Pathology

Evaluation of an Artificial Intelligence-Based Tool and a Universal Low-Cost Robotized Microscope for the Automated Diagnosis of Malaria.

The gold standard diagnosis for malaria is the microscopic visualization of blood smears to identify parasites, although it is an expert-dependent technique and could trigger diagnostic errors. Artificial intelligence (AI) tools based on digital image analysis were postulated as a suitable supportive alternative for automated malaria diagnosis. A diagnostic evaluation of the AI-based system was ...

LC-MS profiling and cytotoxic activity of Angiopteris helferiana against HepG2 cell line: Molecular insight to investigate anticancer agent.

Liver cancer is one of the most prevalent malignant diseases in humans and the second leading cause of cancer-related mortality globally. Angiopteris helferiana was mentioned as a possible anticancer herb according to ethnomedicinal applications. However, the molecular docking and chemical profiling of the bioactive phytoconstituents accountable for the reported anticancer action still require res...

Quantitative immunohistochemistry analysis of breast Ki67 based on artificial intelligence.

Breast cancer is a common malignant tumor of women. Ki67 is an important biomarker of cell proliferation. With the quantitative analysis, it is an imp...

Digital pathology: Revolutionizing oral and maxillofacial diagnostics.

Digital pathology (DP) has revolutionized oral and maxillofacial pathology (OMP) by enhancing diagnostic accuracy and efficiency, leading to improved ...

A novel artificial intelligence segmentation model for early diagnosis of bladder tumors.

OBJECTIVE: Despite cystoscopy plays an important role in bladder tumors diagnosis, it often falls short in flat cancerous tissue and minuscule satelli...

Cooperative multi-task learning and interpretable image biomarkers for glioma grading and molecular subtyping.

Deep learning methods have been widely used for various glioma predictions. However, they are usually task-specific, segmentation-dependent and lack o...

Neuromorphic-enabled video-activated cell sorting.

Imaging flow cytometry allows image-activated cell sorting (IACS) with enhanced feature dimensions in cellular morphology, structure, and composition....

Unsupervised inter-domain transformation for virtually stained high-resolution mid-infrared photoacoustic microscopy using explainable deep learning.

Mid-infrared photoacoustic microscopy can capture biochemical information without staining. However, the long mid-infrared optical wavelengths make th...

Analysis of diagnostic genes and molecular mechanisms of Crohn's disease and colon cancer based on machine learning algorithms.

Crohn's disease (CD) is a chronic inflammatory bowel condition, and colon adenocarcinoma (COAD), as one of the most prevalent malignant tumors of the ...

Artificial intelligence in pancreaticobiliary endoscopy: Current applications and future directions.

Pancreaticobiliary endoscopy is an essential tool for diagnosing and treating pancreaticobiliary diseases. However, it does not fully meet clinical ne...

Interpretable machine learning-driven biomarker identification and validation for Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by limited effective treatments, underscoring the critical need for early detec...

Clinically validated classification of chronic wounds method with memristor-based cellular neural network.

Chronic wounds are a syndrome that affects around 4% of the world population due to several pathologies. The COV-19 pandemic has enforced the need of ...

A machine learning-based immune response signature to facilitate prognosis prediction in patients with endometrial cancer.

Endometrial cancer is the most prevalent form of gynecologic malignancy, with a significant surge in incidence among youngsters. Although the advent o...

Extreme learning machine for identifying soil-dwelling microorganisms cultivated on agar media.

The aim of this research is to create an automated system for identifying soil microorganisms at the genera level based on raw microscopic images of m...

BMT: A Cross-Validated ThinPrep Pap Cervical Cytology Dataset for Machine Learning Model Training and Validation.

In the past several years, a few cervical Pap smear datasets have been published for use in clinical training. However, most publicly available datase...

Deep learning radiomics on grayscale ultrasound images assists in diagnosing benign and malignant of BI-RADS 4 lesions.

This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing betwee...

Multi-stain deep learning prediction model of treatment response in lupus nephritis based on renal histopathology.

The response of the kidney after induction treatment is one of the determinants of prognosis in lupus nephritis, but effective predictive tools are la...

Recent Advances and Future Directions in Sonodynamic Therapy for Cancer Treatment.

Deep-tissue solid cancer treatment has a poor prognosis, resulting in a very low 5-year patient survival rate. The primary challenges facing solid tum...

Prognostic Impact of Tumor Cell Nuclear Size Assessed by Artificial Intelligence in Esophageal Squamous Cell Carcinoma.

Tumor cell nuclear size (NS) indicates malignant potential in breast cancer; however, its clinical significance in esophageal squamous cell carcinoma ...

Predicting benefit from PARP inhibitors using deep learning on H&E-stained ovarian cancer slides.

PURPOSE: Ovarian cancer patients with a Homologous Recombination Deficiency (HRD) often benefit from polyadenosine diphosphate-ribose polymerase (PARP...