Clinical Trials Logo

Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04547673
Other study ID # ZSYY2020
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date June 20, 2020
Est. completion date December 31, 2026

Study information

Verified date February 2024
Source First Affiliated Hospital, Sun Yat-Sen University
Contact Yihui Wen, Ph.D
Phone +86-13480200660
Email wenyihui@mail.sysu.edu.cn
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

Nasopharyngeal carcinoma (NPC) occurs at a high frequency in southern China, northern Africa, and Alaska, with a reported incidence of 30 cases per 100 000 in Guangdong Province. Endoscopic examination and biopsy are the main methods used for detection and diagnosis of NPC. Early NPC patients achieve favourable prognoses after concurrent radiotherapy and chemotherapy in compassion with advanced NPC patients. Here, the investigators focused on the utility of artificial intelligence to detect early NPC, which based on white light imaging (WLI) and Narrow-band imaging (NBI) nasoendoscopic examination. Having access to this unique population provides an unprecedented opportunity to investigate the effect of intelligent system on diverse nasopharyngeal lesions detection and develop a novel Computer-Aided Diagnosis System.


Recruitment information / eligibility

Status Recruiting
Enrollment 1000
Est. completion date December 31, 2026
Est. primary completion date October 31, 2024
Accepts healthy volunteers
Gender All
Age group 18 Years and older
Eligibility Inclusion Criteria: - Older than 18 years of age Exclusion Criteria: - Refuse to sign the informed consent statement - Patients who have contraindications, e.g. coagulation dysfunction, drug allergy.

Study Design


Intervention

Diagnostic Test:
rigid nasal endoscopes
The endoscope is introduced through the nasal passage to observe, in sequence, the posterior nostril, superior and posterior walls of the nasopharynx, torus tubarius, pharyngeal opening of the auditory tube, and Rosenmu¨ller recess. The imaging light mode is set to conventional WLI and subsequently switch to NBI during the procedure, and representative images are collected and preserve for further analysis. All lesions, detected by either WLI or NBI, are biopsied.

Locations

Country Name City State
China First Affiliated Hospital of Sun Yat-sen University Guangzhou Guangdong
China Sun Yat-Sen University Cancer Center Guangzhou Guangdong
China Kiang Wu Hospital Macao Macao

Sponsors (2)

Lead Sponsor Collaborator
First Affiliated Hospital, Sun Yat-Sen University Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Pathological diagnosis All lesions, detected by either WLI or NBI, are biopsied. 2 experienced pathologists, who were blinded to the endoscopic and intelligent assessment, evaluate the pathological nature of the biopsied lesion independently, and 2 professors with over 10 years of experience in Otolaryngology and pathology department are consulted in cases of disagreement. baseline
Secondary Lesion range All images in which lesions are pathologically diagnosed as NPC are collected. 2 experienced pathologists, who were blinded to the endoscopic and intelligent assessment, delineated the malignant area in the images independently, and 2 professors with over 10 years of experience in Otolaryngology department are consulted in cases of disagreement. baseline
See also
  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
Completed 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