Pathology

Classification of target tissues of Eisenia fetida using sequential multimodal chemical analysis and machine learning.

Acquiring comprehensive knowledge about the uptake of pollutants, impact on tissue integrity and the effects at the molecular level in organisms is of increasing interest due to the environmental exposure to numerous contaminants. The analysis of tissues can be performed by histological examination, which is still time-consuming and restricted to target-specific staining methods. The histological ...

Recognition study of denatured biological tissues based on multi-scale rescaled range permutation entropy.

The recognition of denatured biological tissue is an indispensable part in the process of high intensity focused ultrasound treatment. As a nonlinear method, multi-scale permutation entropy (MPE) is widely used in the recognition of denatured biological tissue. However, the traditional MPE method neglects the amplitude information when calculating the time series complexity. The disadvantage will ...

Design of lung nodules segmentation and recognition algorithm based on deep learning.

BACKGROUND: Accurate segmentation and recognition algorithm of lung nodules has great important value of reference for early diagnosis of lung cancer....

Machine learning random forest for predicting oncosomatic variant NGS analysis.

Since 2017, we have used IonTorrent NGS platform in our hospital to diagnose and treat cancer. Analyzing variants at each run requires considerable ti...

A deep learning approach to identify and segment alpha-smooth muscle actin stress fiber positive cells.

Cardiac fibrosis is a pathological process characterized by excessive tissue deposition, matrix remodeling, and tissue stiffening, which eventually le...

Integrated Cells and Collagen Fibers Spatial Image Analysis.

Modern technologies designed for tissue structure visualization like brightfield microscopy, fluorescent microscopy, mass cytometry imaging (MCI) and ...

PEGylated ethyl cellulose micelles as a nanocarrier for drug delivery.

Natural polymers provide a better alternative to synthetic polymers in the domain of drug delivery systems (DDSs) because of their renewability, bioco...

Development of a Machine Learning Classifier for Brain Tumors Diagnosis Based on DNA Methylation Profile.

More than 150 types of brain tumors have been documented. Accurate diagnosis is important for making appropriate therapeutic decisions in treating th...

High-Throughput, Label-Free and Slide-Free Histological Imaging by Computational Microscopy and Unsupervised Learning.

Rapid and high-resolution histological imaging with minimal tissue preparation has long been a challenging and yet captivating medical pursuit. Here, ...

The utility of color normalization for AI-based diagnosis of hematoxylin and eosin-stained pathology images.

The color variation of hematoxylin and eosin (H&E)-stained tissues has presented a challenge for applications of artificial intelligence (AI) in digit...

Selective identification and localization of indolent and aggressive prostate cancers via CorrSigNIA: an MRI-pathology correlation and deep learning framework.

Automated methods for detecting prostate cancer and distinguishing indolent from aggressive disease on Magnetic Resonance Imaging (MRI) could assist i...

The regulatory and modulatory roles of TRP family channels in malignant tumors and relevant therapeutic strategies.

Transient receptor potential (TRP) channels are one primary type of calcium (Ca) permeable channels, and those relevant transmembrane and intracellula...

Improving diagnosing performance for malignant parotid gland tumors using machine learning with multifeatures based on diffusion-weighted magnetic resonance imaging.

In this study, the performance of machine learning in classifying parotid gland tumors based on diffusion-related features obtained from the parotid g...

Automated interpretation and analysis of bronchoalveolar lavage fluid.

BACKGROUND: The cytological analysis of bronchoalveolar lavage fluid (BALF) plays an essential role in the differential diagnosis of respiratory disea...

Deep learning is widely applicable to phenotyping embryonic development and disease.

Genome editing simplifies the generation of new animal models for congenital disorders. However, the detailed and unbiased phenotypic assessment of al...

The value of magnetic resonance imaging and ultrasonography (MRI/US)-fusion biopsy in clinically significant prostate cancer detection in patients with biopsy-naïve men according to PSA levels: A propensity score matching analysis.

OBJECTIVES: To evaluate the detection rate of clinically significant prostate cancer (csPCa) in Magnetic resonance imaging and ultrasonography (MRI/US...

Construction and external validation of a 5-gene random forest model to diagnose non-obstructive azoospermia based on the single-cell RNA sequencing of testicular tissue.

Non-obstructive azoospermia (NOA) is among the most severe factors for male infertility, but our understandings of the latent biological mechanisms re...

Accurate classification of plasma cell dyscrasias is achieved by combining artificial intelligence and flow cytometry.

Monoclonal gammopathy of unknown significance (MGUS), smouldering multiple myeloma (SMM), and multiple myeloma (MM) are very common neoplasms. However...

Identification of diagnostic signatures in ulcerative colitis patients via bioinformatic analysis integrated with machine learning.

Ulcerative colitis (UC) is an immune-related disorder with enhanced prevalence globally. Early diagnosis is critical for the effective treatment of UC...

Identification of Benign and Malignant Lung Nodules in CT Images Based on Ensemble Learning Method.

BACKGROUND AND OBJECTIVE: Under the background of urgent need for computer-aided technology to provide physicians with objective decision support, aim...