AI Medical Compendium Journal:
Histopathology

Showing 1 to 10 of 34 articles

Ki-67 evaluation using deep-learning model-assisted digital image analysis in breast cancer.

Histopathology
AIMS: To test the efficacy of artificial intelligence (AI)-assisted Ki-67 digital image analysis in invasive breast carcinoma (IBC) with quantitative assessment of AI model performance.

Role of artificial intelligence in haematolymphoid diagnostics.

Histopathology
The advent of digital pathology and the deployment of high-throughput molecular techniques are generating an unprecedented mass of data. Thanks to advances in computational sciences, artificial intelligence (AI) approaches represent a promising avenu...

A deep learning approach to case prioritisation of colorectal biopsies.

Histopathology
AIMS: To create and validate a weakly supervised artificial intelligence (AI) model for detection of abnormal colorectal histology, including dysplasia and cancer, and prioritise biopsies according to clinical significance (severity of diagnosis).

Development of a deep-learning model tailored for HER2 detection in breast cancer to aid pathologists in interpreting HER2-low cases.

Histopathology
AIMS: Over 50% of breast cancer cases are "Human epidermal growth factor receptor 2 (HER2) low breast cancer (BC)", characterized by HER2 immunohistochemistry (IHC) scores of 1+ or 2+ alongside no amplification on fluorescence in situ hybridization (...

Artificial intelligence enhances whole-slide interpretation of PD-L1 CPS in triple-negative breast cancer: A multi-institutional ring study.

Histopathology
BACKGROUND AND AIMS: Evaluation of the programmed cell death ligand-1 (PD-L1) combined positive score (CPS) is vital to predict the efficacy of the immunotherapy in triple-negative breast cancer (TNBC), but pathologists show substantial variability i...

Histological interpretation of spitzoid tumours: an extensive machine learning-based concordance analysis for improving decision making.

Histopathology
The histopathological classification of melanocytic tumours with spitzoid features remains a challenging task. We confront the complexities involved in the histological classification of these tumours by proposing machine learning (ML) algorithms tha...

Deceptive learning in histopathology.

Histopathology
AIMS: Deep learning holds immense potential for histopathology, automating tasks that are simple for expert pathologists and revealing novel biology for tasks that were previously considered difficult or impossible to solve by eye alone. However, the...

An artificial intelligence-powered PD-L1 combined positive score (CPS) analyser in urothelial carcinoma alleviating interobserver and intersite variability.

Histopathology
AIMS: Immune checkpoint inhibitors targeting programmed death-ligand 1 (PD-L1) have shown promising clinical outcomes in urothelial carcinoma (UC). The combined positive score (CPS) quantifies PD-L1 22C3 expression in UC, but it can vary between path...

Pros and cons of artificial intelligence implementation in diagnostic pathology.

Histopathology
The rapid introduction of digital pathology has greatly facilitated development of artificial intelligence (AI) models in pathology that have shown great promise in assisting morphological diagnostics and quantitation of therapeutic targets. We are n...