Pathology

Linear regressive weighted Gaussian kernel liquid neural network for brain tumor disease prediction using time series data.

A brain tumor is an abnormal growth of cells within the brain or surrounding tissues, which can be either benign or malignant. Brain tumors develop in various regions of the brain, each affecting different functions such as movement, speech, and vision, depending on their location. Early prediction of brain tumors is crucial for improving survival rates and treatment outcomes. Advanced techniques,...

Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.

BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point F-FDG PET/CT to predict the malignant risk of GGOs.

Prognosis of p16 and Human Papillomavirus Discordant Oropharyngeal Cancers and the Exploration of Using Natural Language Processing to Analyze Free-Text Pathology Reports.

PURPOSE: Treatment deintensification for human papillomavirus-positive (HPV+)-associated oropharyngeal cancer (OPC) has been the catalyst of experts w...

Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma.

Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patien...

Using Machine Learning and Optical Microscopy Image Analysis of Immunosensors Made on Plasmonic Substrates: Application to Detect the SARS-CoV-2 Virus.

In this article, we introduce a diagnostic platform comprising an optical microscopy image analysis system coupled with machine learning. Its efficacy...

Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning.

To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore t...

Prior knowledge-based multi-task learning network for pulmonary nodule classification.

The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. I...

Diagnosis of Thyroid Nodule Malignancy Using Peritumoral Region and Artificial Intelligence: Results of Hand-Crafted, Deep Radiomics Features and Radiologists' Assessment in Multicenter Cohorts.

OBJECTIVE: To develop, test, and externally validate a hybrid artificial intelligence (AI) model based on hand-crafted and deep radiomics features ext...

Artificial intelligence for automatic diagnosis and pleomorphic morphological characterization of malignant biliary strictures using digital cholangioscopy.

Diagnosing and characterizing biliary strictures (BS) remains challenging. Artificial intelligence (AI) applied to digital single-operator cholangiosc...

Fast fault diagnosis of smart grid equipment based on deep neural network model based on knowledge graph.

The smart grid is on the basis of physical grid, introducing all kinds of advanced communications technology and form a new type of power grid. It can...

Deep transfer learning based hierarchical CAD system designs for SFM images.

Present work involves rigorous experimentation for classification of mammographic masses by employing four deep transfer learning models using hierarc...

A Perspective on Artificial Intelligence for Molecular Pathologists.

The widespread adoption of next-generation sequencing technology in molecular pathology has enabled us to interrogate the genome as never before. The ...

3D velocity and pressure field reconstruction in the cardiac left ventricle via physics informed neural network from echocardiography guided by 3D color Doppler.

Fluid dynamics of the heart chamber can provide critical biological cues for understanding cardiac health and disease and have the potential for suppo...

Diagnosis and treatment of a rare bilateral primary thyroid cancer: a case report.

Preoperative ultrasound examination of thyroid nodules is the most economical and effective screening method for diagnosing thyroid nodules. Fine-need...

Monitoring Immunohistochemical Staining Variations Using Artificial Intelligence on Standardized Controls.

Quality control of immunohistochemistry (IHC) slides is crucial to ascertain accurate patient management. Visual assessment is the most commonly used ...

FakET: Simulating cryo-electron tomograms with neural style transfer.

In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, requ...

Artificial intelligence in gastrointestinal cancer research: Image learning advances and applications.

With the rapid advancement of artificial intelligence (AI) technologies, including deep learning, large language models, and neural networks, these me...

Bioinspired design of a tissue-engineered ray with machine learning.

In biomimetic design, researchers recreate existing biological structures to form functional devices. For biohybrid robotic swimmers assembled with ti...

A machine learning-based investigation of integrin expression patterns in cancer and metastasis.

Integrins, a family of transmembrane receptor proteins, are well known to play important roles in cancer development and metastasis. However, a compre...

A metabolic fingerprint of ovarian cancer: a novel diagnostic strategy employing plasma EV-based metabolomics and machine learning algorithms.

Ovarian cancer (OC) is the third most common malignant tumor of women and is accompanied by an alteration of systemic metabolism. A liquid biopsy that...