Pathology

Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects.

Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite ...

Deep learning-based radiomics model can predict extranodal soft tissue metastasis in gastric cancer.

BACKGROUND: The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard of ESTM is determined by pathologic examination after surgery, and there are no preoperative methods for assessment of ESTM yet.

A Deep Learning Approach to Using Wearable Seismocardiography (SCG) for Diagnosing Aortic Valve Stenosis and Predicting Aortic Hemodynamics Obtained by 4D Flow MRI.

In this paper, we explored the use of deep learning for the prediction of aortic flow metrics obtained using 4-dimensional (4D) flow magnetic resonanc...

Deep Learning for Predicting Effect of Neoadjuvant Therapies in Non-Small Cell Lung Carcinomas With Histologic Images.

Neoadjuvant therapies are used for locally advanced non-small cell lung carcinomas, whereby pathologists histologically evaluate the effect using rese...

FGFR1Pred: an artificial intelligence-based model for predicting fibroblast growth factor receptor 1 inhibitor.

Fibroblast growth factor receptors (FGFRs) are a family of cell surface receptors that bind to fibroblast growth factor (FGF) and mediate various cell...

The auxiliary diagnosis of thyroid echogenic foci based on a deep learning segmentation model: A two-center study.

OBJECTIVE: The aim of this study is to develop AI-assisted software incorporating a deep learning (DL) model based on static ultrasound images. The so...

Identification of lymph node metastasis in pre-operation cervical cancer patients by weakly supervised deep learning from histopathological whole-slide biopsy images.

BACKGROUND: Lymph node metastasis (LNM) significantly impacts the prognosis of individuals diagnosed with cervical cancer, as it is closely linked to ...

DLATA: Deep Learning-Assisted transformation alignment of 2D brain slice histology.

Accurate alignment of brain slices is crucial for the classification of neuron populations by brain region, and for quantitative analysis in in vitro ...

Vesicourethral Anastomosis in Transvesical Single-Port Robotic Radical Prostatectomy: A Technical Description and Perioperative Outcomes.

To describe the technical evolution and perioperative outcomes of vesicourethral anastomosis (VUA) in transvesical (TV) single-port robot-assisted ra...

Deep neural network pulmonary nodule segmentation methods for CT images: Literature review and experimental comparisons.

Automatic and accurate segmentation of pulmonary nodules in CT images can help physicians perform more accurate quantitative analysis, diagnose diseas...

Artificial intelligence directed development of a digital twin to measure soft tissue shift during head and neck surgery.

Digital twins derived from 3D scanning data were developed to measure soft tissue deformation in head and neck surgery by an artificial intelligence a...

Artificial Intelligence Optical Biopsy for Evaluating the Functional State of Wounds.

INTRODUCTION: The clinical characterization of the functional status of active wounds in terms of their driving cellular and molecular biology remains...

Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms.

Fluorescence imaging flow cytometry (IFC) has been demonstrated as a crucial biomedical technique for analyzing specific cell subpopulations from hete...

Identification of a novel bile marker clusterin and a public online prediction platform based on deep learning for cholangiocarcinoma.

BACKGROUND: Cholangiocarcinoma (CCA) is a highly aggressive malignant tumor, and its diagnosis is still a challenge. This study aimed to identify a no...

Application of digital pathology-based advanced analytics of tumour microenvironment organisation to predict prognosis and therapeutic response.

In recent years, the application of advanced analytics, especially artificial intelligence (AI), to digital H&E images, and other histological image t...

High-Order Correlation-Guided Slide-Level Histology Retrieval With Self-Supervised Hashing.

Histopathological Whole Slide Images (WSIs) play a crucial role in cancer diagnosis. It is of significant importance for pathologists to search for im...

Deep learning for the prediction of the chemotherapy response of metastatic colorectal cancer: comparing and combining H&E staining histopathology and infrared spectral histopathology.

Colorectal cancer is a global public health problem with one of the highest death rates. It is the second most deadly type of cancer and the third mos...

Development and accuracy of artificial intelligence-generated prediction of facial changes in orthodontic treatment: a scoping review.

Artificial intelligence (AI) has been utilized in soft-tissue analysis and prediction in orthodontic treatment planning, although its reliability has ...

Metadata and Image Features Co-Aware Personalized Federated Learning for Smart Healthcare.

Recently, artificial intelligence has been widely used in intelligent disease diagnosis and has achieved great success. However, most of the works mai...

SliDL: A toolbox for processing whole-slide images in deep learning.

The inspection of stained tissue slides by pathologists is essential for the early detection, diagnosis and monitoring of disease. Recently, deep lear...