Pathology

An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images.

BACKGROUND: Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologists to determine the OBMC with limited clinical information and bone biopsy.

Early Diagnosis of Brain Diseases Using Artificial Intelligence and EV Molecular Data: A Proposed Noninvasive Repeated Diagnosis Approach.

Brain-derived extracellular vesicles (BDEVs) are released from the central nervous system. Brain-related research and diagnostic techniques involving BDEVs have rapidly emerged as a means of diagnosing brain disorders because they are minimally invasive and enable repeatable measurements based on body fluids. However, EVs from various cells and organs are mixed in the blood, acting as potential ob...

Intraoperative cytological diagnosis of brain tumours: A preliminary study using a deep learning model.

BACKGROUND: Intraoperative pathological diagnosis of central nervous system (CNS) tumours is essential to planning patient management in neuro-oncolog...

Automated quantification and statistical assessment of proliferating cardiomyocyte rates in embryonic hearts.

The use of digital image analysis and count regression models contributes to the reproducibility and rigor of histological studies in cardiovascular r...

A strategy to formulate data-driven constitutive models from random multiaxial experiments.

We present a test technique and an accompanying computational framework to obtain data-driven, surrogate constitutive models that capture the response...

A deep-learning model for transforming the style of tissue images from cryosectioned to formalin-fixed and paraffin-embedded.

Histological artefacts in cryosectioned tissue can hinder rapid diagnostic assessments during surgery. Formalin-fixed and paraffin-embedded (FFPE) tis...

Symptoms are known by their companies: towards association guided disease diagnosis assistant.

Over the last few years, dozens of healthcare surveys have shown a shortage of doctors and an alarming doctor-population ratio. With the motivation of...

Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction.

Osteosarcoma is the most common primary bone cancer, whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy ...

Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.

Colorectal cancer (CRC) is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma ar...

Deep learning-based system for automatic prediction of triple-negative breast cancer from ultrasound images.

To develop a deep-learning system for the automatic identification of triple-negative breast cancer (TNBC) solely from ultrasound images. A total of 1...

Machine learning-based models for predicting gas breakthrough pressure of porous media with low/ultra-low permeability.

Gas breakthrough pressure is a significant parameter for the gas exploration and safety evaluation of engineering barrier systems in the carbon dioxid...

Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.

The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strat...

Looking with new eyes: advanced microscopy and artificial intelligence in reproductive medicine.

Microscopy has long played a pivotal role in the field of assisted reproductive technology (ART). The advent of artificial intelligence (AI) has opene...

Medical Image Classification Based on Semi-Supervised Generative Adversarial Network and Pseudo-Labelling.

Deep learning has substantially improved the state-of-the-art in object detection and image classification. Deep learning usually requires large-scale...

Application of deep learning as an ancillary diagnostic tool for thyroid FNA cytology.

BACKGROUND: Several studies have used artificial intelligence (AI) to analyze cytology images, but AI has yet to be adopted in clinical practice. The ...

An open-access breast lesion ultrasound image database‏: Applicable in artificial intelligence studies.

Breast cancer is one of the largest single contributors to the burden of disease worldwide. Early detection of breast cancer has been shown to be asso...

Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning.

Chronic liver diseases affect more than 1 billion people worldwide and represent one of the main public health issues. Nonalcoholic fatty liver diseas...

Attention-based deep learning for breast lesions classification on contrast enhanced spectral mammography: a multicentre study.

BACKGROUND: This study aims to develop an attention-based deep learning model for distinguishing benign from malignant breast lesions on CESM.

Artificial Intelligence Applications in Clinical Chemistry.

Artificial intelligence (AI) applications are an area of active investigation in clinical chemistry. Numerous publications have demonstrated the promi...

Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction.

Unreliable predictions can occur when an artificial intelligence (AI) system is presented with data it has not been exposed to during training. We dem...