Artificial Intelligence Clinical Trial
Official title:
Acanthamoeba and Artificial Intelligence: Single-center Retrospective Observational Study
Acanthamoeba keratitis, caused by the pathogen Acanthamoeba spp, is recognized worldwide as a severe ocular infection that can pose potential risks to vision. This observational retrospective and single-center study, of exploratory nature, aims to determine the possibility of identifying patterns that may be useful for future rapid diagnosis of Acanthamoeba keratitis from confocal images, leveraging the normality of corneal examination and the high specificity and sensitivity of computational models. The data will be based on patients who have been confirmed positive through laboratory tests with proven effectiveness in detecting the infection. The laboratory tests considered for the division of patients into their respective groups are bacterial examination, PCR examination, and culture examination. Patients were divided into two groups, the first comprising patients positive for Acanthamoeba infection, while the second comprised patients negative for Acanthamoeba but positive for other pathogens. The study will last for 18 months. The cohort under study includes 151 patients from the IRCCS San Raffaele Hospital who underwent the aforementioned examinations, of which 76 cases will be included in the group of patients positive for Acanthamoeba and 75 in the group of controls positive for other pathogens. The confocal images of this cohort will be fed into artificial intelligence software. To evaluate the model, the test set will be used, and the AI model's ability will be assessed using the most commonly used metrics in the field of computer vision such as accuracy, specificity, sensitivity, and f1-score; culminating in a comprehensive evaluation of the model.
n/a
Status | Clinical Trial | Phase | |
---|---|---|---|
Completed |
NCT04589078 -
Polyp REcognition Assisted by a Device Interactive Characterization Tool - The PREDICT Study
|
||
Completed |
NCT03857438 -
Correlation of Audiovisual Features With Clinical Variables and Neurocognitive Functions in Bipolar Disorder, Mania
|
||
Completed |
NCT04735055 -
Artificial Intelligence Prediction for the Severity of Acute Pancreatitis
|
||
Not yet recruiting |
NCT05452993 -
Screening for Diabetic Retinopathy in Pharmacies With Artificial Intelligence Enhanced Retinophotography
|
N/A | |
Not yet recruiting |
NCT04337229 -
Evaluation of Comfort Behavior Levels of Newborns With Artificial Intelligence Techniques
|
N/A | |
Completed |
NCT05687318 -
A Clinical Trial of the Effectiveness and Safety of Software Assisting Diagnose the Intestinal Polyp Digestive Endoscopy by Analysis of Colonoscopy Medical Images From Electronic Digestive Endoscopy Equipment
|
N/A | |
Recruiting |
NCT06051682 -
Optimization of the Diagnosis of Bone Fractures in Patients Treated in the Emergency Department by Using Artificial Intelligence for Reading Radiological Images in Comparison With Traditional Reading by the Emergency Doctor.
|
N/A | |
Not yet recruiting |
NCT06039917 -
Effect of the Automatic Surveillance System on Surveillance Rate of Patients With Gastric Premalignant Lesions
|
N/A | |
Not yet recruiting |
NCT06362629 -
AI App for Management of Atopic Dermatitis
|
N/A | |
Recruiting |
NCT06059378 -
Real-life Implementation of an AI-based Optical Diagnosis
|
N/A | |
Recruiting |
NCT06164002 -
A I in the Prediction of Clinical Performance, Marginal Fit and Fracture Resistance of Vertical Versus Horizontal Margin Designs Fabricated With 2 Ceramic Materials
|
N/A | |
Completed |
NCT05517889 -
Repeatability and Stability of Healthy Skin Features on OCT
|
||
Completed |
NCT04816981 -
AI-EBUS-Elastography for LN Staging
|
N/A | |
Completed |
NCT05006092 -
Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)
|
N/A | |
Recruiting |
NCT04535466 -
Diagnosis Predictive Modle for Dense Density Breast Tissue Based on Radiomics
|
||
Enrolling by invitation |
NCT04719117 -
Retrograde Cholangiopancreatography AI Assisted System Validation on Effectiveness and Safety
|
||
Completed |
NCT04399590 -
Comparing the Number of False Activations Between Two Artificial Intelligence CADe Systems: the NOISE Study
|
||
Recruiting |
NCT04126265 -
Artificial Intelligence-assisted Colonoscopy for Detection of Colon Polyps
|
N/A | |
Recruiting |
NCT06255808 -
Development of Assist Tool for Breast Examination Using the Principle of Ultrasonic Sensor
|
||
Recruiting |
NCT04131530 -
Automatic Evaluation of Inflammation Activity in Ulcerative Colitis Using pCLE With Artificial Intelligence
|