Pathology

Meta-Analysis of the Efficacy of Raman Spectroscopy and Machine-Learning-Based Identification of Glioma Tissue.

Intraoperative Raman spectroscopy (RS) has been identified as a potential tool for surgeons to rapidly and noninvasively differentiate between diseased and normal tissue. Since the previous meta-analysis on the subject was published in 2016, improvements in both spectroscopy equipment and machine learning models used to process spectra may have led to an increase in RS efficacy. Therefore, we deci...

Pathomic model based on histopathological features and machine learning to predict IDO1 status and its association with breast cancer prognosis.

PURPOSE: To establish a pathomic model using histopathological image features for predicting indoleamine 2,3-dioxygenase 1 (IDO1) status and its relationship with overall survival (OS) in breast cancer.

Masked hypergraph learning for weakly supervised histopathology whole slide image classification.

BACKGROUND AND OBJECTIVES: Graph neural network (GNN) has been extensively used in histopathology whole slide image (WSI) analysis due to the efficien...

Distillation of multi-class cervical lesion cell detection via synthesis-aided pre-training and patch-level feature alignment.

Automated detection of cervical abnormal cells from Thin-prep cytologic test (TCT) images is crucial for efficient cervical abnormal screening using c...

Diagnosis of neuropsychiatric systemic lupus erythematosus by label-free serum microsphere-coupled SERS fingerprints with machine learning.

Surface-enhanced Raman spectroscopy (SERS) is a powerful optical technique for non-invasive and label-free bioanalysis of liquid biopsy, facilitating ...

A whole-slide foundation model for digital pathology from real-world data.

Digital pathology poses unique computational challenges, as a standard gigapixel slide may comprise tens of thousands of image tiles. Prior models hav...

A Novel Deep Learning Approach for Forecasting Myocardial Infarction Occurrences with Time Series Patient Data.

Myocardial Infarction (MI) commonly referred to as a heart attack, results from the abrupt obstruction of blood supply to a section of the heart muscl...

Artificial intelligence enables precision diagnosis of cervical cytology grades and cervical cancer.

Cervical cancer is a significant global health issue, its prevalence and prognosis highlighting the importance of early screening for effective preven...

The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study.

BACKGROUND: Machine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems i...

Colour fusion effect on deep learning classification of uveal melanoma.

BACKGROUND: Reliable differentiation of uveal melanoma and choroidal nevi is crucial to guide appropriate treatment, preventing unnecessary procedures...

Hyperspectral imaging with machine learning for in vivo skin carcinoma margin assessment: a preliminary study.

Surgical excision is the most effective treatment of skin carcinomas (basal cell carcinoma or squamous cell carcinoma). Preoperative assessment of tum...

Artificial Intelligence for Real-Time Prediction of the Histology of Colorectal Polyps by General Endoscopists.

BACKGROUND: Real-time prediction of histologic features of small colorectal polyps may prevent resection and/or pathologic evaluation and therefore de...

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration.

Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complicati...

Segmentation and quantitative analysis of optical coherence tomography (OCT) images of laser burned skin based on deep learning.

Evaluation of skin recovery is an important step in the treatment of burns. However, conventional methods only observe the surface of the skin and can...

Enhanced multi-class pathology lesion detection in gastric neoplasms using deep learning-based approach and validation.

This study developed a new convolutional neural network model to detect and classify gastric lesions as malignant, premalignant, and benign. We used 1...

Deep learning and digital pathology powers prediction of HCC development in steatotic liver disease.

BACKGROUND AND AIMS: Identifying patients with steatotic liver disease who are at a high risk of developing HCC remains challenging. We present a deep...

Hybrid machine learning model based predictions for properties of poly(2-hydroxyethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacterial cellulose.

Supermacroporous composite cryogels with enhanced adjustable functionality have received extensive interest in bioseparation, tissue engineering, and ...

A hybrid CNN with transfer learning for skin cancer disease detection.

The leading cause of cancer-related deaths worldwide is skin cancer. Effective therapy depends on the early diagnosis of skin cancer through the preci...

Intrinsically explainable deep learning architecture for semantic segmentation of histological structures in heart tissue.

BACKGROUND: Analysis of structures contained in tissue samples and the relevant contextual information is of utmost importance to histopathologists du...

Histopathologic image-based deep learning classifier for predicting platinum-based treatment responses in high-grade serous ovarian cancer.

Platinum-based chemotherapy is the cornerstone treatment for female high-grade serous ovarian carcinoma (HGSOC), but choosing an appropriate treatment...