UMIN-CTR Clinical Trial
| Unique ID issued by UMIN | UMIN000052695 |
|---|---|
| Receipt number | R000060087 |
| Scientific Title | Development of AI-aided ultrasonic diagnosis system for liver tumor |
| Date of disclosure of the study information | 2023/11/04 |
| Last modified on | 2023/11/04 13:12:26 |
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Basic information
Public title
Construction of database and exploratory trial for performance evaluation of artificial intelligence model to support ultrasonic diagnosis of liver mass
Acronym
Construction of database and exploratory trial of AI to support US diagnosis of liver mass
Scientific Title
Development of AI-aided ultrasonic diagnosis system for liver tumor
Scientific Title:Acronym
Development of AI-aided ultrasonic diagnosis system for liver tumor
Region
| Japan |
Condition
Condition
Liver Mass
Classification by specialty
| Hepato-biliary-pancreatic medicine | Laboratory medicine |
Classification by malignancy
Malignancy
Genomic information
NO
Objectives
Narrative objectives1
Construction of a database of ultrasound (US) B-mode video images for the diagnostic testing of liver mass using AI-aided US, and conducting the exploratory trial for the evaluation of the performance of the artificial intelligence (AI) model that supports human examiners for the detection and diagnosis of liver mass in B-mode US.
Basic objectives2
Efficacy
Basic objectives -Others
Trial characteristics_1
Exploratory
Trial characteristics_2
Developmental phase
Assessment
Primary outcomes
Evaluation of improvement for accuracy, sensitivity, specificity, and Matthews correlation coefficient in the discrimination of malignant tumors in B-mode US examination under the support of AI
Key secondary outcomes
1) Evaluation of improvement for the accuracy of liver tumor differentiation among four types of liver lesions (hepatocellular carcinoma, metastatic liver cancer, hepatic hemangioma, and hepatic cyst) under the support of AI.
2) Evaluation of improvement for disease-specific sensitivity and specificity in the differentiation of liver mass among four types of liver lesions under the support of AI.
3) Evaluation for precision, recall, and F-value in the detection of liver mass under the support of AI.
4) Stratified analysis of skilled (Board certified fellows and registered medical sonographers of the Japan Society of Ultrasonics in Medicines) vs. non-Skilled (non-certified fellows and non-registered medical sonographer) in primary and secondary outcomes
5) Construction of database of US video images for the clinical trial of AI-aided US diagnosis of liver tumor
Base
Study type
Interventional
Study design
Basic design
Single arm
Randomization
Non-randomized
Randomization unit
Blinding
Open -no one is blinded
Control
Self control
Stratification
Dynamic allocation
Institution consideration
Blocking
Concealment
Intervention
No. of arms
1
Purpose of intervention
Diagnosis
Type of intervention
| Device,equipment |
Interventions/Control_1
The examiners first review the pre-recorded B-mode US videos and answer the location and diagnosis of liver mass without AI. Subsequently, they refer to the information presented by AI within the same US video (AI intervention) and provide their answers once more under the support of AI. The study aims to observe changes in the detection and differentiation parameters of the diagnosis of liver mass by the examiner's before and under the AI support.
Interventions/Control_2
Interventions/Control_3
Interventions/Control_4
Interventions/Control_5
Interventions/Control_6
Interventions/Control_7
Interventions/Control_8
Interventions/Control_9
Interventions/Control_10
Eligibility
Age-lower limit
| 20 | years-old | <= |
Age-upper limit
| Not applicable |
Gender
Male and Female
Key inclusion criteria
For the construction of a database of B-mode US video images (1080 video images in total) for the exploratory test:
Patients undergoing thorough examination and follow-up for hepatic lesions (liver mass) include the following:
1) Patients who are scheduled for abdominal ultrasound examination with the purpose of investigating newly detected liver mass.
2) Patients who have already been diagnosed with liver cysts, hepatic hemangiomas, hepatocellular carcinoma, or metastatic liver cancer based on imaging findings such as CT or MRI, or pathological evidence from procedures like biopsies, and are scheduled for regular follow-up using abdominal ultrasound."
For the exploratory test of the AI for supporting US diagnosis of liver mass (20 examinors):
Board-certified fellows (5 persons) and registered medical sonographers (5 persons) qualified by the Japan Society of Ultrasonics in Medicine (JSUM).
Non-board certified fellow (5 persons) and Non-registered medical sonographer (5 persons) by JSUM.
Key exclusion criteria
Participants in the construction of database for B-mode US video images of liver mass include:
1) Cases where a definitive diagnosis of liver mass could not be established.
2) Cases where modification of imaging findings is anticipated due to treatment of liver tumors.
3) Cases where consent from the patient has been withdrawn.
4) Cases where obtaining consent from the individual is difficult.
5) Other cases deemed inappropriate by the attending physician.
Target sample size
20
Research contact person
Name of lead principal investigator
| 1st name | Masatoshi |
| Middle name | |
| Last name | Kudo |
Organization
Kindai University
Division name
Faculty of Medicine, Department of Gastroenterology and Hepatology
Zip code
589-8511
Address
377-2, Ohno-higashi, Osaka-sayama, Osaka, Japan
TEL
072-366-0221
m-kudo@med.kindai.ac.jp
Public contact
Name of contact person
| 1st name | Naoshi |
| Middle name | |
| Last name | Nishida |
Organization
Kindai University
Division name
Faculty of Medicine, Department of Gastroenterology and Hepatology
Zip code
589-8511
Address
377-2, Ohno-higashi, Osaka-sayama, Osaka, Japan
TEL
072-366-0221
Homepage URL
naoshi@med.kindai.ac.jp
Sponsor or person
Institute
The Japan Society of Ultrasonics in Medicine
Institute
Department
Personal name
Funding Source
Organization
Japan Agency for Medical Research and Development
Organization
Division
Category of Funding Organization
Japanese Governmental office
Nationality of Funding Organization
Other related organizations
Co-sponsor
Name of secondary funder(s)
IRB Contact (For public release)
Organization
The Ethics Committee of Kindai University Hospital
Address
377-2, Ohno-higashi, Osaka-sayama, Osaka, Japan
Tel
072-366-0221
zizen@med.kindai.ac.jp
Secondary IDs
Secondary IDs
NO
Study ID_1
Org. issuing International ID_1
Study ID_2
Org. issuing International ID_2
IND to MHLW
Institutions
Institutions
近畿大学医学部（大阪府）、東京大学医学部（東京都）、北海道大学医学部（北海道）、日本大学医学部（東京都）、京都大学医学部（京都府）、兵庫医科大学（兵庫県）、芝浦工業大学SIT総合研究所（東京都）、中京大学工学部（愛知県）、愛媛大学医学部（愛媛県）、愛媛県立中央病院（愛媛県）、姫路赤十字病院（兵庫県）
Other administrative information
Date of disclosure of the study information
| 2023 | Year | 11 | Month | 04 | Day |
Related information
URL releasing protocol
Publication of results
Unpublished
Result
URL related to results and publications
Number of participants that the trial has enrolled
Results
Results date posted
Results Delayed
Results Delay Reason
Date of the first journal publication of results
Baseline Characteristics
Participant flow
Adverse events
Outcome measures
Plan to share IPD
IPD sharing Plan description
Progress
Recruitment status
Enrolling by invitation
Date of protocol fixation
| 2021 | Year | 09 | Month | 06 | Day |
Date of IRB
| 2021 | Year | 09 | Month | 06 | Day |
Anticipated trial start date
| 2021 | Year | 09 | Month | 06 | Day |
Last follow-up date
| 2024 | Year | 09 | Month | 30 | Day |
Date of closure to data entry
Date trial data considered complete
Date analysis concluded
Other
Other related information
Management information
Registered date
| 2023 | Year | 11 | Month | 04 | Day |
Last modified on
| 2023 | Year | 11 | Month | 04 | Day |
Link to view the page
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000060087
