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Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs.
TLDR
An algorithm based on deep machine learning had high sensitivity and specificity for detecting referable diabetic retinopathy and diabetic macular edema in retinal fundus photographs from adults with diabetes.
Detecting Cancer Metastases on Gigapixel Pathology Images
TLDR
This work presents a framework to automatically detect and localize tumors as small as 100 x 100 pixels in gigapixel microscopy images sized 100,000 x100,000 pixels and achieves image-level AUC scores above 97% on both the Camelyon16 test set and an independent set of 110 slides.
International evaluation of an AI system for breast cancer screening
TLDR
A robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening and using a combination of AI and human inputs could help to improve screening efficiency.
End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography
TLDR
A convolutional neural network performs automated prediction of malignancy risk of pulmonary nodules in chest CT scan volumes and improves accuracy of lung cancer screening.
Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning
TLDR
Deep learning predicts, from retinal images, cardiovascular risk factors—such as smoking status, blood pressure and age—not previously thought to be present or quantifiable in these images.
Grader variability and the importance of reference standards for evaluating machine learning models for diabetic retinopathy
TLDR
Adjudication reduces the errors in DR grading by using a small number of adjudicated consensus grades as a tuning dataset and higher-resolution images as input, and to train an improved automated algorithm for DR grading.
A deep learning system for differential diagnosis of skin diseases
TLDR
A deep learning system able to identify the most common skin conditions may help clinicians in making more accurate diagnoses in routine clinical practice.
Artificial intelligence and deep learning in ophthalmology
TLDR
There are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms.
Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer
TLDR
The potential of a deep learning algorithm to improve pathologist accuracy and efficiency in a digital pathology workflow is demonstrated by a multireader multicase study utilizing a proof of concept assistant tool.
Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer
TLDR
A deep learning system for Gleason scoring whole-slide images of prostatectomies, developed using 112 million pathologist-annotated image patches from 1226 slides, and evaluated on an independent validation dataset of 331 slides, achieves a significantly higher diagnostic accuracy and trended towards better patient risk stratification in correlations to clinical follow-up data.
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