AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Frozen Sections

Showing 1 to 10 of 27 articles

Clear Filters

Solitary Fibrous Tumor of the Prostate Treated with Frozen-Section Supported Robot-Assisted Nerve-Sparing Radical Prostatectomy.

Urologia internationalis
INTRODUCTION: Solitary fibrous tumors (SFTs) of the prostate are extremely rare. We report on a 60-year-old man who was diagnosed with prostatic SFT through transurethral resection (TUR) of the prostate, and we provide a narrative literature review t...

A deep learning algorithm to detect cutaneous squamous cell carcinoma on frozen sections in Mohs micrographic surgery: A retrospective assessment.

Experimental dermatology
Intraoperative margin analysis is crucial for the successful removal of cutaneous squamous cell carcinomas (cSCC). Artificial intelligence technologies (AI) have previously demonstrated potential for facilitating rapid and complete tumour removal usi...

Improved artificial intelligence discrimination of minor histological populations by supplementing with color-adjusted images.

Scientific reports
Despite the dedicated research of artificial intelligence (AI) for pathological images, the construction of AI applicable to histopathological tissue subtypes, is limited by insufficient dataset collection owing to disease infrequency. Here, we prese...

A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.

Nature communications
Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathol...

Pathology diagnosis of intraoperative frozen thyroid lesions assisted by deep learning.

BMC cancer
BACKGROUND: Thyroid cancer is a common thyroid malignancy. The majority of thyroid lesion needs intraoperative frozen pathology diagnosis, which provides important information for precision operation. As digital whole slide images (WSIs) develop, dee...

The Future of Surgical Diagnostics: Artificial Intelligence-Enhanced Detection of Ganglion Cells for Hirschsprung Disease.

Laboratory investigation; a journal of technical methods and pathology
Hirschsprung disease, a congenital disease characterized by the absence of ganglion cells, presents significant surgical challenges. Addressing a critical gap in intraoperative diagnostics, we introduce transformative artificial intelligence approach...

Deep Learning for Automated Segmentation of Basal Cell Carcinoma on Mohs Micrographic Surgery Frozen Section Slides.

Dermatologic surgery : official publication for American Society for Dermatologic Surgery [et al.]
BACKGROUND: Deep learning has been used to classify basal cell carcinoma (BCC) on histopathologic images. Segmentation models, required for localization of tumor on Mohs surgery (MMS) frozen section slides, have yet to reach clinical utility.

Detection of metastatic breast carcinoma in sentinel lymph node frozen sections using an artificial intelligence-assisted system.

Pathology, research and practice
We developed an automatic method based on a convolutional neural network (CNN) that identifies metastatic lesions in whole slide images (WSI) of intraoperative frozen sections from sentinel lymph nodes in breast cancer. A total of 954 sentinel lymph ...

A Multi-Perspective Self-Supervised Generative Adversarial Network for FS to FFPE Stain Transfer.

IEEE transactions on medical imaging
In clinical practice, frozen section (FS) images can be utilized to obtain the immediate pathological results of the patients in operation due to their fast production speed. However, compared with the formalin-fixed and paraffin-embedded (FFPE) imag...

Deep learning-based analysis of gross features for ovarian epithelial tumors classification: A tool to assist pathologists for frozen section sampling.

Human pathology
Computational pathology has primarily focused on analyzing tissue slides, neglecting the valuable information contained in gross images. To bridge this gap, we proposed a novel approach leveraging the Swin Transformer architecture to develop a Swin-T...