On the role of a mutational database in building artificial intelligence models for understanding gene expression

1997 ◽  
Vol 27 (3) ◽  
pp. 227-230
Author(s):  
T. Murlidharan Nair ◽  
B. D. Kulkarni
Keyword(s):  
Gene Expression ◽  
Artificial Intelligence ◽  
Intelligence Models ◽  
Artificial Intelligence Models

Screening For Bone Marrow Cellularity Changes in Cynomolgus Macaques in Toxicology Safety Studies Using Artificial Intelligence Models

2021 ◽  
pp. 019262332098156 ◽  
Author(s):  
Mark A. Smith ◽  
Thomas Westerling-Bui ◽  
Angela Wilcox ◽  
Julie Schwartz
Keyword(s):  
Artificial Intelligence ◽  
Bone Marrow ◽  
Hematopoietic Cells ◽  
Severity Scores ◽  
Intelligence Models ◽  
Safety Studies ◽  
Artificial Intelligence Models ◽  
Marrow Cellularity ◽  
Whole Slide Images

Many compounds affect the cellularity of hematolymphoid organs including bone marrow. Toxicologic pathologists are tasked with their evaluation as part of safety studies. An artificial intelligence (AI) tool could provide diagnostic support for the pathologist. We looked at the ability of a deep-learning AI model to evaluate whole slide images of macaque sternebrae to identify and enumerate bone marrow hematopoietic cells. The AI model was trained and able to differentiate the hematopoietic cells from the other sternebrae tissues. We compared the model to severity scores in a study with decreased hematopoietic cellularity. The mean cells/mm2 from the model was lower for each increase in severity score. The AI model was trained by 1 pathologist, providing proof of concept that AI model generation can be fast and agile, without the need of a cross disciplinary team and significant effort. We see great potential for the role of AI-based bone marrow screening.

scholarly journals Stress testing reveals gaps in clinic readiness of image-based diagnostic artificial intelligence models

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Albert T. Young ◽  
Kristen Fernandez ◽  
Jacob Pfau ◽  
Rasika Reddy ◽  
Nhat Anh Cao ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Stress Testing ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Skin Lesions ◽  
Stress Tests ◽  
Performance Metric ◽  
Intelligence Models ◽  
Artificial Intelligence Models

AbstractArtificial intelligence models match or exceed dermatologists in melanoma image classification. Less is known about their robustness against real-world variations, and clinicians may incorrectly assume that a model with an acceptable area under the receiver operating characteristic curve or related performance metric is ready for clinical use. Here, we systematically assessed the performance of dermatologist-level convolutional neural networks (CNNs) on real-world non-curated images by applying computational “stress tests”. Our goal was to create a proxy environment in which to comprehensively test the generalizability of off-the-shelf CNNs developed without training or evaluation protocols specific to individual clinics. We found inconsistent predictions on images captured repeatedly in the same setting or subjected to simple transformations (e.g., rotation). Such transformations resulted in false positive or negative predictions for 6.5–22% of skin lesions across test datasets. Our findings indicate that models meeting conventionally reported metrics need further validation with computational stress tests to assess clinic readiness.

scholarly journals How can artificial intelligence models assist PD-L1 expression scoring in breast cancer: results of multi-institutional ring studies

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Xinran Wang ◽  
Liang Wang ◽  
Hong Bu ◽  
Ningning Zhang ◽  
Meng Yue ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Clinical Practice ◽  
Subjective Assessment ◽  
Current Guideline ◽  
Scoring Method ◽  
Programmed Death ◽  
Intelligence Models ◽  
Artificial Intelligence Models ◽  
Scoring Accuracy

AbstractProgrammed death ligand-1 (PD-L1) expression is a key biomarker to screen patients for PD-1/PD-L1-targeted immunotherapy. However, a subjective assessment guide on PD-L1 expression of tumor-infiltrating immune cells (IC) scoring is currently adopted in clinical practice with low concordance. Therefore, a repeatable and quantifiable PD-L1 IC scoring method of breast cancer is desirable. In this study, we propose a deep learning-based artificial intelligence-assisted (AI-assisted) model for PD-L1 IC scoring. Three rounds of ring studies (RSs) involving 31 pathologists from 10 hospitals were carried out, using the current guideline in the first two rounds (RS1, RS2) and our AI scoring model in the last round (RS3). A total of 109 PD-L1 (Ventana SP142) immunohistochemistry (IHC) stained images were assessed and the role of the AI-assisted model was evaluated. With the assistance of AI, the scoring concordance across pathologists was boosted to excellent in RS3 (0.950, 95% confidence interval (CI): 0.936–0.962) from moderate in RS1 (0.674, 95% CI: 0.614–0.735) and RS2 (0.736, 95% CI: 0.683–0.789). The 2- and 4-category scoring accuracy were improved by 4.2% (0.959, 95% CI: 0.953–0.964) and 13% (0.815, 95% CI: 0.803–0.827) (p < 0.001). The AI results were generally accepted by pathologists with 61% “fully accepted” and 91% “almost accepted”. The proposed AI-assisted method can help pathologists at all levels to improve the PD-L1 assay (SP-142) IC assessment in breast cancer in terms of both accuracy and concordance. The AI tool provides a scheme to standardize the PD-L1 IC scoring in clinical practice.

Sign in / Sign up

Export Citation Format

Share Document