Pathology

Flexible Fitting of Small Molecules into Electron Microscopy Maps Using Molecular Dynamics Simulations with Neural Network Potentials.

Despite significant advances in resolution, the potential for cryo-electron microscopy (EM) to be used in determining the structures of protein-drug complexes remains unrealized. Determination of accurate structures and coordination of bound ligands necessitates simultaneous fitting of the models into the density envelopes, exhaustive sampling of the ligand geometries, and, most importantly, conco...

Diagnosis of Thyroid Nodule with New Ultrasound Imaging Modalities.

B-mode ultrasound (US) technology is an integral part of diagnosing and assessing risk stratification of thyroid nodules (TNs). The addition of shear wave elastography and three-dimensional (3D) US imaging may improve risk stratification for thyroid cancer (TC). The patient was evaluated in our clinic with US imaging including B-mode, shear wave elastography, 3D-US, and fine needle aspiration bi...

Automated detection algorithm for C4d immunostaining showed comparable diagnostic performance to pathologists in renal allograft biopsy.

A deep learning-based image analysis could improve diagnostic accuracy and efficiency in pathology work. Recently, we proposed a deep learning-based d...

Ultrasound Image-Based Diagnosis of Malignant Thyroid Nodule Using Artificial Intelligence.

Computer-aided diagnosis systems have been developed to assist doctors in diagnosing thyroid nodules to reduce errors made by traditional diagnosis me...

Machine learning for cluster analysis of localization microscopy data.

Quantifying the extent to which points are clustered in single-molecule localization microscopy data is vital to understanding the spatial relationshi...

A convolutional neural network-based system to classify patients using FDG PET/CT examinations.

BACKGROUND: As the number of PET/CT scanners increases and FDG PET/CT becomes a common imaging modality for oncology, the demands for automated detect...

High-throughput quantitative histology in systemic sclerosis skin disease using computer vision.

BACKGROUND: Skin fibrosis is the clinical hallmark of systemic sclerosis (SSc), where collagen deposition and remodeling of the dermis occur over time...

Impact of machine-learning CT-derived fractional flow reserve for the diagnosis and management of coronary artery disease in the randomized CRESCENT trials.

OBJECTIVE: To determine the potential impact of on-site CT-derived fractional flow reserve (CT-FFR) on the diagnostic efficiency and effectiveness of ...

A knowledge-driven feature learning and integration method for breast cancer diagnosis on multi-sequence MRI.

BACKGROUND: The classification of benign versus malignant breast lesions on multi-sequence Magnetic Resonance Imaging (MRI) is a challenging task sinc...

Deep learning algorithm for surveillance of pneumothorax after lung biopsy: a multicenter diagnostic cohort study.

OBJECTIVES: Pneumothorax is the most common and potentially life-threatening complication arising from percutaneous lung biopsy. We evaluated the perf...

AIBx, Artificial Intelligence Model to Risk Stratify Thyroid Nodules.

Current classification systems for thyroid nodules are very subjective. Artificial intelligence (AI) algorithms have been used to decrease subjectivi...

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry.

Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computati...

Semi-Supervised Nests of Melanocytes Segmentation Method Using Convolutional Autoencoders.

In this research, we present a semi-supervised segmentation solution using convolutional autoencoders to solve the problem of segmentation tasks havin...

Portable deep learning singlet microscope.

Having the least lenses, the significant feature of the singlet imaging system, helps the development of the portable and cost-effective microscopes. ...

Retrospective Cohort Analysis from a High-Volume Center of Prognostic Factors Affecting Biochemical Relapse in Patients with Encapsulated, Margin-Negative, Isolated Seminal Vesicle Invasion After Robot-Assisted Laparoscopic Prostatectomy: A Novel Study.

Specimen pathology findings collectively impact the long-term outcomes of robot-assisted laparoscopic prostatectomy. Since seminal vesicle invasion (...

Improving breast mass classification by shared data with domain transformation using a generative adversarial network.

Training of a convolutional neural network (CNN) generally requires a large dataset. However, it is not easy to collect a large medical image dataset....

Comprehensive Molecular and Pathologic Evaluation of Transitional Mesothelioma Assisted by Deep Learning Approach: A Multi-Institutional Study of the International Mesothelioma Panel from the MESOPATH Reference Center.

INTRODUCTION: Histologic subtypes of malignant pleural mesothelioma are a major prognostic indicator and decision denominator for all therapeutic stra...

Can a machine-learning model improve the prediction of nodal stage after a positive sentinel lymph node biopsy in breast cancer?

The current standard for evaluating axillary nodal burden in clinically node negative breast cancer is sentinel lymph node biopsy (SLNB). However, th...

Unsupervised Machine Learning in Pathology: The Next Frontier.

Applications of artificial intelligence and particularly deep learning to aid pathologists in carrying out laborious and qualitative tasks in histopat...

Automated prediction of mastitis infection patterns in dairy herds using machine learning.

Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in d...