AIMC Topic: Data Accuracy
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Deep learning systems detect dysplasia with human-like accuracy using histopathology and probe-based confocal laser endomicroscopy.
Scientific reports
Mar 3, 2021
Probe-based confocal laser endomicroscopy (pCLE) allows for real-time diagnosis of dysplasia and cancer in Barrett's esophagus (BE) but is limited by low sensitivity. Even the gold standard of histopathology is hindered by poor agreement between path...
High-dimensional hepatopath data analysis by machine learning for predicting HBV-related fibrosis.
Scientific reports
Mar 3, 2021
Chronic HBV infection, the main cause of liver cirrhosis and hepatocellular carcinoma, has become a global health concern. Machine learning algorithms are particularly adept at analyzing medical phenomenon by capturing complex and nonlinear relations...
Generating real-world evidence from unstructured clinical notes to examine clinical utility of genetic tests: use case in BRCAness.
BMC medical informatics and decision making
Jan 6, 2021
BACKGROUND: Next-generation sequencing provides comprehensive information about individuals' genetic makeup and is commonplace in oncology clinical practice. However, the utility of genetic information in the clinical decision-making process has not ...
MeshCut data augmentation for deep learning in computer vision.
PloS one
Dec 23, 2020
To solve overfitting in machine learning, we propose a novel data augmentation method called MeshCut, which uses a mesh-like mask to segment the whole image to achieve more partial diversified information. In our experiments, this strategy outperform...
Development and validation of an artificial intelligence system for grading colposcopic impressions and guiding biopsies.
BMC medicine
Dec 22, 2020
BACKGROUND: Colposcopy diagnosis and directed biopsy are the key components in cervical cancer screening programs. However, their performance is limited by the requirement for experienced colposcopists. This study aimed to develop and validate a Colp...
A Method to Improve Availability and Quality of Patient Race Data in an Electronic Health Record System.
Applied clinical informatics
Nov 25, 2020
BACKGROUND: Although federal regulations mandate documentation of structured race data according to Office of Management and Budget (OMB) categories in electronic health record (EHR) systems, many institutions have reported gaps in EHR race data that...
Deep-learning algorithms for the interpretation of chest radiographs to aid in the triage of COVID-19 patients: A multicenter retrospective study.
PloS one
Nov 24, 2020
The recent medical applications of deep-learning (DL) algorithms have demonstrated their clinical efficacy in improving speed and accuracy of image interpretation. If the DL algorithm achieves a performance equivalent to that achieved by physicians i...
Triage
Radiologists
Radiographic Image Interpretation, Computer-Assisted
Republic of Korea
Data Accuracy
Aged
Humans
Male
Radiography, Thoracic
Aged, 80 and over
Female
COVID-19
Middle Aged
Reproducibility of Results
Retrospective Studies
Adult
Deep Learning
SARS-CoV-2
Tomography, X-Ray Computed
Sensitivity and Specificity
Reverse Transcriptase Polymerase Chain Reaction
Discriminating pseudoprogression and true progression in diffuse infiltrating glioma using multi-parametric MRI data through deep learning.
Scientific reports
Nov 23, 2020
Differentiating pseudoprogression from true tumor progression has become a significant challenge in follow-up of diffuse infiltrating gliomas, particularly high grade, which leads to a potential treatment delay for patients with early glioma recurren...
Implementation of convolutional neural network approach for COVID-19 disease detection.
Physiological genomics
Oct 23, 2020
In this paper, two novel, powerful, and robust convolutional neural network (CNN) architectures are designed and proposed for two different classification tasks using publicly available data sets. The first architecture is able to decide whether a gi...
Recommendations for Reporting Machine Learning Analyses in Clinical Research.
Circulation. Cardiovascular quality and outcomes
Oct 14, 2020
Use of machine learning (ML) in clinical research is growing steadily given the increasing availability of complex clinical data sets. ML presents important advantages in terms of predictive performance and identifying undiscovered subpopulations of ...
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