Oncology/Hematology

Effect of an Artificial Intelligence Decision Support Tool on Palliative Care Referral in Hospitalized Patients: A Randomized Clinical Trial.

CONTEXT: Palliative care services are commonly provided to hospitalized patients, but accurately predicting who needs them remains a challenge.

Diagnosing malignant distal bile duct obstruction using artificial intelligence based on clinical biomarkers.

Diagnosing distal bile duct obstruction remains challenging. This study aimed to examine the diagnostic ability of artificial intelligence (AI) based on clinical biomarkers in diagnosing malignant distal bile duct obstruction. A total of 206 patients with distal bile duct obstruction were included in this study. Clinical laboratory parameters were collected from the patients and evaluated using AI...

Hepatocellular Carcinoma Recognition from Ultrasound Images Using Combinations of Conventional and Deep Learning Techniques.

Hepatocellular Carcinoma (HCC) is the most frequent malignant liver tumor and the third cause of cancer-related deaths worldwide. For many years, the ...

Automated quantification of measurable residual disease in chronic lymphocytic leukemia using an artificial intelligence-assisted workflow.

Detection of measurable residual disease (MRD) in chronic lymphocytic leukemia (CLL) is an important prognostic marker. The most common CLL MRD method...

The devil is in the details: a small-lesion sensitive weakly supervised learning framework for prostate cancer detection and grading.

Prostate cancer (PCa) is a significant health concern in aging males, and the diagnosis depends primarily on histopathological assessments to determin...

Deep learning aided oropharyngeal cancer segmentation with adaptive thresholding for predicted tumor probability in FDG PET and CT images.

 Tumor segmentation is a fundamental step for radiotherapy treatment planning. To define an accurate segmentation of the primary tumor (GTVp) of oroph...

Evaluation of auto-segmentation for brachytherapy of postoperative cervical cancer using deep learning-based workflow.

. The purpose of this study was to evaluate the accuracy of brachytherapy (BT) planning structures derived from Deep learning (DL) based auto-segmenta...

Cross-Domain Transfer of EEG to EEG or ECG Learning for CNN Classification Models.

Electroencephalography (EEG) is often used to evaluate several types of neurological brain disorders because of its noninvasive and high temporal reso...

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology.

BACKGROUND: The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding e...

Artificial Intelligence in Brain Tumor Imaging: A Step toward Personalized Medicine.

The application of artificial intelligence (AI) is accelerating the paradigm shift towards patient-tailored brain tumor management, achieving optimal ...

Evaluation of real-time tumor contour prediction using LSTM networks for MR-guided radiotherapy.

BACKGROUND AND PURPOSE: Magnetic resonance imaging guided radiotherapy (MRgRT) with deformable multileaf collimator (MLC) tracking would allow to tack...

Artificial intelligence based personalized predictive survival among colorectal cancer patients.

BACKGROUND AND OBJECTIVE: Colorectal cancer is a major health concern. It is now the third most common cancer and the fourth leading cause of cancer m...

Clinical applications of deep learning in breast MRI.

Deep learning (DL) is one of the most powerful data-driven machine-learning techniques in artificial intelligence (AI). It can automatically learn fro...

Machine learning approaches to predict drug efficacy and toxicity in oncology.

In recent years, there has been a surge of interest in using machine learning algorithms (MLAs) in oncology, particularly for biomedical applications ...

Brain Tumor Segmentation and Classification from Sensor-Based Portable Microwave Brain Imaging System Using Lightweight Deep Learning Models.

Automated brain tumor segmentation from reconstructed microwave (RMW) brain images and image classification is essential for the investigation and mon...

Ant Colony Optimization-Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer.

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one...

An integrated nomogram combining deep learning, clinical characteristics and ultrasound features for predicting central lymph node metastasis in papillary thyroid cancer: A multicenter study.

OBJECTIVE: Central lymph node metastasis (CLNM) is a predictor of poor prognosis for papillary thyroid carcinoma (PTC) patients. The options for surge...

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases.

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelli...

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology.

Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays....

Gene-environment interaction analysis via deep learning.

Gene-environment (G-E) interaction analysis plays an important role in studying complex diseases. Extensive methodological research has been conducted...