AIMC Topic: Patient Care Planning

Clear Filters Showing 31 to 40 of 66 articles

Artificial intelligence for treatment planning and soft tissue outcome prediction of orthognathic treatment: A systematic review.

Journal of orthodontics
BACKGROUND: The accuracy of artificial intelligence (AI) in treatment planning and outcome prediction in orthognathic treatment (OGT) has not been systematically reviewed.

Assessment of Natural Language Processing of Electronic Health Records to Measure Goals-of-Care Discussions as a Clinical Trial Outcome.

JAMA network open
IMPORTANCE: Many clinical trial outcomes are documented in free-text electronic health records (EHRs), making manual data collection costly and infeasible at scale. Natural language processing (NLP) is a promising approach for measuring such outcomes...

Personalized workflows in reconstructive dentistry-current possibilities and future opportunities.

Clinical oral investigations
OBJECTIVES: The increasing collection of health data coupled with continuous IT advances have enabled precision medicine with personalized workflows. Traditionally, dentistry has lagged behind general medicine in the integration of new technologies: ...

Computer-assisted surgery in medical and dental applications.

Expert review of medical devices
INTRODUCTION: Computer-assisted surgery (CAS) is a broad surgical methodology that utilizes computer technology to both plan and execute surgical intervention. CAS is widespread in both medicine and dentistry as it allows for minimally invasive and p...

A deep learning approach for dental implant planning in cone-beam computed tomography images.

BMC medical imaging
BACKGROUND: The aim of this study was to evaluate the success of the artificial intelligence (AI) system in implant planning using three-dimensional cone-beam computed tomography (CBCT) images.

Machine Learning and Surgical Outcomes Prediction: A Systematic Review.

The Journal of surgical research
BACKGROUND: Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predi...

Robot-Automated Cartilage Contouring for Complex Ear Reconstruction: A Cadaveric Study.

The Laryngoscope
OBJECTIVES/HYPOTHESIS: Auricular reconstruction requiring manual contouring of costal cartilage is complex and time consuming, which could be facilitated by a robot in a fast and precise manner. This feasibility study evaluates the accuracy and speed...

Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning.

Journal of pain and symptom management
CONTEXT: Goals-of-care discussions are an important quality metric in palliative care. However, goals-of-care discussions are often documented as free text in diverse locations. It is difficult to identify these discussions in the electronic health r...

Artificial intelligence and radiomics in pediatric molecular imaging.

Methods (San Diego, Calif.)
In the past decade, a new approach for quantitative analysis of medical images and prognostic modelling has emerged. Defined as the extraction and analysis of a large number of quantitative parameters from medical images, radiomics is an evolving fie...

Automatic mandibular canal detection using a deep convolutional neural network.

Scientific reports
The practicability of deep learning techniques has been demonstrated by their successful implementation in varied fields, including diagnostic imaging for clinicians. In accordance with the increasing demands in the healthcare industry, techniques fo...