Pathology

Artificial intelligence can dynamically adjust strategies for auxiliary diagnosing respiratory diseases and analyzing potential pathological relationships.

Respiratory diseases are one of the leading causes of human death and exacerbate the global burden of non-communicable diseases. Finding a method to assist clinicians pre-diagnose these diseases is an urgent task. Existing artificial intelligence-based methods can improve the clinical diagnosis efficiency, but still face challenges. For example, the lack of interpretability, the problem of informa...

Deep Learning-Based Evaluation of Ultrasound Images for Benign Skin Tumors.

In this study, a combined convolutional neural network for the diagnosis of three benign skin tumors was designed, and its effectiveness was verified through quantitative and statistical analysis. To this end, 698 sonographic images were taken and diagnosed at the Department of Dermatology at Severance Hospital in Seoul, Korea, between 10 November 2017 and 17 January 2020. Through an empirical pro...

Deep learning analysis of mid-infrared microscopic imaging data for the diagnosis and classification of human lymphomas.

The present study presents an alternative analytical workflow that combines mid-infrared (MIR) microscopic imaging and deep learning to diagnose human...

Neuromorphic applications in medicine.

In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in...

Early detection of lung cancer using artificial intelligence-enhanced optical nanosensing of chromatin alterations in field carcinogenesis.

Supranucleosomal chromatin structure, including chromatin domain conformation, is involved in the regulation of gene expression and its dysregulation ...

Breast Cancer Histopathological Images Segmentation Using Deep Learning.

Hospitals generate a significant amount of medical data every day, which constitute a very rich database for research. Today, this database is still n...

Breast cancer histopathology image-based gene expression prediction using spatial transcriptomics data and deep learning.

Tumour heterogeneity in breast cancer poses challenges in predicting outcome and response to therapy. Spatial transcriptomics technologies may address...

Deep learning approach for accurate prostate cancer identification and stratification using combined immunostaining of cytokeratin, p63, and racemase.

BACKGROUND: Prostate cancer (PCa) is the most frequently diagnosed cancer in men worldwide, affecting around 1.4 million individuals. Current PCa diag...

Screening adequacy of unstained thyroid fine needle aspiration samples using a deep learning-based classifier.

Fine needle aspiration (FNA) biopsy of thyroid nodules is a safe, cost-effective, and accurate diagnostic method for detecting thyroid cancer. However...

Construction of artificial intelligence non-invasive diagnosis model for common glomerular diseases based on hyperspectral and urine analysis.

OBJECTIVE: To develop a non-invasive fluid biopsy assisted diagnosis model for glomerular diseases based on hyperspectral, so as to solve the problem ...

iCLOTS: open-source, artificial intelligence-enabled software for analyses of blood cells in microfluidic and microscopy-based assays.

While microscopy-based cellular assays, including microfluidics, have significantly advanced over the last several decades, there has not been concurr...

Automatically Predicting Material Properties with Microscopic Images: Polymer Miscibility as an Example.

Many material properties are manifested in the morphological appearance and characterized using microscopic images, such as scanning electron microsco...

An artificial intelligence-based ecological index for prognostic evaluation of colorectal cancer.

BACKGROUND AND OBJECTIVE: In the tumor microenvironment (TME), the dynamic interaction between tumor cells and immune cells plays a critical role in p...

Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm.

Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of th...

Automated Deep Learning-Based Segmentation of Abdominal Adipose Tissue on Dixon MRI in Adolescents: A Prospective Population-Based Study.

The prevalence of childhood obesity has increased significantly worldwide, highlighting a need for accurate noninvasive quantification of body fat di...

Particle fusion of super-resolution data reveals the unit structure of Nup96 in Nuclear Pore Complex.

Single molecule localization microscopy offers resolution nearly down to the molecular level with specific molecular labelling, and is thereby a promi...

Mammography using low-frequency electromagnetic fields with deep learning.

In this paper, a novel technique for detecting female breast anomalous tissues is presented and validated through numerical simulations. The technique...

Can deep learning replace histopathological examinations in the differential diagnosis of cervical lymphadenopathy?

INTRODUCTION: We aimed to develop a diagnostic deep learning model using contrast-enhanced CT images and to investigate whether cervical lymphadenopat...

Improving the Efficacy of ACR TI-RADS Through Deep Learning-Based Descriptor Augmentation.

Thyroid nodules occur in up to 68% of people, 95% of which are benign. Of the 5% of malignant nodules, many would not result in symptoms or death, yet...

Deep learning integrates histopathology and proteogenomics at a pan-cancer level.

We introduce a pioneering approach that integrates pathology imaging with transcriptomics and proteomics to identify predictive histology features ass...