Pathology

Machine learning in the diagnosis of Hirschsprung disease: a systematic review and meta-analysis.

PURPOSE: To evaluate current evidence on machine learning (ML) for the diagnosis of Hirschsprung disease (HSCR) and summarize its diagnostic performance and potential clinical utility. METHODS: PubMed, Web of Science, Cochrane Library, and Scopus were systematically searched (January 2016-November 2025) for studies applying ML to HSCR diagnosis. Study quality was assessed using QUADAS-2. Findings ...

Deep learning-based automated detection of oral squamous cell carcinoma in histopathological images: a comparative study of five CNN architectures.

Oral squamous cell carcinoma (OSCC) is the most common malignancy of the oral cavity, and early diagnosis plays a crucial role in improving patient prognosis and survival rates. Histopathological examination remains the gold standard for OSCC diagnosis; however, this process is time-consuming and highly dependent on expert interpretation. With the rapid development of digital pathology and artific...

A radio-pathological fusion model for predicting PD-L1 expression and immunotherapy response in non-small cell lung cancer.

OBJECTIVE: This study aims to construct a multimodal fusion model (FM) based on CT and hematoxylin and eosin (H&E) stained slices to predict the PD-L1...

E2E-OCT: end-to-end joint learning model using optical coherence tomography images for vocal cord leukoplakia diagnosis.

In our previous study, a home-built handheld OCT system was used to collect OCT images in vocal cord leukoplakia. First, 383 valid OCT images were col...

An Aptamer-Engineered Phenotyping System for Extracellular Vesicles Based on Honeycomb-like Melamine-Formaldehyde Microspheres and Carbon Dot Nanozymes.

Molecular phenotyping of extracellular vesicles (EVs) holds promise for noninvasive cancer diagnosis; however, current methodologies encounter limitat...

A myeloid immunosuppressive phenotype defines primary refractoriness to atezolizumab Plus bevacizumab in hepatocellular caarcinoma.

BACKGROUND: Despite improved outcomes with atezolizumab plus bevacizumab (A+B) in hepatocellular carcinoma (HCC), primary refractoriness (PRef), chara...

Multimodal multitask deep learning for grading management system in non-small cell lung cancer.

Accurate histologic subtyping, tumor node metastasis classification (TNM) staging and prognostic assessment are central to clinical management of non-...

Robust artificial intelligence frameworks for lung cancer subtyping and malignancy detection on thoracic CT.

Accurate characterization of thoracic malignancies on computed tomography (CT) remains challenging because histological subtype differentiation and no...

Effectiveness of Artificial Intelligence in the Automatic Diagnosis of Lesions Detected in Panoramic Radiographs.

This study was aimed at evaluating the effectiveness of artificial intelligence (AI) in detecting jaw cysts and tumors, analyzing lesion content, and ...

Integrating ultrasound-CT-MR for preoperative multi-task prediction in ovarian cancer: achieving diagnostic parity with multidisciplinary team consensus.

Ovarian cancer, with high mortality, demands accurate preoperative assessment to guide individualized treatment. It typically requires ultrasound, CT,...

Deep learning analysis of breast cancer histology predicts ATM pathogenic variant carrier status.

We performed deep learning analysis of histopathological whole-slide (full-face) images (WSI) to predict ATM pathogenic or likely pathogenic variant (...

Non-invasive imaging diagnostic techniques in metabolic dysfunction-associated steatotic liver disease: a roadmap for clinicians.

Metabolic dysfunction-associated steatotic liver disease (MASLD), previously termed nonalcoholic fatty liver disease (NAFLD), is the most prevalent ch...

Multiscale Biophysical Characterization of Ultra-Short Peptide Hydrogels.

Ultra-short peptide (USP) hydrogels have emerged as a simple yet innovative class of biomaterial. Small peptide sequences (≤8 amino acid residues) sel...

Artificial intelligence in pathology: a framework for preserving brain capital in the diagnostic apex.

AIM: To present a five-criterion calibration framework for evaluating artificial intelligence (AI) tools in pathology centered on preserving "brain ca...

MICLEAR: Intelligent molecular cytology for intraoperative margin assessment of pancreatic ductal adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a highly mortal cancer whose only potentially curative treatment is surgical resection. Intraoperative asse...

Development and temporal validation of an interpretable prediction model for delayed diagnosis of benign paroxysmal positional vertigo: a retrospective study from Beijing, China.

OBJECTIVE: To develop and validate an interpretable prediction model for delayed diagnosis of benign paroxysmal positional vertigo (BPPV). METHODS: Th...

Adapting transformer-based encoder models for automated billing code assignment of current procedural terminology from pathology reports in multi-institutional settings.

Accurate pathology billing is crucial for financial sustainability, process optimization, and regulatory compliance. Current procedural terminology (C...

ACCURACY OF IMAGE-BASED DEEP LEARNING IN ORAL SQUAMOUS CELL CARCINOMA DIAGNOSIS: A SYSTEMATIC REVIEW AND META-ANALYSIS.

OBJECTIVES: The potential of image-based deep learning (DL) in the diagnosis of oral squamous cell carcinoma has been investigated recently. This revi...

Mitochondrial Metabolic Biomarkers in Periodontitis: Discovery and Clinical Validation.

INTRODUCTION AND AIMS: Mitochondrial metabolic dysregulation is associated with periodontitis (PD); however, related biomarkers remain unclear. In thi...

AMCD: A multi-domain agricultural crop and flower image dataset for deep learning-based classification.

The Agricultural Multidisciplinary Collection Dataset (AMCD) contains 5405 JPG images of agricultural crops and flowers collected in Bangladesh. The i...