Clinical trials: Deep Learning
Otalgia, Otitis Media
Validating and AI Software for Assessment of Children With Ear Concerns
The goal of this observational study is to determine if the Glimpse machine learning algorithm can accurately assess ear diseases in children.
- Conditions
- Otalgia, Otitis Media, Otitis Media Effusion
- Trial name
- NCT07243093
- Volunteers
- 658 people
- Sponsor
- Glimpse Diagnostics, Inc.
Fractures, Musculoskeletal
Enhancing Diagnostic Accuracy in Fracture Identification on Musculoskeletal Radiographs Using Deep Learning
This retrospective study aims to evaluate the effectiveness of artificial intelligence (AI) in identifying fractures on musculoskeletal X-rays.
- Trial name
- NCT06644391
- Testing
- Carebot AI Bones
- Where
- Czechia
- Volunteers
- 600 people
- Sponsor
- Carebot s.r.o.
Chronic Coronary Syndrome, Coronary Artery Disease
Feasibility of a Deep Learning-based Algorithm for Non-invasive Assessment of Vulnerable Coronary Plaque
The primary objective of this study is to assess the accuracy in terms of sensitivity, specificity, negative and positive predicted values of the DL-based algorithm with respect to correct identification of the plaque and…
- Conditions
- Chronic Coronary Syndrome, Coronary Artery Disease, Stable Angina, Acute Coronary Syndrome
- Trial name
- FOCUS DL (NCT06186336)
- Testing
- Deep Learning-based Vulnerable Plaque Detection and Assessment Tool
- Where
- Italy
- Volunteers
- 200 people
- Sponsor
- GE Healthcare
Otitis Media Acute
Clinical Development of Deep Learning for the OtoSight
The objective of this study is to clinically develop and evaluate a machine learning approach to improve the performance and data interpretation of the PhotoniCare OtoSight Middle Ear Scope in pediatric patients presenting at the…
- Trial name
- NCT04722770
- Testing
- PhotoniCare OtoSight Middle Ear Scope
- Where
- United States
- Volunteers
- 188 people, healthy volunteers welcome
- Sponsor
- PhotoniCare, Inc.