AIMC Topic: Area Under Curve

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Clinical Data Extraction and Normalization of Cyrillic Electronic Health Records Via Deep-Learning Natural Language Processing.

JCO clinical cancer informatics
PURPOSE: A substantial portion of medical data is unstructured. Extracting data from unstructured text presents a barrier to advancing clinical research and improving patient care. In addition, ongoing studies have been focused predominately on the E...

Radiomics MRI Phenotyping with Machine Learning to Predict the Grade of Lower-Grade Gliomas: A Study Focused on Nonenhancing Tumors.

Korean journal of radiology
OBJECTIVE: To assess whether radiomics features derived from multiparametric MRI can predict the tumor grade of lower-grade gliomas (LGGs; World Health Organization grade II and grade III) and the nonenhancing LGG subgroup.

Stand-Alone Artificial Intelligence for Breast Cancer Detection in Mammography: Comparison With 101 Radiologists.

Journal of the National Cancer Institute
BACKGROUND: Artificial intelligence (AI) systems performing at radiologist-like levels in the evaluation of digital mammography (DM) would improve breast cancer screening accuracy and efficiency. We aimed to compare the stand-alone performance of an ...

An Observational Study of Deep Learning and Automated Evaluation of Cervical Images for Cancer Screening.

Journal of the National Cancer Institute
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...

Deep Learning-Based Radiomics Models for Early Recurrence Prediction of Hepatocellular Carcinoma with Multi-phase CT Images and Clinical Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Hepatocellular carcinoma (HCC) is the fifth most common malignancy in the world and the second most common cause of cancer-related death. By surgically removing hepatocellular carcinoma, the patients may have the early recurrence within one year. Rec...

Classification of radiographic lung pattern based on texture analysis and machine learning.

Journal of veterinary science
This study evaluated the feasibility of using texture analysis and machine learning to distinguish radiographic lung patterns. A total of 1200 regions of interest (ROIs) including four specific lung patterns (normal, alveolar, bronchial, and unstruct...

Detection of Brain Activation in Unresponsive Patients with Acute Brain Injury.

The New England journal of medicine
BACKGROUND: Brain activation in response to spoken motor commands can be detected by electroencephalography (EEG) in clinically unresponsive patients. The prevalence and prognostic importance of a dissociation between commanded motor behavior and bra...

[Research on algorithms for identifying the severity of acute respiratory distress syndrome patients based on noninvasive parameters].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi
Acute respiratory distress syndrome (ARDS) is a serious threat to human life and health disease, with acute onset and high mortality. The current diagnosis of the disease depends on blood gas analysis results, while calculating the oxygenation index....

Application of deep learning (3-dimensional convolutional neural network) for the prediction of pathological invasiveness in lung adenocarcinoma: A preliminary study.

Medicine
To compare results for radiological prediction of pathological invasiveness in lung adenocarcinoma between radiologists and a deep learning (DL) system.Ninety patients (50 men, 40 women; mean age, 66 years; range, 40-88 years) who underwent pre-opera...

A Deep Learning-Based Decision Support Tool for Precision Risk Assessment of Breast Cancer.

JCO clinical cancer informatics
PURPOSE: The Breast Imaging Reporting and Data System (BI-RADS) lexicon was developed to standardize mammographic reporting to assess cancer risk and facilitate the decision to biopsy. Because of substantial interobserver variability in the applicati...