Pathology

Modelling kidney disease using ontology: insights from the Kidney Precision Medicine Project.

An important need exists to better understand and stratify kidney disease according to its underlying pathophysiology in order to develop more precise and effective therapeutic agents. National collaborative efforts such as the Kidney Precision Medicine Project are working towards this goal through the collection and integration of large, disparate clinical, biological and imaging data from patien...

Deep-learning-assisted biophysical imaging cytometry at massive throughput delineates cell population heterogeneity.

The association of the intrinsic optical and biophysical properties of cells to homeostasis and pathogenesis has long been acknowledged. Defining these label-free cellular features obviates the need for costly and time-consuming labelling protocols that perturb the living cells. However, wide-ranging applicability of such label-free cell-based assays requires sufficient throughput, statistical pow...

Evaluation of the Classification Accuracy of the Kidney Biopsy Direct Immunofluorescence through Convolutional Neural Networks.

BACKGROUND AND OBJECTIVES: Immunohistopathology is an essential technique in the diagnostic workflow of a kidney biopsy. Deep learning is an effective...

Evaluation of the Diagnostic Accuracy of an Online Artificial Intelligence Application for Skin Disease Diagnosis.

Artificial intelligence (AI) algorithms for automated classification of skin diseases are available to the consumer market. Studies of their diagnosti...

Artificial Intelligence for the Prediction of Helicobacter Pylori Infection in Endoscopic Images: Systematic Review and Meta-Analysis Of Diagnostic Test Accuracy.

BACKGROUND: Helicobacter pylori plays a central role in the development of gastric cancer, and prediction of H pylori infection by visual inspection o...

Modeling adult skeletal stem cell response to laser-machined topographies through deep learning.

The response of adult human bone marrow stromal stem cells to surface topographies generated through femtosecond laser machining can be predicted by a...

Machine Learning-Based MRI Texture Analysis to Predict the Histologic Grade of Oral Squamous Cell Carcinoma.

This study aimed to explore the performance of machine learning (ML)-based MRI texture analysis in discriminating between well-differentiated (WD) or...

Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives.

Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With ...

A detection method for android application security based on TF-IDF and machine learning.

Android is the most widely used mobile operating system (OS). A large number of third-party Android application (app) markets have emerged. The absenc...

Automatic deep learning-based colorectal adenoma detection system and its similarities with pathologists.

OBJECTIVES: The microscopic evaluation of slides has been gradually moving towards all digital in recent years, leading to the possibility for compute...

Automated detection of cribriform growth patterns in prostate histology images.

Cribriform growth patterns in prostate carcinoma are associated with poor prognosis. We aimed to introduce a deep learning method to detect such patte...

A Comparative Analysis of Two Machine Learning-Based Diagnostic Patterns with Thyroid Imaging Reporting and Data System for Thyroid Nodules: Diagnostic Performance and Unnecessary Biopsy Rate.

The risk stratification system of the American College of Radiology Thyroid Imaging Reporting and Data System (ACR TI-RADS) for thyroid nodules is af...

Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation.

Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts...

Synthesis of diagnostic quality cancer pathology images by generative adversarial networks.

Deep learning-based computer vision methods have recently made remarkable breakthroughs in the analysis and classification of cancer pathology images....

Design and development of a non-contact robotic gripper for tissue manipulation in minimally invasive surgery.

This paper describes the design and testing of a gripper developed for handling of delicate and flexible tissues during minimally invasive surgery. Th...

Artificial intelligence technology applications in the pathologic diagnosis of the gastrointestinal tract.

Artificial intelligence (AI) is a complex technology with a steady flow of new applications, including in the pathology laboratory. Applications of AI...

Classification of parotid gland tumors by using multimodal MRI and deep learning.

Various MRI sequences have shown their potential to discriminate parotid gland tumors, including but not limited to T -weighted, postcontrast T -weigh...

High throughput assessment of biomarkers in tissue microarrays using artificial intelligence: PTEN loss as a proof-of-principle in multi-center prostate cancer cohorts.

Phosphatase and tensin homolog (PTEN) loss is associated with adverse outcomes in prostate cancer and has clinical potential as a prognostic biomarker...

T1 and T2 MR fingerprinting measurements of prostate cancer and prostatitis correlate with deep learning-derived estimates of epithelium, lumen, and stromal composition on corresponding whole mount histopathology.

OBJECTIVES: To explore the associations between T1 and T2 magnetic resonance fingerprinting (MRF) measurements and corresponding tissue compartment ra...

Experimental study of the optimum puncture pattern of robot-assisted needle insertion into hyperelastic materials.

The robot-assisted insertion surgery plays a crucial role in biopsy and therapy. This study focuses on determining the optimum puncture pattern for ro...