Pathology

A machine learning-based analysis for the definition of an optimal renal biopsy for kidney cancer.

OBJECTIVE: Renal Tumor biopsy (RTB) can assist clinicians in determining the most suitable approach for treatment of renal cancer. However, RTB's limitations in accurately determining histology and grading have hindered its broader adoption and data on the concordance rate between RTB results and final pathology after surgery are unavailable. Therefore, we aimed to develop a machine learning algor...

Artificial intelligence algorithms enhance urine cytology reporting confidence in postoperative follow-up for upper urinary tract urothelial carcinoma.

PURPOSE: In Taiwan, the incidence of urothelial carcinoma of the upper urinary tract (UTUC) is high and intravesical recurrence is approximately 22%-47%. Thus, postoperative cystoscopy and urine cytology follow-up, which require experienced cytologists, are needed. The AIxURO system aligns with The Paris System (TPS) guidelines for reporting urinary cytology. This study assessed the benefit of ear...

Enhancing mosquito classification through self-supervised learning.

Traditional mosquito identification methods, relied on microscopic observation and morphological characteristics, often require significant expertise ...

Weakly Supervised Multiple Instance Learning Model With Generalization Ability for Clinical Adenocarcinoma Screening on Serous Cavity Effusion Pathology.

Accurate and rapid screening of adenocarcinoma cells in serous cavity effusion is vital in diagnosing the stage of metastatic tumors and providing pro...

G-Protein Signaling in Alzheimer's Disease: Spatial Expression Validation of Semi-supervised Deep Learning-Based Computational Framework.

Systemic study of pathogenic pathways and interrelationships underlying genes associated with Alzheimer's disease (AD) facilitates the identification ...

Comprehensive Analysis of Immune Infiltration and Key Genes in Peri-Implantitis Using Bioinformatics and Molecular Biology Approaches.

BACKGROUND Peri-implantitis is the main cause of failure of implant treatment, and there is little research on its molecular mechanism. This study aim...

Framework for Deep Learning Based Multi-Modality Image Registration of Snapshot and Pathology Images.

Multi-modality image registration is an important task in medical imaging because it allows for information from different domains to be correlated. H...

Optimizing thyroid AUS nodules malignancy prediction: a comprehensive study of logistic regression and machine learning models.

BACKGROUND: The accurate diagnosis of thyroid nodules with indeterminate cytology, particularly in the atypia of undetermined significance (AUS) categ...

Evaluating Advanced Machine Learning Models for Histopathological Diagnosis of Hansen Disease.

INTRODUCTION: Leprosy is a neglected infectious disease caused by Mycobacterium leprae and Mycobacterium lepromatosis and remains a public health chal...

Computer vision applications for the detection or analysis of tuberculosis using digitised human lung tissue images - a systematic review.

OBJECTIVE: To conduct a systematic review of the computer vision applications that detect, diagnose, or analyse tuberculosis (TB) pathology or bacilli...

Scalable deep learning artificial intelligence histopathology slide analysis and validation.

Deep learning involves an artificial intelligence (AI) approach and has been shown to provide superior performance for automating image recognition ta...

Development of an automated artificial intelligence-based system for urogenital schistosomiasis diagnosis using digital image analysis techniques and a robotized microscope.

BACKGROUND: Urogenital schistosomiasis is considered a Neglected Tropical Disease (NTD) by the World Health Organization (WHO). It is estimated to aff...

Clinical validation of an AI-based pathology tool for scoring of metabolic dysfunction-associated steatohepatitis.

Metabolic dysfunction-associated steatohepatitis (MASH) is a major cause of liver-related morbidity and mortality, yet treatment options are limited. ...

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.

Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computat...

Automated Detection of Oral Malignant Lesions Using Deep Learning: Scoping Review and Meta-Analysis.

OBJECTIVE: Oral diseases, specifically malignant lesions, are serious global health concerns requiring early diagnosis for effective treatment. In rec...

Combining array-assisted SERS microfluidic chips and machine learning algorithms for clinical leukemia phenotyping.

The disease progression and treatment options of leukemia between different subtypes vary considerably, emphasizing the importance of phenotyping. How...

A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning.

Different oncologists make their own decisions about the detection and classification of the type of ovarian cancer from histopathological whole slide...

Editorial: Artificial Intelligence (AI), Digital Image Analysis, and the Future of Cancer Diagnosis and Prognosis.

On October 8 2024, the Royal Swedish Academy of Sciences announced the 2024 Nobel Prize in Physics was awarded to Hopfield and Hinton for their founda...

AI-Supported Digital Microscopy Diagnostics in Primary Health Care Laboratories: Protocol for a Scoping Review.

BACKGROUND: Digital microscopy combined with artificial intelligence (AI) is increasingly being implemented in health care, predominantly in advanced ...

Efficient brain tumor grade classification using ensemble deep learning models.

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental t...