Pathology

Self-driving bioprinting laboratories.

The severe shortage of donor organs and limitations of current disease models highlight the urgent need for transformative strategies in tissue engineering (TE) and regenerative medicine (RM). Bioprinting has emerged as a powerful approach for creating functional tissues and organs, yet current workflows remain labor-intensive, variable, and challenging to scale. The convergence of artificial inte...

Machine Learning-Based Detection of Intestinal Metaplasia Identifies Clinically Missed Cases.

CONTEXT.—: Gastric intestinal metaplasia is a recognized precursor and risk factor to gastric cancer, and correct diagnosis is required for clinical decision-making. OBJECTIVE.—: To evaluate potential missed intestinal metaplasia diagnoses and develop automated approaches to quantification, we developed an artificial intelligence pipeline for review of both whole slide images and pathology reports...

Deep learning-based automated contrast enema analysis to improve the assessment of Hirschsprung disease.

OBJECTIVES: To compare the radiologic assessment of Hirschsprung disease (HD) based on contrast enema with automated image analysis using a deep neura...

Review of flow cytometry findings and associated scoring approaches for identifying myelodysplastic syndrome and the future role of machine learning in improving the diagnostic algorithm.

OBJECTIVE: Myelodysplastic syndrome (MDS) is a heterogeneous set of neoplasms that require careful exclusion of potential mimics before diagnosis. In ...

A screening strategy based on machine learning for diagnostic biomarkers in small cell lung cancer.

Small cell lung cancer (SCLC) is the most aggressive subtype with high mortality rates due to the lack of specific diagnostic biomarkers to delay the ...

Machine learning-assisted detection of canine mammary tumors using serum autoantibody signatures.

Canine mammary tumors (CMTs) are the most common neoplasms in intact female dogs, yet early detection remains challenging due to the lack of clinicall...

Early diagnosis of Alzheimer's disease from functional rs-fMRI images based on deep learning networks and transfer learning approach.

Exploiting deep learning methods to accelerate the analysis of medical images and the interpretation of pathology results for early diagnosis of Alzhe...

Integrating thermal liquid biopsy, clinical data, and mass spectrometry for early diagnosis and biomarker discovery in colorectal cancer.

Early detection of colorectal cancer is essential to improving survival, where yet current diagnostic tools show limited performance. This study aimed...

Artificial Intelligence Versus Radiologist False-Positives on Digital Breast Tomosynthesis Examinations in a Population-Based Screening Program.

BACKGROUND. Insights into the nature of false-positive findings flagged by contemporary mammography artificial intelligence (AI) systems could inform ...

Artificial Intelligence Helps Diagnose Oral Potentially Malignant Disorders: A Systematic Review and Meta-Analysis.

INTRODUCTION: Oral potentially malignant disorders (OPMDs) can lead to oral cancer, which is one of the most common cancers worldwide. Prevention is c...

Real-Time Characterization of Colonic Polyps: A Multicenter Prospective Study Evaluating the CAD-EYE System in Screening Colonoscopies.

BACKGROUND AND STUDY AIMS: Polypectomy-related costs could potentially be reduced through optical diagnosis strategies, such as 'diagnose-and-leave' a...

Multi-omics integrative analysis of stemness-associated pathological signatures to guide prognosis and therapeutic strategies in uveal melanoma.

BACKGROUND: Uveal melanoma (UVM) is the most common intraocular malignancy in adults and exhibits poor prognosis upon metastasis. Stemness, a hallmark...

Comparison of conventional 512-matrix CT images with Swin2SR-based 2048-matrix images in the visualization and diagnosis of lung nodules.

PURPOSE: Clear visualization and diagnosis of lung nodules depend on the spatial resolution of CT images. Transformer-based generative neural networks...

STF: A Unified Framework for Joint Pixel-Level Segmentation and Tracking of Tissues in Endoscopic Surgery.

Endoscopic minimally invasive surgery relies on precise tissue video segmentation to avoid complications such as vascular bleeding or nerve injury. Ho...

Histo-Miner: Deep learning based tissue features extraction pipeline from H&E whole slide images of cutaneous squamous cell carcinoma.

Recent advances in digital pathology have enabled comprehensive analyses of Whole-Slide Images (WSIs) from tissue samples, leveraging high-resolution ...

iMCN: information compression-based multimodal confidence-guided fusion network for cancer survival prediction.

The rapid development of deep learning-based computational pathology and genomics has demonstrated the significant promise of effectively integrating ...

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity.

BACKGROUND: Despite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly ...

Reciprocal cooperative gating fusion of SqueezeNet and ShuffleNetV2 for breast cancer detection in histopathology images.

Breast cancer remains a critical global health challenge, with timely and reliable diagnosis being essential for improving clinical outcomes. Although...

An interpretable deep learning model for predicting endometrial cancer molecular subtypes from H&E-stained slides.

The molecular subtype of endometrial cancer is important for predicting prognosis and treatment effectiveness. This study aimed to develop an interpre...

Spatial-Spectral Deep Learning for Prostate Cancer Tissue Classification in Infrared Spectroscopy.

Modern methods of infrared (IR) spectroscopy yield full IR absorbance spectra in arrays, forming hyperspectral images. End-to-end processing of these ...