Pathology

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage.

. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters.. A lit...

Integrated multi-omics analysis and machine learning to refine molecular subtypes, prognosis, and immunotherapy in lung adenocarcinoma.

Lung adenocarcinoma (LUAD) has a malignant characteristic that is highly aggressive and prone to metastasis. There is still a lack of suitable biomarkers to facilitate the refinement of precision-based therapeutic regimens. We used a combination of 10 known clustering algorithms and the omics data from 4 dimensions to identify high-resolution molecular subtypes of LUAD. Subsequently, consensus mac...

Clinical evaluation of an artificial intelligence-assisted cytological system among screening strategies for a cervical cancer high-risk population.

BACKGROUND: Primary cervical cancer screening and treating precancerous lesions are effective ways to prevent cervical cancer. However, the coverage r...

Identification of chronic non-atrophic gastritis and intestinal metaplasia stages in the Correa's cascade through machine learning analyses of SERS spectral signature of non-invasively-collected human gastric fluid samples.

The progression of gastric cancer involves a complex multi-stage process, with gastroscopy and biopsy being the standard procedures for diagnosing gas...

Dual-stream multi-dependency graph neural network enables precise cancer survival analysis.

Histopathology image-based survival prediction aims to provide a precise assessment of cancer prognosis and can inform personalized treatment decision...

AI-based histopathology image analysis reveals a distinct subset of endometrial cancers.

Endometrial cancer (EC) has four molecular subtypes with strong prognostic value and therapeutic implications. The most common subtype (NSMP; No Speci...

Fluorescence excitation-scanning hyperspectral imaging with scalable 2D-3D deep learning framework for colorectal cancer detection.

Colorectal cancer is one of the top contributors to cancer-related deaths in the United States, with over 100,000 estimated cases in 2020 and over 50,...

Artificial intelligence and skin melanoma.

Melanoma is the deadliest skin cancer, presenting typically with changing pigmented areas and usually treated with surgical removal. As benign cutaneo...

Molecular Classification of Breast Cancer Using Weakly Supervised Learning.

PURPOSE: The molecular classification of breast cancer is crucial for effective treatment. The emergence of digital pathology has ushered in a new era...

Background removal for debiasing computer-aided cytological diagnosis.

To address the background-bias problem in computer-aided cytology caused by microscopic slide deterioration, this article proposes a deep learning app...

Skin-CAD: Explainable deep learning classification of skin cancer from dermoscopic images by feature selection of dual high-level CNNs features and transfer learning.

Skin cancer (SC) significantly impacts many individuals' health all over the globe. Hence, it is imperative to promptly identify and diagnose such con...

Machine Learning-Enhanced Extraction of Biomarkers for High-Grade Serous Ovarian Cancer from Proteomics Data.

Comprehensive biomedical proteomic datasets are accumulating exponentially, warranting robust analytics to deconvolute them for identifying novel biol...

Prediction of Robotic Anastomosis Competency Evaluation (RACE) metrics during vesico-urethral anastomosis using electroencephalography, eye-tracking, and machine learning.

Residents learn the vesico-urethral anastomosis (VUA), a key step in robot-assisted radical prostatectomy (RARP), early in their training. VUA assessm...

Identifying radiogenomic associations of breast cancer based on DCE-MRI by using Siamese Neural Network with manufacturer bias normalization.

BACKGROUND AND PURPOSE: The immunohistochemical test (IHC) for Human Epidermal Growth Factor Receptor 2 (HER2) and hormone receptors (HR) provides pro...

Diagnosis and classification of kidney transplant rejection using machine learning-assisted surface-enhanced Raman spectroscopy using a single drop of serum.

The quest to reduce kidney transplant rejection has emphasized the urgent requirement for the development of non-invasive, precise diagnostic technolo...

scHolography: a computational method for single-cell spatial neighborhood reconstruction and analysis.

Spatial transcriptomics has transformed our ability to study tissue complexity. However, it remains challenging to accurately dissect tissue organizat...

Integrated analysis of single-cell sequencing and machine learning identifies a signature based on monocyte/macrophage hub genes to analyze the intracranial aneurysm associated immune microenvironment.

Monocytes are pivotal immune cells in eliciting specific immune responses and can exert a significant impact on the progression, prognosis, and immuno...

Artificial intelligence in hepatocellular carcinoma diagnosis: a comprehensive review of current literature.

BACKGROUND AND AIM: Hepatocellular carcinoma (HCC) diagnosis mainly relies on its pathognomonic radiological profile, obviating the need for biopsy. T...

Artificial Intelligence in Head and Neck Surgery.

This article explores artificial intelligence's (AI's) role in otolaryngology for head and neck cancer diagnosis and management. It highlights AI's po...

Machine learning assisted microfluidics dual fluorescence flow cytometry for detecting bladder tumor cells based on morphological characteristic parameters.

BACKGROUND: Bladder cancer (BC) is the most common malignant tumor and has become a major public health problem, leading the causes of death worldwide...