Pathology

Non-invasive Prediction of CYP11B2-Defined Subtypes in Primary Aldosteronism Using 18F-Pentixafor PET/CT and Machine Learning.

PURPOSE: This study aims to develop and validate an interpretable machine learning model that integrates clinical data, radiomics, and deep learning (DL) features extracted from 18F-AlF-NOTA-Pentixafor positron emission tomography/computed tomography (PET/CT) images for the non-invasive prediction of pathological subtypes in primary aldosteronism (PA). METHODS: In this single-center retrospective ...

A research on applying the diffusion model algorithm for Infrared and Raman spectroscopy data augmentation to improve the accuracy of diseases.

In recent years, Raman and Infrared spectroscopy have become important tools in disease diagnosis due to their high sensitivity and non-invasive detection advantages. Combined with deep learning methods, spectral data can achieve high-precision identification of a wide range of diseases. However, deep learning models rely on large amounts of high-quality data. In practice, spectral data often face...

Predicting Chronicity in Children and Adolescents With Newly Diagnosed Immune Thrombocytopenia at the Timepoint of Diagnosis Using Machine Learning-Based Approaches.

OBJECTIVES: To identify predictors of chronic ITP (cITP) and to develop a model based on several machine learning (ML) methods to estimate the individ...

Machine learning-guided identification of metastasis-associated miRNAs and their integration into a PFI-based cox risk score and nomogram.

BACKGROUND: Metastasis drives mortality in breast invasive carcinoma. We sought miRNA biomarkers that (i) discriminate metastatic potential, (ii) stra...

The answer lies within: Detecting Trojans from DNNs' inherent characteristics.

Deep neural networks (DNNs) are vulnerable to Trojan attacks, where adversaries implant Trojans that cause DNNs to misbehave when encountering specifi...

Machine learning assisted raman spectroscopy for the classification of ovarian cancer cells.

Ovarian cancer is one of the most lethal gynecological malignancies, asymptomatic early progression, ineffective screening, and high histological hete...

Utilizing artificial intelligence for the diagnosis of ocular surface squamous neoplasia with ultrasound biomicroscopy images.

PURPOSE: This study aims to develop an artificial intelligence (AI) model to assist ophthalmologists in distinguishing ocular surface squamous neoplas...

Development of a deep learning-based histological evaluation model for critical-size bone defect healing in rats - an objective tool.

INTRODUCTION: Critical-size femoral defects in rats are a well-established model for preclinical bone regeneration research. Histological evaluation i...

UTMorph: A hybrid CNN-transformer network for weakly-supervised multimodal image registration in biopsy puncture.

Accurate registration of preoperative magnetic resonance imaging (MRI) and intraoperative ultrasound (US) images is essential to enhance the precision...

Beyond the Cell Atlas: Functional Communities as the Essential Pathologic Units Driving Kidney Disease.

Kidney disease represents a growing global health crisis, demanding a deeper understanding of its underlying pathophysiology. Current mechanistic effo...

Explainability-Based Optimized Deep Learning in Histopathological Diagnosis of Multiple Cancers and Development of Mobile Application.

Histopathological image analysis is critical for cancer diagnosis, yet many existing models suffer from limited interpretability, high computational d...

CellViT++: Energy-efficient and adaptive cell segmentation and classification using foundation models.

BACKGROUND AND OBJECTIVE: Deep learning-based cell segmentation and classification methods in digital pathology are critical for diagnostics but are h...

A novel cross-domain fault diagnosis method for multi-condition industrial processes based on meta-domain adaptation with progressive meta-learning.

Complex industrial processes are characterized by high dynamics, diverse operating conditions, and strong inter-system coupling, often leading to redu...

Comparison of blood sample quality and test results between robotic and manual venipuncture: a pilot study.

OBJECTIVES: This study aimed to evaluate the feasibility of robotic venipuncture in clinical settings and compare its performance with manual venipunc...

Emerging Diagnostic Indications for Endoscopic Ultrasound.

Endoscopic ultrasound (EUS) has evolved from a diagnostic imaging tool into a versatile platform that enables high-precision access, sampling, and the...

Decoding Temporomandibular Disorders: Identifying Stage-Specific and Functional Patterns in the Articular Disc.

BACKGROUND: Despite affecting approximately 30% of the population, the pathogenesis of temporomandibular disorders (TMD) remains poorly understood. Co...

Building a national repository of dural-based lesions: clinical, pathological, and demographic insights from the Indian population.

BACKGROUND: Meningiomas are the most common dural-based intracranial tumors, yet Indian literature is predominantly composed of limited single-center ...

Artificial intelligence for colposcopic and cytological image analysis in early cervical cancer detection.

Artificial intelligence (AI) is reshaping cervical cancer screening by automating interpretation of cytology, colposcopic, and related imaging to impr...

Chemo-prAIdict Breast: A deep learning solution for predicting residual disease on biopsies of breast cancer patients treated with neoadjuvant chemotherapy.

BACKGROUND: Predicting chemosensitivity before treatment could help tailor neoadjuvant chemotherapy (NAC) in early breast cancer (eBC). Pathological c...

YiQiFuMai ameliorates hypoperfusion-induced cerebral infarction by targeting the shear stress-PLBD1-glycolysis-angiogenesis axis.

BACKGROUND: Hypoperfusion-induced cerebral infarction remains a significant therapeutic challenge due to limited treatment options. Yiqi Fumai (YQFM),...