AIMC Topic: Sensitivity and Specificity
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Artificial Intelligence Clinical Evidence Engine for Automatic Identification, Prioritization, and Extraction of Relevant Clinical Oncology Research.
JCO clinical cancer informatics
Jan 1, 2021
PURPOSE: We developed a system to automate analysis of the clinical oncology scientific literature from bibliographic databases and match articles to specific patient cohorts to answer specific questions regarding the efficacy of a treatment. The app...
Improved SSD network for accurate detection of optic disc and fovea and application in excyclotropia screening.
Journal of the Optical Society of America. A, Optics, image science, and vision
Jan 1, 2021
The detection of the optic disc (OD) and fovea is essential to many automatic diagnosis systems for retinal diseases. The single shot multibox detector (SSD) can generate predictions from feature maps of various resolutions, which has not been introd...
Analysis of Risk Factors in Dementia Through Machine Learning.
Journal of Alzheimer's disease : JAD
Jan 1, 2021
BACKGROUND: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer's disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiabl...
Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections.
Journal of X-ray science and technology
Jan 1, 2021
BACKGROUND: Accurate and rapid diagnosis of coronavirus disease (COVID-19) is crucial for timely quarantine and treatment.
Ultrasound image analysis using deep neural networks for discriminating between benign and malignant ovarian tumors: comparison with expert subjective assessment.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
Jan 1, 2021
OBJECTIVES: To develop and test the performance of computerized ultrasound image analysis using deep neural networks (DNNs) in discriminating between benign and malignant ovarian tumors and to compare its diagnostic accuracy with that of subjective a...
A Survey on Machine Learning and Deep Learning-based Computer-Aided Methods for Detection of Polyps in CT Colonography.
Current medical imaging
Jan 1, 2021
BACKGROUND: Colon cancer generally begins as a neoplastic growth of tissue, called polyps, originating from the inner lining of the colon wall. Most colon polyps are considered harmless but over the time, they can evolve into colon cancer, which, whe...
Adversarial attack on deep learning-based dermatoscopic image recognition systems: Risk of misdiagnosis due to undetectable image perturbations.
Medicine
Dec 11, 2020
Deep learning algorithms have shown excellent performances in the field of medical image recognition, and practical applications have been made in several medical domains. Little is known about the feasibility and impact of an undetectable adversaria...
[Establishment and test results of an artificial intelligence burn depth recognition model based on convolutional neural network].
Zhonghua shao shang za zhi = Zhonghua shaoshang zazhi = Chinese journal of burns
Nov 20, 2020
To establish an artificial intelligence burn depth recognition model based on convolutional neural network, and to test its effectiveness. In this evaluation study on diagnostic test, 484 wound photos of 221 burn patients in Xiangya Hospital of Cen...
Detection of Pathogenic Variants With Germline Genetic Testing Using Deep Learning vs Standard Methods in Patients With Prostate Cancer and Melanoma.
JAMA
Nov 17, 2020
IMPORTANCE: Less than 10% of patients with cancer have detectable pathogenic germline alterations, which may be partially due to incomplete pathogenic variant detection.
High-Throughput Nucleotide Sequencing
DNA Mutational Analysis
Female
Germ-Line Mutation
Humans
Melanoma
Neural Networks, Computer
Predictive Value of Tests
Sensitivity and Specificity
Male
Prostatic Neoplasms
Cross-Sectional Studies
Middle Aged
Genetic Testing
Deep Learning
Genetic Predisposition to Disease
Detection of COVID-19 Using Deep Learning Algorithms on Chest Radiographs.
Journal of thoracic imaging
Nov 1, 2020
PURPOSE: To evaluate the performance of a deep learning (DL) algorithm for the detection of COVID-19 on chest radiographs (CXR).
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