AIMC Topic: Sensitivity and Specificity
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Computer Vision Analysis of Intraoperative Video: Automated Recognition of Operative Steps in Laparoscopic Sleeve Gastrectomy.
Annals of surgery
Sep 1, 2019
OBJECTIVE(S): To develop and assess AI algorithms to identify operative steps in laparoscopic sleeve gastrectomy (LSG).
An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.
Journal of the National Cancer Institute
Sep 1, 2019
BACKGROUND: Human papillomavirus vaccination and cervical screening are lacking in most lower resource settings, where approximately 80% of more than 500 000 cancer cases occur annually. Visual inspection of the cervix following acetic acid applicati...
Humans
Uterine Cervical Neoplasms
Early Detection of Cancer
Algorithms
Middle Aged
Female
Aged, 80 and over
Area Under Curve
Population Surveillance
Deep Learning
Severity of Illness Index
Aged
Young Adult
Uterine Cervical Dysplasia
Image Processing, Computer-Assisted
Case-Control Studies
Adult
Colposcopy
Mass Screening
Sensitivity and Specificity
Cervix Uteri
Development and Validation of a Deep Learning-based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs.
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Aug 16, 2019
BACKGROUND: Detection of active pulmonary tuberculosis on chest radiographs (CRs) is critical for the diagnosis and screening of tuberculosis. An automated system may help streamline the tuberculosis screening process and improve diagnostic performan...
Tracheal Sound Analysis Using a Deep Neural Network to Detect Sleep Apnea.
Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine
Aug 15, 2019
STUDY OBJECTIVES: Portable devices for home sleep apnea testing are often limited by their inability to discriminate sleep/wake status, possibly resulting in underestimations. Tracheal sound (TS), which can be visualized as a spectrogram, carries inf...
The Classification of Renal Cancer in 3-Phase CT Images Using a Deep Learning Method.
Journal of digital imaging
Aug 1, 2019
In this research, we exploit an image-based deep learning framework to distinguish three major subtypes of renal cell carcinoma (clear cell, papillary, and chromophobe) using images acquired with computed tomography (CT). A biopsy-proven benchmarking...
Effectiveness of Deep Learning Algorithms to Determine Laterality in Radiographs.
Journal of digital imaging
Aug 1, 2019
Develop a highly accurate deep learning model to reliably classify radiographs by laterality. Digital Imaging and Communications in Medicine (DICOM) data for nine body parts was extracted retrospectively. Laterality was determined directly if encoded...
Improved Cancer Detection Using Artificial Intelligence: a Retrospective Evaluation of Missed Cancers on Mammography.
Journal of digital imaging
Aug 1, 2019
To determine whether cmAssist™, an artificial intelligence-based computer-aided detection (AI-CAD) algorithm, can be used to improve radiologists' sensitivity in breast cancer screening and detection. A blinded retrospective study was performed with ...
Ankle Fracture Detection Utilizing a Convolutional Neural Network Ensemble Implemented with a Small Sample, De Novo Training, and Multiview Incorporation.
Journal of digital imaging
Aug 1, 2019
To determine whether we could train convolutional neural network (CNN) models de novo with a small dataset, a total of 596 normal and abnormal ankle cases were collected and processed. Single- and multiview models were created to determine the effect...
Reduction of False-Positive Markings on Mammograms: a Retrospective Comparison Study Using an Artificial Intelligence-Based CAD.
Journal of digital imaging
Aug 1, 2019
The aim was to determine whether an artificial intelligence (AI)-based, computer-aided detection (CAD) software can be used to reduce false positive per image (FPPI) on mammograms as compared to an FDA-approved conventional CAD. A retrospective study...
Utility of machine learning algorithms in assessing patients with a systemic right ventricle.
European heart journal. Cardiovascular Imaging
Aug 1, 2019
AIMS: To investigate the utility of novel deep learning (DL) algorithms in recognizing transposition of the great arteries (TGA) after atrial switch procedure or congenitally corrected TGA (ccTGA) based on routine transthoracic echocardiograms. In ad...
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