Pathology

CellApop: A knowledge-guided decoupled distillation framework for label-efficient apoptotic cell segmentation and dynamic analysis in brightfield microscopy.

BACKGROUND AND OBJECTIVE: Conventional apoptosis detection methods primarily depend on fluorescence staining, which is labor-intensive, potentially cytotoxic, and unsuitable for real-time monitoring. To overcome these limitations, this study presents a segmentation-based deep learning (DL) framework for label-free, dynamic detection of apoptotic cells in bright-field microscopy images. METHODS: A ...

Flavonoid-modulated JAK-STAT signaling mitigates malignant transformation and drug resistance in breast tumors: A clinically relevant 3PM-guided innovation.

BACKGROUND: Breast cancer (BC) treatment efficacy is often compromised by tumor cell plasticity and multidrug resistance of multi-factorial origin. Among emerging therapeutic agents, flavonoids - a structurally diverse group of naturally occurring polyphenols - have demonstrated a significant potential to modulate the Janus kinase/signal transducer and activator of transcription (JAK-STAT) signali...

An Internet of Things operating room platform for neurosurgery.

OBJECTIVE: The operating room (OR) is a data-rich environment and largely follows closed-door policies for health data security and privacy. To overco...

Renal Cell Carcinoma subtyping: Learning from multi-resolution localization.

BACKGROUND AND OBJECTIVE: Renal Cell Carcinoma (RCC) is often diagnosed at advanced stages, limiting treatment options. Since prognosis depends on tum...

Explainable multimodal fusion for breast carcinoma diagnosis: A systematic review, open problems, and future directions.

Breast carcinoma (BC) remains one of the most common and lethal malignancies in women worldwide, making an early and accurate diagnosis a public healt...

Evaluation of an artificial intelligence model based on multiparametric transrectal ultrasound for localizing clinically significant prostate cancer by simulation of targeted biopsies.

INTRODUCTION AND OBJECTIVES: An AI model that performs well during training does not guarantee similar performance in clinical practice and should be ...

PathViT Model for Automated Disease Classification from Skeletal Muscle Histopathology.

Analyzing skeletal muscle pathology from histological images is labor intensive (requiring manual cell counting, segmentation, and thresholding), time...

Machine Learning-Based Analysis of UF-5000 Scattergrams Improves the Identification of Urinary Dysmorphic Red Blood Cells.

BACKGROUND: Identifying dysmorphic red blood cells (RBCs) is critical for diagnosing glomerular diseases, as distinguishing glomerular from non-glomer...

Deep learning based label-free virtual staining and classification of human tissues using digital slide scanner.

Hematoxylin and eosin (H&E) staining has long been a cornerstone of histopathology, typically applied to formalin-fixed, paraffin-embedded (FFPE) tiss...

Optimization of print parameters for batch and continuous manufacturing of three-dimensional (3D) printed dosage forms using artificial intelligence and machine learning.

Three-dimensional printing (3DP) holds significant potential for developing personalized pharmaceutical oral dosage forms (printlets). 3D printing has...

Development and Deployment of a Machine Learning Model to Triage the Use of Prostate MRI (ProMT-ML) in Patients With Suspected Prostate Cancer.

BACKGROUND: Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE: To develop machine lea...

A multi-task deep learning framework for intraoperative diagnosis of thyroid cancer metastasis using whole slide images.

BACKGROUND: Lymph node metastasis (LNM) is a critical prognostic indicator in papillary thyroid carcinoma (PTC), significantly influencing surgical de...

Temporal dynamics and predictive modeling of oral epithelial dysplasia features during carcinogenesis.

OBJECTIVE: To investigate the temporal evolution and predictive value of individual histopathological features of oral epithelial dysplasia (OED) duri...

Few-shot learning and explainable AI for colon cancer histopathology: A prototypical network with multi-technique interpretability.

BACKGROUND: Colon cancer diagnosis from histopathology is challenging due to limited annotated data and the lack of interpretability in deep models. O...

Machine learning-assisted renewable and polarity-switchable photoelectrochemical biosensor for circRNA intelligent diagnosis.

Herein, a renewable polarity-switchable PEC biosensor is reported for a highly sensitive and selective assay of circRNA in human whole blood, and mach...

Deep learning for accurate tumour volume measurement and prediction of therapy response in paediatric osteosarcoma.

OBJECTIVES: To assess treatment response in osteosarcoma, two automated convolutional neural networks (CNNs) were developed to quantify tumour volumes...

Precision medicine in orthopaedics: A review of current technologies and future directions.

PURPOSE: To synthesise the paradigm shift towards precision medicine in orthopaedics, where individual anatomical, biomechanical, molecular and kinema...

Deep learning-based segmentation of T1 and T2 cardiac MRI maps for automated disease detection.

OBJECTIVES: Parametric tissue mapping enables quantitative cardiac tissue characterization but is limited by inter-observer variability during manual ...

Comparison of glass-based and whole-slide image-based grade assignment for clear-cell and papillary renal cell carcinoma: a multi-cohort, international reproducibility study.

BACKGROUND AND AIMS: Histological grading of renal cell carcinoma (RCC) is an important part of diagnostic evaluation. Reproducibility of RCC grading ...

Malignant pleural mesothelioma classification and survival prediction with CT imaging using ResNet.

OBJECTIVES: This study aims to achieve accurate differentiation of malignant pleural mesothelioma (MPM) from metastatic pleural disease (MPD) and to p...