesome problem, which can make the medical staff spend several hours observing whether the CE enters the descending segment of the duodenu...
Foreign Bodies Duodenum Algorithms Automation Retrospective Studies Middle Aged Capsule Endoscopy Female Humans Stomach Aged Adolescent Neural Networks, Computer Capsule Endoscopes Aged, 80 and over Monitoring, Intraoperative Image Processing, Computer-Assisted Young Adult Adult Sensitivity and Specificity Male
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Using Natural Language Processing to improve EHR Structured Data-based Surgical Site Infection Surveillance.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Surgical Site Infection surveillance in healthcare systems is labor intensive and plagued by underreporting as current methodology relies heavily on manual chart review. The rapid adoption of electronic health records (EHRs) has the potential to allo...
Surgical Wound Infection Sensitivity and Specificity Humans Information Storage and Retrieval Logistic Models Machine Learning Support Vector Machine Decision Trees Algorithms Natural Language Processing Electronic Health Records
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Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.

AMIA ... Annual Symposium proceedings. AMIA Symposium Mar 4, 2020
Hospital acquired pneumonia (HAP) is the second most common nosocomial infection in the ICU and costs an estimated $3.1 billion annually. The ability to predict HAP could improve patient outcomes and reduce costs. Traditional pneumonia risk predictio...
Humans Sensitivity and Specificity Intensive Care Units Risk Assessment Deep Learning Healthcare-Associated Pneumonia
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Comparing machine and human reviewers to evaluate the risk of bias in randomized controlled trials.

Research synthesis methods Mar 3, 2020
BACKGROUND: Evidence from new health technologies is growing, along with demands for evidence to inform policy decisions, creating challenges in completing health technology assessments (HTAs)/systematic reviews (SRs) in a timely manner. Software can...
Risk Bias Outcome Assessment, Health Care Observer Variation Pattern Recognition, Automated Humans Machine Learning Software Reproducibility of Results Sensitivity and Specificity Risk Assessment Health Policy Randomized Controlled Trials as Topic
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Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms.

JAMA network open Mar 2, 2020
IMPORTANCE: Mammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives.
Female Sweden Aged Adult Sensitivity and Specificity Humans Artificial Intelligence Algorithms Early Detection of Cancer Mammography Radiology Radiologists Image Interpretation, Computer-Assisted Breast Neoplasms Middle Aged Deep Learning United States
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CT-based radiomics and machine learning to predict spread through air space in lung adenocarcinoma.

European radiology Feb 28, 2020
PURPOSE: Spread through air space (STAS) is a novel invasive pattern of lung adenocarcinoma and is also a risk factor for recurrence and worse prognosis of lung adenocarcinoma. The aims of this study are to develop and validate a computed tomography ...
Risk Factors Sensitivity and Specificity Male Humans Middle Aged Machine Learning Image Processing, Computer-Assisted Neoplasm Recurrence, Local ROC Curve Lung Neoplasms Retrospective Studies Adenocarcinoma of Lung Logistic Models Tomography, X-Ray Computed Neoplasm Invasiveness Female
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