Cardiovascular

Integration and interplay of machine learning and bioinformatics approach to identify genetic interaction related to ovarian cancer chemoresistance.

Although chemotherapy is the first-line treatment for ovarian cancer (OCa) patients, chemoresistance (CR) decreases their progression-free survival. This paper investigates the genetic interaction (GI) related to OCa-CR. To decrease the complexity of establishing gene networks, individual signature genes related to OCa-CR are identified using a gradient boosting decision tree algorithm. Additional...

Robotics, imaging, and artificial intelligence in the catheterisation laboratory.

The catheterisation laboratory today combines diagnosis and therapeutics, through various imaging modalities and a prolific list of interventional tools, led by balloons and stents. In this review, we focus primarily on advances in image-based coronary interventions. The X-ray images that are the primary modality for diagnosis and interventions are combined with novel tools for visualisation and d...

Automatic contour segmentation of cervical cancer using artificial intelligence.

In cervical cancer treatment, radiation therapy is selected based on the degree of tumor progression, and radiation oncologists are required to deline...

Prediction of Neutropenic Events in Chemotherapy Patients: A Machine Learning Approach.

PURPOSE: Severe and febrile neutropenia present serious hazards to patients with cancer undergoing chemotherapy. We seek to develop a machine learning...

Highly accurate diagnosis of papillary thyroid carcinomas based on personalized pathways coupled with machine learning.

Thyroid nodules are neoplasms commonly found among adults, with papillary thyroid carcinoma (PTC) being the most prevalent malignancy. However, curren...

Artificial Intelligence in Diagnostic Imaging and Radiation Therapy.

Artificial intelligence (AI) is a rapidly growing profession that shows continued promise for diagnostic imaging in various disciplines, including der...

Artificial intelligence technologies empowering identification of novel diagnostic molecular markers in gastric cancer.

In recent clinical practice the molecular diagnostics have been significantly empowered and upgraded by the use of Artificial Intelligence and its ass...

Survey and comparative assessments of computational multi-omics integrative methods with multiple regulatory networks identifying distinct tumor compositions across pan-cancer data sets.

The significance of pan-cancer categories has recently been recognized as widespread in cancer research. Pan-cancer categorizes a cancer based on its ...

Magnetic resonance imaging-based artificial intelligence model in rectal cancer.

Rectal magnetic resonance imaging (MRI) is the preferred method for the diagnosis of rectal cancer as recommended by the guidelines. Rectal MRI can ac...

The potential for reduced radiation dose from deep learning-based CT image reconstruction: A comparison with filtered back projection and hybrid iterative reconstruction using a phantom.

The purpose of this phantom study is to compare radiation dose and image quality of abdominal computed tomography (CT) scanned with different tube vol...

Robotic Radical Cystectomy Outcomes after Intervention for Prostate Cancer.

We evaluated patients who underwent treatment for prostate cancer and then subsequent robot-assisted radical cystectomy (RARC). Our objective was to ...

[Intelligent prediction of HER2 status based on breast histopathology].

To study the association between histopathological features and HER2 overexpression/amplification in breast cancers using deep learning algorithms. ...

Automated approach for segmenting gross tumor volumes for lung cancer stereotactic body radiation therapy using CT-based dense V-networks.

The aim of this study was to develop an automated segmentation approach for small gross tumor volumes (GTVs) in 3D planning computed tomography (CT) i...

Women's attitudes to the use of AI image readers: a case study from a national breast screening programme.

BACKGROUND: Researchers and developers are evaluating the use of mammogram readers that use artificial intelligence (AI) in clinical settings.

Feasibility Study on Automatic Interpretation of Radiation Dose Using Deep Learning Technique for Dicentric Chromosome Assay.

The interpretation of radiation dose is an important procedure for both radiological operators and persons who are exposed to background or artificial...

Deep learning based analysis of sentiment dynamics in online cancer community forums: An experience.

Online health communities (OHC) provide various opportunities for patients with chronic or life-threatening illnesses, especially for cancer patients ...

CT Dosimetry: What Has Been Achieved and What Remains to Be Done.

Radiation dose in computed tomography (CT) has become a hot topic due to an upward trend in the number of CT procedures worldwide and the relatively h...

Chemotherapy Knowledge Base Management in the Era of Precision Oncology.

Cancer medicine has grown increasingly complex in recent years with the advent of precision oncology and wide utilization of multidrug regimens. Repre...

Machine Learning Frameworks to Predict Neoadjuvant Chemotherapy Response in Breast Cancer Using Clinical and Pathological Features.

PURPOSE: Neoadjuvant chemotherapy (NAC) is used to treat locally advanced breast cancer (LABC) and high-risk early breast cancer (BC). Pathological co...

Dual residual convolutional neural network (DRCNN) for low-dose CT imaging.

The excessive radiation doses in the application of computed tomography (CT) technology pose a threat to the health of patients. However, applying a l...