Pathology

A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides.

Current artificial intelligence (AI) trends are revolutionizing medical image processing, greatly improving cervical cancer diagnosis. Machine learning (ML) algorithms can discover patterns and anomalies in medical images, whereas deep learning (DL) methods, specifically convolutional neural networks (CNNs), are extremely accurate at identifying malignant lesions. Deep models that have been pre-tr...

Deep learning enabled liquid-based cytology model for cervical precancer and cancer detection.

Deep learning (DL) enabled liquid-based cytology has potential for cervical cancer screening or triage. Here, we develop a DL model using whole cytology slides from 17,397 women and test it on 10,826 additional cases through a three-stage process. The DL model achieves robust performance across nine hospitals. In a multi-reader, multi-case study, it outperforms cytopathologists' sensitivity by 9%....

Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients.

The precise and efficient diagnosis of an individual's skeletal class is necessary in orthodontics to ensure correct and stable treatment planning. Ho...

Integrating Machine Learning and Bulk and Single-Cell RNA Sequencing to Decipher Diverse Cell Death Patterns for Predicting the Prognosis of Neoadjuvant Chemotherapy in Breast Cancer.

Breast cancer (BRCA) continues to pose a serious risk to women's health worldwide. Neoadjuvant chemotherapy (NAC) is a critical treatment strategy. Ne...

Stable distance regression via spatial-frequency state space model for robot-assisted endomicroscopy.

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) is a noninvasive technique that enables the direct visualization of tissue at a microscopic ...

Radiomics for lung cancer diagnosis, management, and future prospects.

Lung cancer remains the leading cause of cancer-related mortality worldwide, with its early detection and effective treatment posing significant clini...

Future prospects of deep learning in esophageal cancer diagnosis and clinical decision support (Review).

Esophageal cancer (EC) is one of the leading causes of cancer-related mortality worldwide, still faces significant challenges in early diagnosis and p...

Multifaceted profiling of virus-specific CD8 T cells reveals distinct immune signatures against cytomegalovirus infection states during pregnancy.

Anti-cytomegalovirus (CMV) serological testing, including the IgG avidity index (AI), is used to assess CMV infection phases during pregnancy. However...

Deep Learning Methods in the Imaging of Hepatic and Pancreaticobiliary Diseases.

Reports indicate a growing role for artificial intelligence (AI) in the evaluation of pancreaticobiliary and hepatic conditions. A key focus is differ...

Leveraging deep learning for improving parameter extraction from perfusion MR images: A narrative review.

BACKGROUND: Perfusion magnetic resonance imaging (MRI) is a non-invasive technique essential for assessing tissue microcirculation and perfusion dynam...

Characterization of fibrotic liver tissue microstructure for predicting shear wave speed variability: a machine-learning-based computational study.

This study aimed to establish a link between the microstructure of simulated fibrotic liver tissues and the measured shear wave speed (SWS) variabilit...

Identification of M1 macrophage infiltration-related genes for immunotherapy in Her2-positive breast cancer based on bioinformatics analysis and machine learning.

Over the past several decades, there has been a significant increase in the number of breast cancer patients. Among the four subtypes of breast cancer...

AI-Driven Information for Relatives of Patients with Malignant Middle Cerebral Artery Infarction: A Preliminary Validation Study Using GPT-4o.

This study examines GPT-4o's ability to communicate effectively with relatives of patients undergoing decompressive hemicraniectomy (DHC) after malig...

Micro- and Nano-Bots for Infection Control.

Medical micro- and nano-bots (MMBs and MNBs) have attracted a lot of attention owing to their precise motion for accessing difficult-to-reach areas in...

DIPathMamba: A domain-incremental weakly supervised state space model for pathology image segmentation.

Accurate segmentation of pathology images plays a crucial role in digital pathology workflow. However, two significant issues exist with the present p...

Hybrid convolutional neural network and bi-LSTM model with EfficientNet-B0 for high-accuracy breast cancer detection and classification.

Breast cancer detection remains one of the most challenging problems in medical imaging. We propose a novel hybrid model that integrates Convolutional...

Application of artificial intelligence in the diagnosis of malignant digestive tract tumors: focusing on opportunities and challenges in endoscopy and pathology.

BACKGROUND: Malignant digestive tract tumors are highly prevalent and fatal tumor types globally, often diagnosed at advanced stages due to atypical e...

Fast, accurate, and versatile data analysis platform for the quantification of molecular spatiotemporal signals.

Optical recording of intricate molecular dynamics is becoming an indispensable technique for biological studies, accelerated by the development of new...

Integrated bioinformatics analysis to develop diagnostic models for malignant transformation of chronic proliferative diseases.

The combined analysis of dual diseases can provide new insights into pathogenic mechanisms, identify novel biomarkers, and develop targeted therapeuti...

Validation of body composition parameters extracted via deep learning-based segmentation from routine computed tomographies.

Sarcopenia and body composition metrics are strongly associated with patient outcomes. In this study, we developed and validated a flexible, open-acce...