Pathology

Artificial intelligence for detection, grading, and prognostication in prostate cancer pathology: A scoping review.

Artificial intelligence (AI) has been transforming many aspects of medical care. In prostate cancer, ongoing progress in AI has improved research and patient care. Recent advances in machine learning and deep learning have produced tools that help diagnose cancer, assess risk, and predict outcomes. In screening, AI-based risk calculators improve detection and help avoid unnecessary biopsies. Deep ...

International Delphi consensus on acute kidney injury: Foundations for AI-driven digital twin development in critical care nephrology.

BACKGROUND: Acute kidney injury (AKI) in critically ill patients is clinically complex and heterogeneous, limiting the development of structured simulation models. Digital twin approaches require clearly defined causal relationships grounded in expert consensus. We aimed to establish an international Delphi consensus to inform the development of an interpretable, AI-driven digital twin framework f...

AI-discovered cellular morphometric biomarkers in needle biopsy of prostate cancer predict neoadjuvant androgen deprivation therapy response and enable therapeutic targeting of mTOR in androgen deprivation therapy-resistant tumors.

It is imperative to identify patients with prostate cancer (PCa) who will not benefit from androgen receptor signaling inhibitors and to improve their...

Assessment of a Deep Learning Model Trained on Permanent Pathology for the Classification of Squamous Cell Carcinoma in Mohs Frozen Sections: Lessons Learned.

ALDH5A1 as a key in osteonecrosis of the femoral head: Insights from bioinformatics and experimental validation.

The present study aims to identify key genes and elucidate the molecular mechanisms underlying osteonecrosis of the femoral head (ONFH), with the goal...

Bioinformatics and machine learning identify ITIH4 and ZC3H13 as novel mRNA biomarkers for major depressive disorder that promote microglial M1 polarization.

UNLABELLED: Major depressive disorder (MDD), a prevalent mental illness, currently lacks reliable biomarkers and depends predominantly on subjective d...

2D Multimodal Image Collection for Fluorescence Prediction from Transmitted Light Microscopy.

We present the Light My Cells Database, a large-scale open-access collection comprising 2,574 acquisition sets and 56,984 microscopy 2D images designe...

Machine learning vs. radiomics for discriminating atypical cartilaginous tumors from benign enchondromas on MRI.

BACKGROUND: Enchondromas (EC) present cartilaginous tumors that are difficult to differentiate from their intermediate counterpart, atypical cartilagi...

Segmentation and diagnosis of anterior cruciate ligament tear using deep learning and radiomics based on knee CT.

PURPOSE: Timely and accurate diagnosis of anterior cruciate ligament (ACL) tears has important clinical significance. In this study we tried to establ...

Artificial Intelligence-Driven Segmentation of Three Oral Diseases: Enabling Precision Diagnosis and Decision Support.

INTRODUCTION: The advancement of AI-assisted oral disease diagnosis critically relies on the availability of annotated, high-quality datasets and resi...

Integrated single-cell and bulk transcriptomic analysis identifies epithelial cell-related biomarkers and immune subtypes in focal segmental glomerulosclerosis.

BACKGROUND: Focal segmental glomerulosclerosis (FSGS) is a major cause of nephrotic syndrome and chronic kidney disease, characterized by podocyte inj...

CT-Based Automated Segmentation and Recurrence Prediction in Chronic Subdural Hematoma: A Dual-Label Multicenter Study.

Chronic subdural hematoma (CSDH) frequently recurs after burr-hole surgery, yet most prior imaging studies have focused primarily on hematoma and have...

A deep learning pipeline for mapping in situ network-level neurovascular coupling in multi-photon fluorescence microscopy.

Functional hyperemia is a well-established hallmark of healthy brain function, whereby local brain blood flow adjusts in response to a change in the a...

Imaging-Based Prediction of Key Breast Cancer Biomarkers Using Deep Learning on Digital Breast Tomosynthesis.

OBJECTIVE: To evaluate the feasibility of using deep learning models applied to digital breast tomosynthesis (DBT) images for non-invasive prediction ...

Deep learning reconstruction enables accelerated T2-weighted MRI for rectal cancer staging: a prospective study of diagnostic consistency across NEX value reduction.

OBJECTIVES: To evaluate the image quality, interpretation consistency, and scanning efficiency of deep learning-based reconstruction (DLR) algorithm (...

PNET-PRISM: a multicenter-validated radiomics nomogram for noninvasive grading of pancreatic neuroendocrine tumors.

BACKGROUND: Accurate preoperative grading of pancreatic neuroendocrine tumors (PNETs) is essential for optimal treatment selection, yet endoscopic ult...

Multimodal biomarker AI techniques for early neurocognitive disorder diagnosis: A systematic review.

BACKGROUND: Early diagnosis of Alzheimer's disease (AD) and related dementias remains challenging because no single biomarker sufficiently captures th...

Negligible impact of perifissural nodules in an AI-first reader workflow from UK lung screening trial.

OBJECTIVE: To evaluate the effect of perifissural nodules (PFNs) on radiologist workload within an AI-first reader workflow for lung cancer screening,...

A deep learning approach for the diagnosis and recurrence prediction of OKC.

Odontogenic Keratocyst (OKC) is a benign jaw tumor characterized by a high recurrence rate. However, its clinical diagnosis presents significant chall...

An enhanced diabetic retinopathy detection approach using optimized deep learning technique.

Diabetic Retinopathy (DR) remains a leading cause of vision loss among diabetic patients, underscoring the importance of early detection through relia...