AI Medical Compendium Topic

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

Spinal Neoplasms

Showing 21 to 30 of 30 articles

Clear Filters

Improving Computer-Aided Detection Using Convolutional Neural Networks and Random View Aggregation.

IEEE transactions on medical imaging
Automated computer-aided detection (CADe) has been an important tool in clinical practice and research. State-of-the-art methods often show high sensitivities at the cost of high false-positives (FP) per patient rates. We design a two-tiered coarse-t...

Inverse treatment planning for spinal robotic radiosurgery: an international multi-institutional benchmark trial.

Journal of applied clinical medical physics
Stereotactic radiosurgery (SRS) is the accurate, conformal delivery of high-dose radiation to well-defined targets while minimizing normal structure doses via steep dose gradients. While inverse treatment planning (ITP) with computerized optimization...

A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks.

Computers in biology and medicine
Spinal metastasis, a metastatic cancer of the spine, is the most common malignant disease in the spine. In this study, we investigate the feasibility of automated spinal metastasis detection in magnetic resonance imaging (MRI) by using deep learning ...

Shared Decision-Making Ontology for a Healthcare Team Executing a Workflow, an Instantiation for Metastatic Spinal Cord Compression Management.

AMIA ... Annual Symposium proceedings. AMIA Symposium
Regardless of potential benefits and better outcomes, adoption of shared decision-making between a patient and providers involved in his/her care is still in its infancy. This paper intends to fill this gap by formalizing shared decision-making, situ...

Deep convolutional neural network-based segmentation and classification of difficult to define metastatic spinal lesions in 3D CT data.

Medical image analysis
This paper aims to address the segmentation and classification of lytic and sclerotic metastatic lesions that are difficult to define by using spinal 3D Computed Tomography (CT) images obtained from highly pathologically affected cases. As the lesion...

Development of Machine Learning Algorithms for Prediction of 5-Year Spinal Chordoma Survival.

World neurosurgery
BACKGROUND: Chordomas are locally invasive slow-growing tumors that are difficult to study because of the rarity of the tumors and the lack of significant volumes of patients with longitudinal follow-up. As such, there are currently no machine learni...

Random forest classifiers aid in the detection of incidental osteoblastic osseous metastases in DEXA studies.

International journal of computer assisted radiology and surgery
PURPOSE: Dual-energy X-ray absorptiometry (DEXA) studies are used for screening patients for low bone mineral density (BMD). Patients with breast and prostate cancer are often treated with hormone-altering drugs that result in low BMD. These patients...

Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis.

Neurosurgery
BACKGROUND: Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision making around end-of-life care.

Predicting 90-Day and 1-Year Mortality in Spinal Metastatic Disease: Development and Internal Validation.

Neurosurgery
BACKGROUND: Increasing prevalence of metastatic disease has been accompanied by increasing rates of surgical intervention. Current tools have poor to fair predictive performance for intermediate (90-d) and long-term (1-yr) mortality.