CholedocholithiasisNCT06066372
Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis
An observational study of Choledocholithiasis, run by Asian Institute of Gastroenterology, India at 1 site in India.
Trial summary
Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients. Read more about this study
- Who is it for?
- Ages 18 to 80 years
- Conditions the study is for
- Choledocholithiasis
- How many people
- 1,000 participants
- Where
- 1 siteIndia
- Study dates
- 1 Oct 2023to 30 Oct 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 80 years
- All sexes
- Adult, Older adult
Study population: All patients with suspected choledocholithiasis satisfying either ESGE or ASGE risk stratification criteria of intermediate likelihood of choledocholithiasis will be prospectively enrolled from AIG Hospitals, Hyderabad
Who may be able to join
- Individual 18 years or older with a suspected choledocholithiasis satisfying either ASGE or ESGE risk stratification criteria of intermediate likelihood undergoing EUS or MRCP
Who cannot join
- Patients having co-exiting disease of pancreato biliary system other than gall stones and choledocholithiasis which include chronic pancreatitis, biliary stricture, pancreatobiliary malignancy, portal biliopathy
- Patients having underlying chronic liver diseases
- Pregnancy and breast feeding
- Previous history of cholecystectomy
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
India1 site, 1 recruiting
- Recruiting
Asian Institute of Gastroenterology
Hyderabad, Telangana, 500032
Mohan Ramchandani, MD, +919282859523, docnits13@gmail.com
About this study
Official title: Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis- A Prospective, Open Label, Diagnostic Study
Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.
Read the detailed description
The current guidelines for suspected choledocholithiasis are aimed to reduce the risk of patient receiving diagnostic ERCP and reduce the risk of post ERCP adverse events. In this process there is apparent increase in number of patients in the intermediate likelihood group requiring EUS or MRCP. This can increase the health care utilization and cost of care for intermediate likelihood patients. The field of artificial intelligence in clinical medicine is evolving rapidly. The use of artificial intelligence based machine learning model is not adequately studied for prediction of choledocholithiasis. Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.
- Conditions
- Choledocholithiasis
- Medical subject headings
- Choledocholithiasis
What is being measured
The primary outcome is the main question the study is designed to answer.
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model
Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of choledocholithiasis.
Measured: 1 month
Secondary outcomes (2)
Diagnostic Accuracy Metrics of Endoscopic Ultrasound (EUS) or Magnetic Resonance Cholangiopancreatography (MRCP)
Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUROC) of magnetic resonance cholangiopancreatography (MRCP) for identification of choledocholithiasis.
Measured: 1 Month
Validation Performance of the Machine Learning Prediction Model
Validation performance of the machine learning model for predicting choledocholithiasis, assessed using AUROC, calibration metrics (Brier score), and calibration plots in an independent validation cohort.
Measured: 1 Month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Oct 2023
- Study start1 Oct 2023
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Oct 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for CholedocholithiasisStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case only
- Time perspective
- Prospective
- Enrolment
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Asian Institute of Gastroenterology, India
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Mohan Ramchandani, MD, Study director, Asian Institute of Gastroenterology, India
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06066372
- Sponsor's protocol ID
- AI EUS Choledocholithiasis
- Status last verified
- Dec 2025
- First submitted
- 13 Sep 2023
- Last updated
- 6 Jan 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 6 Jan 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Application of Machine Learning Models to Reduce Need for Diagnostic EUS or MRCP in Patients With Intermediate Likelihood of Choledocholithiasis- A Prospective, Open Label, Diagnostic Study
- ClinicalTrials.gov ID
- NCT06066372
- Acronym / sponsor ID
- AI EUS Choledocholithiasis
- Conditions
- Choledocholithiasis
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Asian Institute of Gastroenterology, IndiaUniversity, hospital or other
- Study start
- 1 Oct 2023 (actual)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Oct 2026 (estimated)
- First posted
- 4 Oct 2023 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.
Detailed description
The current guidelines for suspected choledocholithiasis are aimed to reduce the risk of patient receiving diagnostic ERCP and reduce the risk of post ERCP adverse events. In this process there is apparent increase in number of patients in the intermediate likelihood group requiring EUS or MRCP. This can increase the health care utilization and cost of care for intermediate likelihood patients. The field of artificial intelligence in clinical medicine is evolving rapidly. The use of artificial intelligence based machine learning model is not adequately studied for prediction of choledocholithiasis. Machine learning predictive model can help in stratifying heterogenous intermediate likelihood group to reduce need for EUS or MRCP in selected subgroup of patients.
Study design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- All patients with suspected choledocholithiasis satisfying either ESGE or ASGE risk stratification criteria of intermediate likelihood of choledocholithiasis will be prospectively enrolled from AIG Hospitals, Hyderabad
Key inclusion criteria (1 listed)
- Individual 18 years or older with a suspected choledocholithiasis satisfying either ASGE or ESGE risk stratification criteria of intermediate likelihood undergoing EUS or MRCP
Key exclusion criteria (4 listed)
- Patients having co-exiting disease of pancreato biliary system other than gall stones and choledocholithiasis which include chronic pancreatitis, biliary stricture, pancreatobiliary malignancy, portal biliopathy
- Patients having underlying chronic liver diseases
- Pregnancy and breast feeding
- Previous history of cholecystectomy
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model
Time frame: 1 month
Area under the receiver operating characteristic curve (AUROC) of the machine learning-based prediction model for identifying the presence of choledocholithiasis.
2 secondary outcomes
Diagnostic Accuracy Metrics of Endoscopic Ultrasound (EUS) or Magnetic Resonance Cholangiopancreatography (MRCP)
Time frame: 1 Month
Validation Performance of the Machine Learning Prediction Model
Time frame: 1 Month
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 6 Jan 2026.
Related trials for Choledocholithiasis
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Supraglottic Oxygenation Via Oral Transtracheal Catheter for Reducing the Incidence of Hypoxemia During ERCP Under Sedation
Choledocholithiasis With Cholecystitis With Obstruction, Obstructive Jaundice
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ChOlecystectomy aFter successFul Endoscopic Common Bile Duct Stone Extraction in Elderly
Cholecystectomy, Laparoscopic, Choledocholithiasis
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Hyoscine Butylbromide During Transcystic Papillary Balloon Dilation for Choledocholithiasis
Choledocholithiasis, Common Bile Duct Stones
Not yet recruiting
EUS-Guided Gallbladder Drainage Versus Conservative Management in Patients Unfit for Surgery After a Biliary Event Without Cholecystitis. DRECON Study
Cholecystectomy, Gallbladder
Recruiting
Prophylactic PS Placement to Prevent Pancreatitis After Endoscopic Transpapillary GPC for Cholelithiasis With Concomitant Choledocholithiasis
Cholelithiasis Associated With Common Bile Duct Stones
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Mohan Ramchandani, MD study director
Asian Institute of Gastroenterology, India
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Choledocholithiasis, run by Asian Institute of Gastroenterology, India at 1 site in India.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: looking for participants now, as of the sponsor's last update on 6 Jan 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in India. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Nitin G Jagtap, MD (+919182859523, docnits13@gmail.com), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area Under the Receiver Operating Characteristic Curve (AUROC) of the Machine Learning Model (time frame: 1 month). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 6 Jan 2026.