Cardiovascular

Radiation Protection and Occupational Exposure on Ga-PSMA-11-Based Cerenkov Luminescence Imaging Procedures in Robot-Assisted Prostatectomy.

Cerenkov luminescence imaging (CLI) was successfully implemented in the intraoperative context as a form of radioguided cancer surgery, showing promise in the detection of surgical margins during robot-assisted radical prostatectomy. The present study was designed to provide a quantitative description of the occupational radiation exposure of surgery and histopathology personnel from CLI-guided ro...

Deep Learning for Radiotherapy Outcome Prediction Using Dose Data - A Review.

Artificial intelligence, and in particular deep learning using convolutional neural networks, has been used extensively for image classification and segmentation, including on medical images for diagnosis and prognosis prediction. Use in radiotherapy prognostic modelling is still limited, however, especially as applied to toxicity and tumour response prediction from radiation dose distributions. W...

Byzantine-robust federated learning via credibility assessment on non-IID data.

Federated learning is a novel framework that enables resource-constrained edge devices to jointly learn a model, which solves the problem of data prot...

A machine and human reader study on AI diagnosis model safety under attacks of adversarial images.

While active efforts are advancing medical artificial intelligence (AI) model development and clinical translation, safety issues of the AI models eme...

Radiation Dose Reduction in Digital Mammography by Deep-Learning Algorithm Image Reconstruction: A Preliminary Study.

PURPOSE: To develop a denoising convolutional neural network-based image processing technique and investigate its efficacy in diagnosing breast cancer...

Molecular Biology in Treatment Decision Processes-Neuro-Oncology Edition.

Computational approaches including machine learning, deep learning, and artificial intelligence are growing in importance in all medical specialties a...

Predicting distant metastases in soft-tissue sarcomas from PET-CT scans using constrained hierarchical multi-modality feature learning.

Positron emission tomography-computed tomography (PET-CT) is regarded as the imaging modality of choice for the management of soft-tissue sarcomas (ST...

Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising.

The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient's associated radi...

Optimizing Lipofectamine LTX Complex and G-418 Concentration for Improvement of Transfection Efficiency in Human Mesenchymal Stem Cells.

Conventional cancer therapies, including surgery, radiotherapy, and chemotherapy, are not tumor site-specific and have cytotoxic and harmful side effe...

Design and Characterization of Liposomal Methotrexate and Its Effect on BT-474 Breast Cancer Cell Line.

Breast cancer is the most common type of cancer among women worldwide. Traditional treatments, including chemotherapy, surgery, mastectomy, and radio...

Analysis of Curative Effect and Prognostic Factors of Radiotherapy for Esophageal Cancer Based on the CNN.

An esophageal cancer intelligent diagnosis system is developed to improve the recognition rate of esophageal cancer image diagnosis and the efficiency...

Complex Relationship Between Artificial Intelligence and CT Radiation Dose.

Concerns over need for CT radiation dose optimization and reduction led to improved scanner efficiency and introduction of several reconstruction tech...

Computed tomography-based deep-learning prediction of induction chemotherapy treatment response in locally advanced nasopharyngeal carcinoma.

BACKGROUND: Deep learning methods have great potential to predict treatment response. The objective of this study was to evaluate and validate the pre...

Deep learning-based auto-segmentation of clinical target volumes for radiotherapy treatment of cervical cancer.

OBJECTIVES: Because radiotherapy is indispensible for treating cervical cancer, it is critical to accurately and efficiently delineate the radiation t...

Radiation dose reduction with deep-learning image reconstruction for coronary computed tomography angiography.

OBJECTIVES: Deep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnost...

Using Machine Learning Approaches to Predict Short-Term Risk of Cardiotoxicity Among Patients with Colorectal Cancer After Starting Fluoropyrimidine-Based Chemotherapy.

Cardiotoxicity is a severe side effect for colorectal cancer (CRC) patients undergoing fluoropyrimidine-based chemotherapy. To develop and compare mac...

The Long-Term Effect of Intensity Modulated Radiation Therapy for Prostate Cancer on Testosterone Levels.

PURPOSE: Concern about a long-term effect of the delivery of intensity modulated radiation therapy (IMRT) for prostate cancer on serum testosterone le...

Prospective pilot study protocol evaluating the safety and feasibility of robot-assisted nipple-sparing mastectomy (RNSM).

INTRODUCTION: Nipple-sparing mastectomy (NSM) can be performed for the treatment of breast cancer and risk reduction, but total mammary glandular exci...

Deep learning-based motion tracking using ultrasound images.

PURPOSE: Ultrasound (US) imaging is an established imaging modality capable of offering video-rate volumetric images without ionizing radiation. It ha...

Integration of Deep Learning Radiomics and Counts of Circulating Tumor Cells Improves Prediction of Outcomes of Early Stage NSCLC Patients Treated With Stereotactic Body Radiation Therapy.

PURPOSE: We develop a deep learning (DL) radiomics model and integrate it with circulating tumor cell (CTC) counts as a clinically useful prognostic m...