Artificial Intelligence Clinical Trial
Official title:
A Multicentric Validation Study on the Accuracy of Artificial Intelligence Assisted System in Clinical Application of Digestive Endoscopy
Verified date | January 2021 |
Source | Renmin Hospital of Wuhan University |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Observational |
This is an artificial intelligence-based optical artificial intelligence assisted system that can assist endoscopists in improving the quality of endoscopy.
Status | Active, not recruiting |
Enrollment | 10000 |
Est. completion date | December 31, 2025 |
Est. primary completion date | December 31, 2025 |
Accepts healthy volunteers | |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: 1. male or female aged 18 or above; 2. endoscopy and related examinations should be performed to further clarify the characteristics of digestive tract diseases; 3. be able to read, understand and sign the informed consent; 4. the researcher believes that the subject can understand the process of the clinical study, is willing and able to complete all the study procedures and follow-up visits, and cooperate with the study procedures; Exclusion Criteria: 1. have participated in other clinical trials, signed the informed consent and have been in the follow-up period of other clinical trials; 2. drug or alcohol abuse or psychological disorder in the last 5 years; 3. pregnant or nursing women; 4. subjects with previous history of gastrointestinal surgery; 5. the researcher considers that the subject is not suitable for endoscopy and related examination; 6. high-risk diseases or other special conditions that the investigator considers inappropriate for the subject to participate in the clinical trial. |
Country | Name | City | State |
---|---|---|---|
China | Renmin Hospital of Wuhan University | Wuhan | Hubei |
Lead Sponsor | Collaborator |
---|---|
Renmin Hospital of Wuhan University |
China,
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | Accuracy | Calculate the accuracy of AI's judgment on images and videos. Accuracy is : | 2020.1.12-2023.12.31 | |
Primary | Sensitivity | Calculate the sensitivity of AI's judgment on images and videos. Sensitivity is : in the sample that is positive actually, the proportion that judges to be positive (for example, in the person that is really sick, be judged to be the proportion that is sick by the hospital), computation way is the ratio that true positive divides true positive add false negative (be positive actually, but judge is negative). | 2020.1.12-2023.12.31 | |
Primary | Specificity | Calculate the specificity of AI's judgment on images and videos. Specificity is : in the samples that are actually negative, the proportion of those that are judged negative (for example, the proportion of those who are not actually ill, who are judged by the hospital to be not ill) is calculated as the ratio of true negative divided by true negative + false positive (actually negative, but judged positive). | 2020.1.12-2023.12.31 | |
Primary | Positive Predictive Value (PPV) | The percentage of true positive people in positive test results indicates the probability that the positive test results belong to true cases. | 2020.1.12-2023.12.31 | |
Primary | Negative Predictive Value (NPV) | The percentage of true negative to negative test results indicates the probability that the negative test results are non-cases. | 2020.1.12-2023.12.31 | |
Primary | Receiver Operating Characteristic (ROC) Curve | Definition 1:The subject's operating characteristic curve is a coordinate graph composed of false positive rate as the horizontal axis and true positive rate as the vertical axis, and the curve drawn by the subject under specific stimulus conditions due to the different judgment criteria.
Definition 2:ROC curves were created by plotting the proportion of true positive cases (sensitivity) against the proportion of false positive cases (1-specificity), by varying the predictive probability threshold. |
2020.1.12-2023.12.31 | |
Primary | Area Under the Curve (AUC) | Calculate the area under the curve of AI's receiver operating characteristic (ROC) curve. | 2020.1.12-2023.12.31 | |
Secondary | mean Average Precision (mAP) | mAP is setting a threshold for average precision and taking 1 or 0, and then taking the average of the sum of average precision divided by the number of values. | 2020.1.12-2023.12.31 | |
Secondary | Sørensen-Dice coefficient (F1 score) | The Sørensen-Dice coefficient is a statistic used to guage the similarity of two samples. The F1 score is a weighted average of model accuracy and recall. | 2020.1.12-2023.12.31 | |
Secondary | Recall Rate | The percentage of positive examples of predicted pairs in all samples of predicted pairs (including correct predicted positive examples and correct predicted negative examples). | 2020.1.12-2023.12.31 | |
Secondary | Positive Likelihood Ratio | 2020.1.12-2023.12.31 | ||
Secondary | Negative Likelihood Ratio | 2020.1.12-2023.12.31 |
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 |
NCT05006092 -
Surveillance Modified by Artificial Intelligence in Endoscopy (SMARTIE)
|
N/A | |
Completed |
NCT04816981 -
AI-EBUS-Elastography for LN Staging
|
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
|