AIMC Topic: Retrospective Studies

Clear Filters Showing 5831 to 5840 of 9989 articles

Improved breast cancer histological grading using deep learning.

Annals of oncology : official journal of the European Society for Medical Oncology
BACKGROUND: The Nottingham histological grade (NHG) is a well-established prognostic factor for breast cancer that is broadly used in clinical decision making. However, ∼50% of patients are classified as grade 2, an intermediate risk group with low c...

Artificial intelligence in the GPs office: a retrospective study on diagnostic accuracy.

Scandinavian journal of primary health care
OBJECTIVE: Machine learning (ML) is expected to play an increasing role within primary health care (PHC) in coming years. No peer-reviewed studies exist that evaluate the diagnostic accuracy of ML models compared to general practitioners (GPs). The a...

Artificial intelligence-based detection of epimacular membrane from color fundus photographs.

Scientific reports
Epiretinal membrane (ERM) is a common ophthalmological disorder of high prevalence. Its symptoms include metamorphopsia, blurred vision, and decreased visual acuity. Early diagnosis and timely treatment of ERM is crucial to preventing vision loss. Al...

A Cascaded Deep Learning-Based Artificial Intelligence Algorithm for Automated Lesion Detection and Classification on Biparametric Prostate Magnetic Resonance Imaging.

Academic radiology
RATIONALE AND OBJECTIVES: Prostate MRI improves detection of clinically significant prostate cancer; however, its diagnostic performance has wide variation. Artificial intelligence (AI) has the potential to assist radiologists in the detection and cl...

COVID-19 Mortality Prediction From Deep Learning in a Large Multistate Electronic Health Record and Laboratory Information System Data Set: Algorithm Development and Validation.

Journal of medical Internet research
BACKGROUND: COVID-19 is caused by the SARS-CoV-2 virus and has strikingly heterogeneous clinical manifestations, with most individuals contracting mild disease but a substantial minority experiencing fulminant cardiopulmonary symptoms or death. The c...

MTU-COVNet: A hybrid methodology for diagnosing the COVID-19 pneumonia with optimized features from multi-net.

Clinical imaging
PURPOSE: The aim of this study was to establish and evaluate a fully automatic deep learning system for the diagnosis of COVID-19 using thoracic computed tomography (CT).

Robot-assisted laparoscopic urologic surgery in infants weighing ≤10 kg: A weight stratified analysis.

Journal of pediatric urology
INTRODUCTION: Robot-assisted laparoscopic (RAL) urologic surgery is widely used in pediatric patients, though less commonly in infants. There are small series demonstrating safety and efficacy in infants, however, stratification by infant size has ra...

Detection of Baseline Emotion in Brow Lift Patients Using Artificial Intelligence.

Aesthetic plastic surgery
BACKGROUND: The widespread popularity of browlifts and blepharoplasties speaks directly to the importance that patients place on the periorbital region of the face. In literature, most esthetic outcomes are based on instinctive analysis of the esthet...

Artificial intelligence-based image analysis can predict outcome in high-grade serous carcinoma via histology alone.

Scientific reports
High-grade extrauterine serous carcinoma (HGSC) is an aggressive tumor with high rates of recurrence, frequent chemotherapy resistance, and overall 5-year survival of less than 50%. Beyond determining and confirming the diagnosis itself, pathologist ...

Claims-based algorithms for common chronic conditions were efficiently constructed using machine learning methods.

PloS one
Identification of medical conditions using claims data is generally conducted with algorithms based on subject-matter knowledge. However, these claims-based algorithms (CBAs) are highly dependent on the knowledge level and not necessarily optimized f...