Dataset Information

DeepScreen: An Accurate, Rapid, and Anti-Interference Screening Approach for Nanoformulated Medication by Deep Learning.

ABSTRACT: Accuracy of current efficacy judgment methods for nanoformulated drug remains unstable due to the interference of nanocarriers. Herein, DeepScreen, a drug screening system utilizing convolutional neural network based on flow cytomerty single-cell images, is introduced. Compared to existing experimental approaches, the high-throughput system has superior precision, rapidity, and anti-interference, and is cost-cutting with high accuracy. First, it can resist most disturbances from manual factors of complicated evaluation progress. In addition, class activation maps generated from DeepScreen indicate that it may identify and locate the tiny variation from cell apoptosis and slight changes of cellular period caused by drug or even nanoformulated drug action at very early stages. More importantly, the excellent performance of assessment on two types of nanoformulations and fluorescent drug proves the fine generality and anti-interference of this novel system. All these privileged performances make DeepScreen a very smart and promising system for drug detection.

SUBMITTER: Zhu Y

PROVIDER: S-EPMC6145411 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

<i>DeepScreen</i>: An Accurate, Rapid, and Anti-Interference Screening Approach for Nanoformulated Medication by Deep Learning.

Zhu Yanjing Y Huang Ruiqi R Zhu Rui R Xu Wei W Zhu Rongrong R Cheng Liming L

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 20180723 9

Accuracy of current efficacy judgment methods for nanoformulated drug remains unstable due to the interference of nanocarriers. Herein, <i>DeepScreen</i>, a drug screening system utilizing convolutional neural network based on flow cytomerty single-cell images, is introduced. Compared to existing experimental approaches, the high-throughput system has superior precision, rapidity, and anti-interference, and is cost-cutting with high accuracy. First, it can resist most disturbances from manual fa ...[more]

PMID: 30250814

Similar Datasets

Project description:Purpose: To assess the accuracy and efficacy of a semi-automated deep learning algorithm (DLA) assisted approach to detect vision-threatening diabetic retinopathy (DR). Methods: We developed a two-step semi-automated DLA-assisted approach to grade fundus photographs for vision-threatening referable DR. Study images were obtained from the Lingtou Cohort Study, and captured at participant enrollment in 2009-2010 ("baseline images") and annual follow-up between 2011 and 2017. To begin, a validated DLA automatically graded baseline images for referable DR and classified them as positive, negative, or ungradable. Following, each positive image, all other available images from patients who had a positive image, and a 5% random sample of all negative images were selected and regraded by trained human graders. A reference standard diagnosis was assigned once all graders achieved consistent grading outcomes or with a senior ophthalmologist's final diagnosis. The semi-automated DLA assisted approach combined initial DLA screening and subsequent human grading for images identified as high-risk. This approach was further validated within the follow-up image datasets and its time and economic costs evaluated against fully human grading. Results: For evaluation of baseline images, a total of 33,115 images were included and automatically graded by the DLA. 2,604 images (480 positive results, 624 available other images from participants with a positive result, and 1500 random negative samples) were selected and regraded by graders. The DLA achieved an area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and accuracy of 0.953, 0.970, 0.879, and 88.6%, respectively. In further validation within the follow-up image datasets, a total of 88,363 images were graded using this semi-automated approach and human grading was performed on 8975 selected images. The DLA achieved an AUC, sensitivity, and specificity of 0.914, 0.852, 0.853, respectively. Compared against fully human grading, the semi-automated DLA-assisted approach achieved an estimated 75.6% time and 90.1% economic cost saving. Conclusions: The DLA described in this study was able to achieve high accuracy, sensitivity, and specificity in grading fundus images for referable DR. Validated against long-term follow-up datasets, a semi-automated DLA-assisted approach was able to accurately identify suspect cases, and minimize misdiagnosis whilst balancing safety, time, and economic cost.

Dataset Information

DeepScreen: An Accurate, Rapid, and Anti-Interference Screening Approach for Nanoformulated Medication by Deep Learning.

Publications

<i>DeepScreen</i>: An Accurate, Rapid, and Anti-Interference Screening Approach for Nanoformulated Medication by Deep Learning.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets